{"group":{"id":1,"name":"Community","lockable":false,"created_at":"2012-01-18T18:02:15.000Z","updated_at":"2026-06-25T00:23:38.000Z","description":"Problems submitted by members of the MATLAB Central community.","is_default":true,"created_by":161519,"badge_id":null,"featured":false,"trending":false,"solution_count_in_trending_period":0,"trending_last_calculated":"2026-06-25T00:00:00.000Z","image_id":null,"published":true,"community_created":false,"status_id":2,"is_default_group_for_player":false,"deleted_by":null,"deleted_at":null,"restored_by":null,"restored_at":null,"description_opc":null,"description_html":null,"published_at":null},"problems":[{"id":2318,"title":"Combine Cards to make 21","description":"Given between two and six cards, e.g.\r\n\r\n A _ 3 _ 7 _ 6 _ 2\r\n\r\nplace one of the mathematical symbols (+,-,*,/) in the space between each\r\npair of cards to make the equation equal 21.\r\n\r\nThe input will be a string, e.g.\r\n\r\n* 'A3762'\r\n\r\nThe output should be a cell array of mathematical symbols in the order that satisfies the puzzle, e.g. for the string given above, the output would be:\r\n\r\n  {'+-*/'}\r\n\r\nWhich means that A + 3 - 7 * 6 / 2 = 21.\r\n\r\n*  A + 3 = 14\r\n* 14 - 7 = 7\r\n*  7 * 6 = 42\r\n* 42 / 2 = 21\r\n\r\nRules:\r\n\r\n* The Ace can represent either 1 or 11.\r\n* All operations should be performed from left to right\r\n* Cards will be represented by the characters: A,2,3,4,5,6,7,8,9,J,Q,K\r\n","description_html":"\u003cp\u003eGiven between two and six cards, e.g.\u003c/p\u003e\u003cpre\u003e A _ 3 _ 7 _ 6 _ 2\u003c/pre\u003e\u003cp\u003eplace one of the mathematical symbols (+,-,*,/) in the space between each\r\npair of cards to make the equation equal 21.\u003c/p\u003e\u003cp\u003eThe input will be a string, e.g.\u003c/p\u003e\u003cul\u003e\u003cli\u003e'A3762'\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eThe output should be a cell array of mathematical symbols in the order that satisfies the puzzle, e.g. for the string given above, the output would be:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e{'+-*/'}\r\n\u003c/pre\u003e\u003cp\u003eWhich means that A + 3 - 7 * 6 / 2 = 21.\u003c/p\u003e\u003cul\u003e\u003cli\u003eA + 3 = 14\u003c/li\u003e\u003cli\u003e14 - 7 = 7\u003c/li\u003e\u003cli\u003e7 * 6 = 42\u003c/li\u003e\u003cli\u003e42 / 2 = 21\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eRules:\u003c/p\u003e\u003cul\u003e\u003cli\u003eThe Ace can represent either 1 or 11.\u003c/li\u003e\u003cli\u003eAll operations should be performed from left to right\u003c/li\u003e\u003cli\u003eCards will be represented by the characters: A,2,3,4,5,6,7,8,9,J,Q,K\u003c/li\u003e\u003c/ul\u003e","function_template":"function mathSyms = solveBlackjackPuzzle(cards)\r\n  mathSyms  = {'+'};\r\nend","test_suite":"%% Example Case\r\ncards = 'A3762';\r\nmathSymbols = {'+-*/'};\r\nassert(isequal(sort(solveBlackjackPuzzle(cards)), sort(mathSymbols)))\r\n\r\n%%\r\ncards = 'A23QJK';\r\nmathSymbols = {'+-/++'};\r\nassert(isequal(sort(solveBlackjackPuzzle(cards)), sort(mathSymbols)))\r\n\r\n%%\r\ncards = '923';\r\nmathSymbols = {'*+', '-*'};\r\nassert(isequal(sort(solveBlackjackPuzzle(cards)), sort(mathSymbols)))\r\n\r\n%%\r\ncards = 'A23456';\r\nmathSymbols = {'**+++', '*+*-+', '*-+*+', '+++++', '+++-+', '-+/*+', '--+++', '/-*++', '/--+*'};\r\nassert(isequal(sort(solveBlackjackPuzzle(cards)), sort(mathSymbols)))\r\n\r\n%%\r\ncards = 'JQKA';\r\nmathSymbols = {'*/+', '+-+', '-++', '/*+'};\r\nassert(isequal(sort(solveBlackjackPuzzle(cards)), sort(mathSymbols)))\r\n\r\n%%\r\ncards = 'KA';\r\nmathSymbols = {'+'};\r\nassert(isequal(sort(solveBlackjackPuzzle(cards)), sort(mathSymbols)))\r\n\r\n%%\r\ncards = 'AAA';\r\nmathSymbols =  {'+-','-+'};\r\nassert(isequal(sort(solveBlackjackPuzzle(cards)), sort(mathSymbols)))","published":true,"deleted":false,"likes_count":4,"comments_count":4,"created_by":1446,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":43,"test_suite_updated_at":"2014-05-16T13:53:18.000Z","rescore_all_solutions":false,"group_id":15,"created_at":"2014-05-13T15:52:33.000Z","updated_at":"2026-06-17T09:45:09.000Z","published_at":"2014-05-13T15:53:27.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven between two and six cards, e.g.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ A _ 3 _ 7 _ 6 _ 2]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eplace one of the mathematical symbols (+,-,*,/) in the space between each pair of cards to make the equation equal 21.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe input will be a string, e.g.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e'A3762'\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe output should be a cell array of mathematical symbols in the order that satisfies the puzzle, e.g. for the string given above, the output would be:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[{'+-*/'}]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhich means that A + 3 - 7 * 6 / 2 = 21.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA + 3 = 14\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e14 - 7 = 7\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e7 * 6 = 42\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e42 / 2 = 21\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRules:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Ace can represent either 1 or 11.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAll operations should be performed from left to right\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCards will be represented by the characters: A,2,3,4,5,6,7,8,9,J,Q,K\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":61177,"title":"[Master Regular Expression] Unique Email Addresses","description":"Every valid email consists of a local name and a domain name, separated by the '@' sign. Besides lowercase letters, the email may contain one or more '.' or '+'.\r\nFor example, in \"alice@leetcode.com\", \"alice\" is the local name, and \"leetcode.com\" is the domain name.\r\nIf you add periods '.' between some characters in the local name part of an email address, mail sent there will be forwarded to the same address without dots in the local name. Note that this rule does not apply to domain names.\r\nFor example, \"alice.z@leetcode.com\" and \"alicez@leetcode.com\" forward to the same email address.\r\nIf you add a plus '+' in the local name, everything after the first plus sign will be ignored. This allows certain emails to be filtered. Note that this rule does not apply to domain names.\r\nFor example, \"m.y+name@email.com\" will be forwarded to \"my@email.com\".\r\nIt is possible to use both of these rules at the same time.\r\nGiven an array of strings emails where we send one email to each emails[i], return the number of different addresses that actually receive mails.\r\n \r\nExample 1:\r\nInput: emails = [\"test.email+alex@leetcode.com\",\"test.e.mail+bob.cathy@leetcode.com\",\"testemail+david@lee.tcode.com\"] Output: 2 \r\nExplanation: \"testemail@leetcode.com\" and \"testemail@lee.tcode.com\" actually receive mails. \r\n\r\nExample 2:\r\nInput: emails = [\"a@leetcode.com\",\"b@leetcode.com\",\"c@leetcode.com\"] \r\nOutput: 3 \r\n \r\nConstraints:\r\n1 \u003c= emails.length \u003c= 100\r\n1 \u003c= emails[i].length \u003c= 100\r\nemails[i] consist of lowercase English letters, '+', '.' and '@'.\r\nEach emails[i] contains exactly one '@' character.\r\nAll local and domain names are non-empty.\r\nLocal names do not start with a '+' character.\r\nDomain names end with the \".com\" suffix.\r\nDomain names must contain at least one character before \".com\" suffix.","description_html":"\u003cdiv style = \"text-align: start; 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\"\u003e\u003cspan style=\"font-weight: 700; \"\u003evalid email\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003econsists of a\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; 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\"\u003e\u003cspan style=\"font-weight: 700; \"\u003edomain name\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, separated by the\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e'@'\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003esign. Besides lowercase letters, the email may contain one or more\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e'.'\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eor\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e'+'\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 20.4375px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 391px 10.2125px; transform-origin: 391px 10.2188px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example, in\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\"alice@leetcode.com\"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\"alice\"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eis the\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003elocal name\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, and\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\"leetcode.com\"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eis the\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003edomain name\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf you add periods\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e'.'\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ebetween some characters in the\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003elocal name\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003epart of an email address, mail sent there will be forwarded to the same address without dots in the local name. Note that this rule\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003edoes not apply\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eto\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003edomain names\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 20.4375px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 391px 10.2125px; transform-origin: 391px 10.2188px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\"alice.z@leetcode.com\"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eand\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\"alicez@leetcode.com\"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eforward to the same email address.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf you add a plus\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e'+'\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ein the\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003elocal name\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, everything after the first plus sign\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ewill be ignored\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. This allows certain emails to be filtered. Note that this rule\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003edoes not apply\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eto\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003edomain names\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 20.4375px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 391px 10.2125px; transform-origin: 391px 10.2188px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\"m.y+name@email.com\"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ewill be forwarded to\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\"my@email.com\"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIt is possible to use both of these rules at the same time.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven an array of strings\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eemails\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ewhere we send one email to each\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eemails[i]\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, return\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ethe number of different addresses that actually receive mails\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample 1:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e emails = [\"test.email+alex@leetcode.com\",\"test.e.mail+bob.cathy@leetcode.com\",\"testemail+david@lee.tcode.com\"] \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e 2 \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExplanation:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \"testemail@leetcode.com\" and \"testemail@lee.tcode.com\" actually receive mails. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample 2:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e emails = [\"a@leetcode.com\",\"b@leetcode.com\",\"c@leetcode.com\"] \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e 3 \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eConstraints:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 163.5px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 391px 81.75px; transform-origin: 391px 81.75px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e1 \u0026lt;= emails.length \u0026lt;= 100\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e1 \u0026lt;= emails[i].length \u0026lt;= 100\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eemails[i]\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003econsist of lowercase English letters,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e'+'\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e'.'\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eand\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e'@'\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eEach\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eemails[i]\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003econtains exactly one\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e'@'\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003echaracter.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAll local and domain names are non-empty.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eLocal names do not start with a\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e'+'\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003echaracter.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eDomain names end with the\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\".com\"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003esuffix.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eDomain names must contain at least one character before\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\".com\"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003esuffix.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function result = solution(emails)\r\n\r\nend","test_suite":"%%\r\nemails = [\"test.email+alex@leetcode.com\" \"test.e.mail+bob.cathy@leetcode.com\" \"testemail+david@lee.tcode.com\"];\r\ncorrect_answer = 2;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"a@leetcode.com\" \"b@leetcode.com\" \"c@leetcode.com\"];\r\ncorrect_answer = 3;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"wjhlbrie@t..j.mrg.com\" \"hwbrvpohlgr+ed@rn.uafx.ix.fmut.com\" \"v.tejo@x+oprw.com\" \"gerpzqhpheaur@pfngovbsx+a...com\" \"phgn+d@eategig.com\" \"ejoaqlqtcyc@d+rj+mhzraz.com\" \"yaqli@pn.bk.com\"];\r\ncorrect_answer = 7;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"gdksgnj@.zcm.c..com\" \"ia+rirqhpz+.bh@+nir.mna+.narn.com\" \"tdmafwenkjn+@v+eawfkimygd.com\"];\r\ncorrect_answer = 3;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"htbv+v+xhaq+@shs+qknwhfev.com\" \"buayr.eqllgo@vn.nmootopsw.com\" \"xp+tqxmaz.@.sghdcvgxj.com\" \".t.sbvxhm+kbxf@.pv..lssiliniy.com\" \"lmogy@+.o+f.com\" \"nfjjsnl+.+r@maard+jybz..com\" \"pieizze@i.qfzj..com\" \"ocdz.oobbf@k+rbhwh+k..com\" \"shfewmgunty..j@gbzyd+ycvyjsdcg.com\"];\r\ncorrect_answer = 9;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"lutiy.imybcj.r@fcqze.ldj.dwmi.com\" \"jhn.cj@.isopja.com\" \"spogtwhtrq@c+cca+qsaqp.com\"];\r\ncorrect_answer = 3;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"w+.jqzvv+u@ok.kkgc+j..com\" \"ngoikkt@iblhmfx.com\" \"kj.qby@tdipvy.com\" \".lbtymy@gj.rwwa.com\" \"ugigzcfwgbfn@vnjoabkxsrvp.com\" \"exudwkules@xrqfp+qqfi..com\" \"fhwhmh@olgc+p.com\" \"mhpmqixa@u.+xtqic.com\" \".ligb.fvvgu@wkhghvyxeka.com\"];\r\ncorrect_answer = 9;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"a.uyfnngdr@rwxpkk.ujz.com\" \".sjpzarzekgp@ya+kridafidw.com\"];\r\ncorrect_answer = 2;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\".ccrxzyiud@ti+mrviezj.com\" \"qelcesbdgqqmlj@+yxfafndxvccan.com\" \"wtyo+l@olhjyj.com\" \"ah.mphqmt@trvfb+.wyz.com\" \"um+xu.mona+x+p@zteq++m+rk+nil.com\" \"dlvvupcrlxwbrjm@fgohf+uwo+uofum.com\" \"uq.hrr@cuarwk.com\" \"ydbghox+kl+noe@boexhfj.ctecwq.com\" \"dlrshw+k.c.w@msuzwzocmhsz.com\"];\r\ncorrect_answer = 9;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"xmdykvtahgyp@lterrniu+.ur.com\" \"qld+wq.ek@oi.mw.hy+.com\" \"tsg+ut@mz++ox.com\" \".trwkzce@lcxmomue.com\" \"t.b+bfpeh.v@lb+uny.gr+i.com\" \"qsy.lno@mzknpk+u.com\" \"hct+uqr@vdcs.et.com\" \"qmiussebdmpfn@wmzn.c.sjgoc..com\"];\r\ncorrect_answer = 8;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"cqqh.avzp@robtj+htf.com\" \"qjdwzhvn@pjlpq++y.com\" \"bmlr+q@htqwupm.com\" \"wpddmnz.txfuvw@xudvlgxnv+zj++.com\" \"xnqtqbhtfz@m+ssz.qyn+.com\"];\r\ncorrect_answer = 5;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\".zmjgh@+sbvhe.com\" \"nh+onxodyfnp@ps.a+fk+of++.com\"];\r\ncorrect_answer = 2;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"emqg+nociysj@ell+.gcndyeg.com\" \"xudupb..dmsl@hx+qxejgvzou.com\" \"gi+yj+vzd@go.shnerp.com\" \"exegawmmybuhj@oynjvqdwmwetc.com\"];\r\ncorrect_answer = 4;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"ivyvy@.koud.com\" \"zlya+d@v+zlxo.com\" \"hfepaczs@lvaqpc+a.com\" \"mkhiihjwgpsh@.coaqlfhrbgz.com\" \"wjvgijfibmzk@.uqfo+u.+mxr.com\" \"bknkbmznylqm@wxt++n+zbm+y.com\" \"gflhomxq@tdmmhksp.com\" \"hknwtdctuun+sa@abxrmyyn+q+jzp.com\"];\r\ncorrect_answer = 8;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"mzo+sjve@gejsbgth.com\" \"hhwdnr@zzvzee.com\" \"hezr+ov@bb.aloi.com\" \"ngzazovqa.nvb+@x+lraofkkqx.ar.com\" \"blup+@+ymca.com\" \"szkznwyoqh@.ot.kkwdql.com\" \"nfkyat+iu@cxasim+z+.com\" \"lqdhzf+.@vms+n+zo.com\" \"fosq.wjbkw+xikw@g.axsoacnrmvusm.com\" \"a+egj.de@byyk+iox.com\"];\r\ncorrect_answer = 10;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"wosbgfbq+iohjs@w+a.wf+h+khs.t.com\" \"yoryltsqgprg+jg@hy.t.qrwdkodwqd.com\" \"dcknj@e.r+i.com\"];\r\ncorrect_answer = 3;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"nrzlyiewbu@sb.+peednq.com\" \"zj.pre.agf+soq@udki.emvlyzujz.com\" \"h+aeeap.iiocu.@.+z.oglfkpxsbu.com\" \"h+yhm@fvzki.com\"];\r\ncorrect_answer = 4;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"koguyqhghscjiob@xi+.r+hlx+tcxqw.com\" \"fdqgr.tqgjlyyra@mtbvrcqenvkibqg.com\" \"bpaxe.lo.hrydqy@tru+b.irfstinxy.com\"];\r\ncorrect_answer = 3;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"ncsyteko@qfxr+++dm.com\" \".qdh.hz@gvecrk+.com\" \".j+hb@.sol+.com\"];\r\ncorrect_answer = 3;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"vgqkvatdx@qrdsolaz..com\" \"xei+++osalaqm@xxeeqqjgsnsao.com\"];\r\ncorrect_answer = 2;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"onutdg@eqbhng.com\" \"rsptijxxsnadwn@v+hdlhhpliptgn.com\" \"kuofap@lq+qdg.com\" \"btfpitlmblpnr@+cb.uzrxk.drv.com\" \"vxolxcm+rwlq@..nbwpgxjy+..com\"];\r\ncorrect_answer = 5;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"h.npduspt+xt@.gm+trja+gvy.com\" \"eekvae++kjs@pr.txifzbdi.com\" \"n.+syybgkgg@jivviajknfl.com\" \"j++zrdfoc..uj@ishji..po.sed.com\"];\r\ncorrect_answer = 4;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"oslxuvd+j+ppdtj@+++akbe.kjoukhz.com\" \".fgnbscrtpc.j+@.+hgpqbbwourp+.com\" \"y+sh.bf.n@xh+sm+krf.com\"];\r\ncorrect_answer = 3;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"rkfqxlo.y.+@ibeduayh.i+.com\" \"xcuiy.au@vmfwel...com\" \"kkx..lwnuw@eueutajdrs.com\" \"v+oqoufiqmdzo@lgroppjdjbehe.com\" \"d.znfhymi..ybu@zhcsbqguazgwhc.com\" \"r+ulbtcjs@m++o+cfa..com\" \"aajyp+dyuyxnw.@eyjiyhgxjlarpa.com\"];\r\ncorrect_answer = 7;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"dp.+vp+k@kaagwxkb.com\" \"r.lhe.o@kjvljpa.com\" \"vxhjyeu+llbdd@wz+cfo+pgbtmg.com\" \"nrv+p@lwmsz.com\" \"pylevc@wv.zjd.com\"];\r\ncorrect_answer = 5;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"pniur@pb.iu.com\" \"jadg.@uk+bp.com\" \"vrdq.hlhmu.f.@uvlysycaayckm.com\" \"glffs+ezwlhxjl@+nobjdaejgtlwfy.com\" \"rjeay@vx+ji.com\" \"movemhf+rjdu+b@v.w++fvrlpyg+t.com\" \"nrj.kjwhdrd@pqhrhiqnzbf.com\" \"qbkn.@ugotg.com\"];\r\ncorrect_answer = 8;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"d+mhas.tsgqorpw@srjrbeqzfngvhcc.com\" \".wd.nycikhwpio@uluma+y.zptdvxo.com\" \"ewfu+c.cpkgxqi@cpkwcquwmhfjlk.com\" \"rtojs..@yxpal.a.com\" \"ftogpu@wy.zpf.com\" \".qjrog++yxy@te.i+nttbbi.com\" \"skqac@wbpfw.com\" \".yus+rotyed@+yvfccxjtxs.com\" \"znfa.tlseed@+sjidvzaufr.com\" \"eocdtookevafe@.vejunakxmwmu.com\"];\r\ncorrect_answer = 10;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"nnazmvkxsqseu@owlov+y++sh++.com\" \"mcd+.zaoqvjt@mshwokdsnbpa.com\" \"rvgxh@uepgp.com\" \"pyxs.tccxm@hwqtesq+ac.com\" \"nkohjc.rdoom@lnwnahosijdx.com\" \"squpelgagh@fysvplojt..com\" \"svr.thd.akqo@pnvu++nc.g+..com\" \"atx+qtgps@krecrkgco.com\" \"hfbngnwgsjdy@csxah+ujrhbf.com\" \".gblz+gv+@+ccoudafz.com\"];\r\ncorrect_answer = 10;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"lumrfllp@ycgw..+y.com\" \"e+meeqfiv+sgdb@kfatozbkmjqnwu.com\" \"oopl.tztedjk@vwnwy+sbdwj..com\"];\r\ncorrect_answer = 3;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\".hy.flt@oduitys.com\" \"r+kyc@q+pkp.com\" \".htrhfbptj@febgrmels+.com\" \"cgiy+aoodkoyuad@tmczkzjutq.rmp..com\" \"nrcbiwxcjg@atvxzuuya..com\" \"yy.ica+vy@evulrlsul.com\" \"kxdu.xgqr@qkehuqjzl.com\" \"cdiyt++@mpb.jos.com\" \"jvx+opucdmj@zpsgwhusqw..com\"];\r\ncorrect_answer = 9;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"vvtjpnbwceyz@gdcgjaxiiiqo.com\" \"dxb+pfbibxfm@cxk.fqegt+w+s.com\" \"xlxiq.sdffx@iczdyroddo..com\" \"udvnp.@bzldti.com\" \"myvdwpwk+@pxahvnofh.com\" \".i+.t@srhjn.com\" \"q.nt+cmrxj@jfefhhvqyn.com\" \"g.bjes.m@+m+ncvfw.com\" \"dcumhljkpg+@bkinkypk.o+.com\" \".rmsmixyyiyn@mw+vozgvkev..com\"];\r\ncorrect_answer = 10;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"kbzkwnexjd+j+f@i+.ttkuphkfuf..com\" \"vzz+.mgo@nofllmhs.com\" \".iodt@recpp.com\" \"wfjixhqgtx+xk@tbcu.mfcc.mmq.com\" \"olk.ivns.tt++@tvgfits.ighr+.com\" \"eehxp+cj+onn+ku@lxbdwjocmfjlxnu.com\" \"doycs.zkeqwiywn@pe+c.dwo+mgtyzi.com\" \"n+voq@ietp+.com\" \".zkwm.gredn@umj+uhugnza.com\"];\r\ncorrect_answer = 9;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"iyexebhw.iau+hl@wlkxwcizawtsgxk.com\" \"fqdruepre.swxnb@jaqjnp.dvfiveug.com\" \"i+vwklzdm@xpeejkmlp..com\" \"qpla+@.vmbz.com\" \"pg+etmaqi+wae@rrxhpqk.frevh.com\" \"bdfqqoejbcb@qprhwxqzgmd.com\" \".en+qpx.dtkg@aapkhyixcvzm.com\"];\r\ncorrect_answer = 7;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"luffyuctpfb@lcrcudc+oas.com\" \"liedcyn@skgq.ny.com\" \"vdiuwqvnbbt@zomxloeqbu+.com\" \"dei.yrqq+tjb@+nrvays+bj+x.com\" \"wd..bxdxxamf@rtphghmicffc.com\" \"g+emmrm.au+t@rpl.sv+gvfnk.com\" \"uenakj.jtvg@ldduvmakrpg+.com\" \"nt+zofb@rhmc.oq.com\" \"yosrqs@exesju.com\" \"kehogbp.nol@+bxlsqqj.fg.com\"];\r\ncorrect_answer = 10;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"xamepf@m+ie...com\" \"blwpot@evqzxa.com\" \"ibyuogiqh@hwhangzte.com\" \"rdri.la.emkpa@guupavcgqsnq..com\" \"fxtes@cxxir.com\" \"krwy..x++i.exba@jhniby.cxy+f..v.com\" \"abgobuyol@rsexxbwlm.com\" \"jnalpyhai@bccsuj.af.com\"];\r\ncorrect_answer = 8;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":4945898,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":15,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2026-01-31T14:56:56.000Z","updated_at":"2026-06-20T18:01:56.000Z","published_at":"2026-01-31T14:56:56.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEvery\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003evalid email\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003econsists of a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003elocal name\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eand a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edomain name\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, separated by the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e'@'\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003esign. Besides lowercase letters, the email may contain one or more\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e'.'\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eor\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e'+'\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, in\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e\\\"alice@leetcode.com\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e\\\"alice\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eis the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003elocal name\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e\\\"leetcode.com\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eis the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edomain name\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf you add periods\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e'.'\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ebetween some characters in the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003elocal name\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003epart of an email address, mail sent there will be forwarded to the same address without dots in the local name. Note that this rule\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edoes not apply\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eto\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edomain names\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e\\\"alice.z@leetcode.com\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eand\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e\\\"alicez@leetcode.com\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eforward to the same email address.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf you add a plus\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e'+'\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ein the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003elocal name\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, everything after the first plus sign\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ewill be ignored\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. This allows certain emails to be filtered. Note that this rule\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edoes not apply\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eto\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edomain names\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e\\\"m.y+name@email.com\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ewill be forwarded to\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e\\\"my@email.com\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIt is possible to use both of these rules at the same time.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven an array of strings\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eemails\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ewhere we send one email to each\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eemails[i]\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, return\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ethe number of different addresses that actually receive mails\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample 1:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e emails = [\\\"test.email+alex@leetcode.com\\\",\\\"test.e.mail+bob.cathy@leetcode.com\\\",\\\"testemail+david@lee.tcode.com\\\"] \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e 2 \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExplanation:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \\\"testemail@leetcode.com\\\" and \\\"testemail@lee.tcode.com\\\" actually receive mails. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample 2:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e emails = [\\\"a@leetcode.com\\\",\\\"b@leetcode.com\\\",\\\"c@leetcode.com\\\"] \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e 3 \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eConstraints:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e1 \u0026lt;= emails.length \u0026lt;= 100\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e1 \u0026lt;= emails[i].length \u0026lt;= 100\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eemails[i]\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003econsist of lowercase English letters,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e'+'\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e'.'\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eand\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e'@'\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEach\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eemails[i]\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003econtains exactly one\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e'@'\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e 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The person who was unlucky enough to draw the short straw would be executed by the other nine members of his group.\r\n\r\nThe bloodthirsty Roman Centurion Carnage Maximus decided to apply this to his prisoners, with a few gruesome differences.  Rather than kill every tenth prisoner and allow the rest to live, he is going to leave only one prisoner alive and kill all of the others.  Instead of killing every tenth prisoner, he chooses a number (kill_every).  If kill_every=3, he kills every third prisoner.  If kill_every=5, he kills every fifth prisoner.  He always chooses a number between 2 and the number of prisoners he has, and this process will be repeated until there is only one prisoner left.  For example, if there are 10 prisoners, and kill_every=3\r\n\r\nFirst iteration: 1 2 3 4 5 6 7 8 9 10\r\n\r\n1-2-3 4-5-6 7-8-9 10\r\n\r\nPrisoners 3, 6 and 9 will be killed.\r\n\r\nSecond iteration: 1 2 4 5 7 8 10\r\n\r\nBecause Prisoner 10 was counted during the first iteration, the executions\r\nwill proceed as such: 10-1-2 4-5-7 8-10, so prisoners 2 and 7 will be killed\r\n\r\nThird iteration: 1 4 5 8 10\r\n8-10-1 4-5-8 10, so prisoners 1 and 8 executed.\r\n\r\nFourth Iteration:  10-4-5 10\r\nPrisoner 5 is executed.\r\n\r\nFifth iteration:  10-4 10\r\nPrisoner 10 is executed\r\n\r\nSince the sole survivor is prisoner 4, he is released.\r\n\r\nYou are an unlucky prisoner caught by Carnage Maximum.  Prior to lining up the prisoners, he reveals the number of prisoners he has and his value of kill_every for the day.  Your job is to figure out which prisoner you need to be in order to survive.  Write a MATLAB script that takes the values of num_prisoners and kill_every.  The output will be survivor, which is the position of the person who survives.  If you write your script quickly enough, that person will be you.\r\n\r\nGood luck!","description_html":"\u003cp\u003eWhen dealing to the Roman Army, the term decimate meant that the entire unit would be broken up into groups of ten soldiers, and lots would be drawn.  The person who was unlucky enough to draw the short straw would be executed by the other nine members of his group.\u003c/p\u003e\u003cp\u003eThe bloodthirsty Roman Centurion Carnage Maximus decided to apply this to his prisoners, with a few gruesome differences.  Rather than kill every tenth prisoner and allow the rest to live, he is going to leave only one prisoner alive and kill all of the others.  Instead of killing every tenth prisoner, he chooses a number (kill_every).  If kill_every=3, he kills every third prisoner.  If kill_every=5, he kills every fifth prisoner.  He always chooses a number between 2 and the number of prisoners he has, and this process will be repeated until there is only one prisoner left.  For example, if there are 10 prisoners, and kill_every=3\u003c/p\u003e\u003cp\u003eFirst iteration: 1 2 3 4 5 6 7 8 9 10\u003c/p\u003e\u003cp\u003e1-2-3 4-5-6 7-8-9 10\u003c/p\u003e\u003cp\u003ePrisoners 3, 6 and 9 will be killed.\u003c/p\u003e\u003cp\u003eSecond iteration: 1 2 4 5 7 8 10\u003c/p\u003e\u003cp\u003eBecause Prisoner 10 was counted during the first iteration, the executions\r\nwill proceed as such: 10-1-2 4-5-7 8-10, so prisoners 2 and 7 will be killed\u003c/p\u003e\u003cp\u003eThird iteration: 1 4 5 8 10\r\n8-10-1 4-5-8 10, so prisoners 1 and 8 executed.\u003c/p\u003e\u003cp\u003eFourth Iteration:  10-4-5 10\r\nPrisoner 5 is executed.\u003c/p\u003e\u003cp\u003eFifth iteration:  10-4 10\r\nPrisoner 10 is executed\u003c/p\u003e\u003cp\u003eSince the sole survivor is prisoner 4, he is released.\u003c/p\u003e\u003cp\u003eYou are an unlucky prisoner caught by Carnage Maximum.  Prior to lining up the prisoners, he reveals the number of prisoners he has and his value of kill_every for the day.  Your job is to figure out which prisoner you need to be in order to survive.  Write a MATLAB script that takes the values of num_prisoners and kill_every.  The output will be survivor, which is the position of the person who survives.  If you write your script quickly enough, that person will be you.\u003c/p\u003e\u003cp\u003eGood luck!\u003c/p\u003e","function_template":"function survivor=decimate(num_prisoners,kill_every)\r\nsurvivor=4;\r\nend","test_suite":"%%\r\nassert(isequal(decimate(10,3),4))\r\n%%\r\nassert(isequal(decimate(1024,3),676))\r\n%%\r\nassert(isequal(decimate(2012,50),543))\r\n%%\r\nassert(isequal(decimate(30,5),3))\r\n%%\r\nassert(isequal(decimate(10,10),8))\r\n%%\r\nassert(isequal(decimate(2048,2),1))","published":true,"deleted":false,"likes_count":20,"comments_count":12,"created_by":1615,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":322,"test_suite_updated_at":"2012-12-04T21:28:04.000Z","rescore_all_solutions":false,"group_id":13,"created_at":"2012-12-04T19:47:49.000Z","updated_at":"2026-06-11T14:10:13.000Z","published_at":"2012-12-04T19:53:55.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhen dealing to the Roman Army, the term decimate meant that the entire unit would be broken up into groups of ten soldiers, and lots would be drawn. The person who was unlucky enough to draw the short straw would be executed by the other nine members of his group.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe bloodthirsty Roman Centurion Carnage Maximus decided to apply this to his prisoners, with a few gruesome differences. Rather than kill every tenth prisoner and allow the rest to live, he is going to leave only one prisoner alive and kill all of the others. Instead of killing every tenth prisoner, he chooses a number (kill_every). If kill_every=3, he kills every third prisoner. If kill_every=5, he kills every fifth prisoner. He always chooses a number between 2 and the number of prisoners he has, and this process will be repeated until there is only one prisoner left. For example, if there are 10 prisoners, and kill_every=3\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFirst iteration: 1 2 3 4 5 6 7 8 9 10\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e1-2-3 4-5-6 7-8-9 10\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrisoners 3, 6 and 9 will be killed.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSecond iteration: 1 2 4 5 7 8 10\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBecause Prisoner 10 was counted during the first iteration, the executions will proceed as such: 10-1-2 4-5-7 8-10, so prisoners 2 and 7 will be killed\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThird iteration: 1 4 5 8 10 8-10-1 4-5-8 10, so prisoners 1 and 8 executed.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFourth Iteration: 10-4-5 10 Prisoner 5 is executed.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFifth iteration: 10-4 10 Prisoner 10 is executed\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSince the sole survivor is prisoner 4, he is released.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou are an unlucky prisoner caught by Carnage Maximum. Prior to lining up the prisoners, he reveals the number of prisoners he has and his value of kill_every for the day. Your job is to figure out which prisoner you need to be in order to survive. Write a MATLAB script that takes the values of num_prisoners and kill_every. The output will be survivor, which is the position of the person who survives. If you write your script quickly enough, that person will be you.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGood luck!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44374,"title":"Tautology","description":"Check if the given expression is always true. For example, the sentence\r\n\r\n  '~(A \u0026 B) == (~A | ~B)'\r\n\r\nis always true.\r\n\r\nCharacters in the input sequences may include *~ \u0026 | == ( )*, whitespace, 0 for false, 1 for true and letters for variables.","description_html":"\u003cp\u003eCheck if the given expression is always true. For example, the sentence\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e'~(A \u0026 B) == (~A | ~B)'\r\n\u003c/pre\u003e\u003cp\u003eis always true.\u003c/p\u003e\u003cp\u003eCharacters in the input sequences may include \u003cb\u003e~ \u0026 | == ( )\u003c/b\u003e, whitespace, 0 for false, 1 for true and letters for variables.\u003c/p\u003e","function_template":"function y = tautology(x)\r\n  y = true;\r\nend","test_suite":"%%\r\nx = '0';\r\ny_correct = false;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = '1';\r\ny_correct = true;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = 'A';\r\ny_correct = false;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = '~A';\r\ny_correct = false;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = 'A|1';\r\ny_correct = true;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = '1\u0026A';\r\ny_correct = false;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = 'A|B';\r\ny_correct = false;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = 'A\u0026B';\r\ny_correct = false;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = 'A|A';\r\ny_correct = false;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = 'A|~A';\r\ny_correct = true;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = '0==0';\r\ny_correct = true;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = '~0';\r\ny_correct = true;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = '~(A \u0026 B) == (~A | ~B)';\r\ny_correct = true;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = '~(Z \u0026 Y) == (~Y | ~Z)';\r\ny_correct = true;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = 'A|B|C|D|E|F|G|H|I|J|K|L|M|N|O|P|Q|R|S|T|U|X|V|W|Y|Z';\r\ny_correct = false;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = 'A|B|C|D|E|F|G|H|I|J|K|L|M|~A|O|P|Q|R|S|T|U|X|V|W|Y|Z';\r\ny_correct = true;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nassert(isequal(tautology('(A|B)|C'),false));\r\n%%\r\nassert(isequal(tautology('(A|B)|(C == C)'),true));\r\n%%\r\nassert(isequal(tautology('(A == B)|(B == C)|(C == A)'),true));\r\n%%\r\nassert(isequal(tautology('~(~(~(~(~(~(0))))))'),false)); \r\n%%\r\nassert(isequal(tautology('~(~(~(~(~(~(~0))))))'),true));\r\n% provided by Alfonso:\r\nassert(isequal(tautology('((0\u00261)|~B)\u0026~B'),false)); \r\n%%\r\nassert(isequal(tautology('((0\u0026~B)\u0026~B)'),false)); \r\n%%\r\nassert(isequal(tautology('((0|A)\u0026~A)'),false)); \r\n%%\r\nassert(isequal(tautology('((0|A)|1)'),true)); \r\n%%\r\nassert(isequal(tautology('((0|~B)|1)'),true)); \r\n%%\r\nassert(isequal(tautology('((1\u00260)|B)'),false)); \r\n%%\r\nassert(isequal(tautology('((1\u00261)\u0026A)'),false)); \r\n%%\r\nassert(isequal(tautology('((1|0)|A)'),true)); \r\n%%\r\nassert(isequal(tautology('((1|A)|0)'),true)); \r\n%%\r\nassert(isequal(tautology('((1|~A)\u0026B)'),false)); \r\n%%\r\nassert(isequal(tautology('((A\u00261)|~A)|A'),true)); \r\n%%\r\nassert(isequal(tautology('((A\u0026~A)\u0026~B)|~A'),false)); \r\n%%\r\nassert(isequal(tautology('((A\u0026~B)\u00261)|B'),false)); \r\n%%\r\nassert(isequal(tautology('((A|0)\u00261)\u0026~B'),false)); \r\n%%\r\nassert(isequal(tautology('((A|A)\u0026A)|~A'),true)); \r\n%%\r\nassert(isequal(tautology('((B|0)\u0026B)'),false)); \r\n%%\r\nassert(isequal(tautology('((B|1)\u0026B)\u0026A'),false)); \r\n%%\r\nassert(isequal(tautology('((B|A)|~A)'),true)); \r\n%%\r\nassert(isequal(tautology('((~A\u0026~A)\u00260)\u0026B'),false)); \r\n%%\r\nassert(isequal(tautology('((~A\u0026~A)|0)'),false)); \r\n%%\r\nassert(isequal(tautology('((~A\u0026~A)|~A)|1'),true)); \r\n%%\r\nassert(isequal(tautology('((~A|A)|~B)\u00261'),true)); \r\n%%\r\nassert(isequal(tautology('((~A|B)|A)'),true)); \r\n%%\r\nassert(isequal(tautology('((~A|~A)|1)'),true)); \r\n%%\r\nassert(isequal(tautology('((~A|~B)\u00260)'),false)); \r\n%%\r\nassert(isequal(tautology('((~B\u00260)\u0026A)'),false)); \r\n%%\r\nassert(isequal(tautology('(0\u00261)|1\u00261'),true)); \r\n%%\r\nassert(isequal(tautology('(0|~A\u0026B)'),false)); \r\n%%\r\nassert(isequal(tautology('(1|A\u00260)'),true)); \r\n%%\r\nassert(isequal(tautology('(A\u0026A\u0026~B)'),false)); \r\n%%\r\nassert(isequal(tautology('(A\u0026~A|1)'),true)); \r\n%%\r\nassert(isequal(tautology('(A|1)|B'),true)); \r\n%%\r\nassert(isequal(tautology('(A|A)|A|1'),true)); \r\n%%\r\nassert(isequal(tautology('(B\u00261)|~B'),true)); \r\n%%\r\nassert(isequal(tautology('(B\u0026~B)\u0026~B\u00260'),false)); \r\n%%\r\nassert(isequal(tautology('(B|~B)|B'),true)); \r\n%%\r\nassert(isequal(tautology('(~A\u0026B\u00260)'),false)); \r\n%%\r\nassert(isequal(tautology('(~A|0)|~B\u0026~A'),false)); \r\n%%\r\nassert(isequal(tautology('(~A|1)|1'),true)); \r\n%%\r\nassert(isequal(tautology('(~A|B\u0026B)'),false)); \r\n%%\r\nassert(isequal(tautology('(~A|B)|~B'),true)); \r\n%%\r\nassert(isequal(tautology('(~A|~A)|0'),false)); \r\n%%\r\nassert(isequal(tautology('(~B\u00260)\u00261|1'),true)); \r\n%%\r\nassert(isequal(tautology('1\u0026B|~B|0'),true)); \r\n%%\r\nassert(isequal(tautology('B\u00261\u0026A\u00261'),false)); \r\n%%\r\nassert(isequal(tautology('~A\u00260\u00261|1'),true)); \r\n%%\r\nassert(isequal(tautology('~B\u00260\u0026~A|B'),false)); \r\n%%\r\nassert(isequal(tautology('~B|1|1|~B'),true)); \r\n%%\r\nassert(isequal(tautology('~B|~B\u00261|1'),true));\r\n%%\r\nassert(isequal(tautology('A==~A'),false));\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":3,"comments_count":30,"created_by":14358,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":45,"test_suite_updated_at":"2017-10-31T07:45:16.000Z","rescore_all_solutions":true,"group_id":35,"created_at":"2017-10-10T23:20:08.000Z","updated_at":"2026-05-25T02:36:10.000Z","published_at":"2017-10-16T01:51:01.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCheck if the given expression is always true. For example, the sentence\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA['~(A \u0026 B) == (~A | ~B)']]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eis always true.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCharacters in the input sequences may include\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e~ \u0026amp; | == ( )\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, whitespace, 0 for false, 1 for true and letters for variables.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":60486,"title":"Compute Farey sequences","description":"Problem statement\r\nThe Farey sequence of order  consists of fractions between 0 and 1 expressed in reduced form in increasing order and with no denominator greater than . For example, the Farey sequence of order 3 is {0/1, 1/3, 1/2, 2/3, 1/1}. \r\nWrite a function to compute the Farey sequence of order . Put the numerators in the first row of a two-row matrix and denominators in the second row. \r\nFurther comments\r\nFarey sequences are connected to Stern-Brocot trees, but unlike some other problems of mine (e.g., CP 59791 and 60311), this one was not inspired by a problem of minnolina’s. Instead it arose out of reading The Riemann Hypothesis: The Greatest Unsolved Problem in Mathematics by Karl Sabbagh. Geologist John Farey noted, in a half-page 1816 paper in Philosophical Magazine, a relationship between a fraction in the sequence and the fractions to its left and right. In discussing the connection between the Riemann hypothesis and Farey sequences, Sabbagh writes that, mathematician G.H. Hardy “said, somewhat cruelly:\r\nJust once in his life Mr. Farey rose above mediocrity, and made an original observation. He did not understand very well what he was doing, and he was too weak a mathematician to prove the quite simple theorem he had discovered. It is evident also that he did not consider his discovery…at all important…He had obviously no idea that this casual letter was the one event of real importance in his life. We may be tempted to think that Farey was very lucky; but a man who has made an observation that has escaped Fermat and Euler deserves any luck that comes his way.”  \r\nStunned by this mean-spirited statement—which Hardy read in a 1928 lecture in New York City, I decided to write a problem on the sequences named for Farey.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 474px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 237px; transform-origin: 407px 237px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 63.0083px 8px; transform-origin: 63.0083px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eProblem statement\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 92.1833px 8px; transform-origin: 92.1833px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe Farey sequence of order \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 276.958px 8px; transform-origin: 276.958px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e consists of fractions between 0 and 1 expressed in reduced form in increasing order and with no denominator greater than \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 222.075px 8px; transform-origin: 222.075px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. For example, the Farey sequence of order 3 is {0/1, 1/3, 1/2, 2/3, 1/1}. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 176.842px 8px; transform-origin: 176.842px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to compute the Farey sequence of order \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 185.508px 8px; transform-origin: 185.508px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. Put the numerators in the first row of a two-row matrix and denominators in the second row. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 61.45px 8px; transform-origin: 61.45px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eFurther comments\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 126px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 63px; text-align: left; transform-origin: 384px 63px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 109.692px 8px; transform-origin: 109.692px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFarey sequences are connected to \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/60266\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"text-decoration-line: underline; \"\u003eStern-Brocot\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 175.408px 8px; transform-origin: 175.408px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e trees, but unlike some other problems of mine (e.g., CP \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/59791\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"text-decoration-line: underline; \"\u003e59791\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 15.5583px 8px; transform-origin: 15.5583px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/60311\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"text-decoration-line: underline; \"\u003e60311\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 5.65833px 8px; transform-origin: 5.65833px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e), this one was not inspired by a problem of minnolina’s. Instead it arose out of reading \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.525px 8px; transform-origin: 80.525px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eThe Riemann Hypothesis:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.94167px 8px; transform-origin: 1.94167px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14px 8px; transform-origin: 14px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eThe Greatest Unsolved Problem in Mathematics \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 234.567px 8px; transform-origin: 234.567px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eby Karl Sabbagh. Geologist John Farey noted, in a half-page 1816 paper in \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 73.5417px 8px; transform-origin: 73.5417px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ePhilosophical Magazine\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 278.492px 8px; transform-origin: 278.492px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, a relationship between a fraction in the sequence and the fractions to its left and right. In discussing the connection between the Riemann hypothesis and Farey sequences, Sabbagh writes that, mathematician G.H. Hardy “said, somewhat cruelly:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 126px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 63px; text-align: left; transform-origin: 384px 63px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 384px 8px; transform-origin: 384px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eJust once in his life Mr. Farey rose above mediocrity, and made an original observation. He did not understand very well what he was doing, and he was too weak a mathematician to prove the quite simple theorem he had discovered. It is evident also that he did not consider his discovery…at all important…He had obviously no idea that this casual letter was the one event of real importance in his life. We may be tempted to think that Farey was very lucky; but a man who has made an observation that has escaped Fermat and Euler deserves any luck that comes his way.” \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.85px 8px; transform-origin: 3.85px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; font-style: italic; \"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 384px 8px; transform-origin: 384px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eStunned by this mean-spirited statement—which Hardy read in a 1928 lecture in New York City, I decided to write a problem on the sequences named for Farey.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = Farey(n)\r\n  y = [0 1:n 1; 1 n:-1:1 1]; % Numerators in the first row, denominators in the second\r\nend","test_suite":"%%\r\nn = 1;\r\ny = Farey(n);\r\ny_correct = [0 1; 1 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 3;\r\ny = Farey(n);\r\ny_correct = [0 1 1 2 1; 1 3 2 3 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 5;\r\ny = Farey(n);\r\ny_correct = [0 1 1 1 2 1 3 2 3 4 1; 1 5 4 3 5 2 5 3 4 5 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 8;\r\ny = Farey(n);\r\ny_correct = [0 1 1 1 1 1 2 1 3 2 3 1 4 3 5 2 5 3 4 5 6 7 1; 1 8 7 6 5 4 7 3 8 5 7 2 7 5 8 3 7 4 5 6 7 8 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%% \r\nn = 18;\r\ny = Farey(n);\r\ny_correct = [0 1 1 1 1 1 1 1 1 1 1 2 1 2 1 2 1 3 2 3 1 3 2 3 4 1 4 3 5 2 5 3 4 5 1 6 5 4 3 5 7 2 7 5 3 7 4 5 6 7 8 1 9 8 7 6 5 9 4 7 10 3 11 8 5 7 9 11 2 11 9 7 12 5 13 8 11 3 13 10 7 11 4 13 9 14 5 11 6 13 7 15 8 9 10 11 12 13 14 15 16 17 1; ...\r\n             1 18 17 16 15 14 13 12 11 10 9 17 8 15 7 13 6 17 11 16 5 14 9 13 17 4 15 11 18 7 17 10 13 16 3 17 14 11 8 13 18 5 17 12 7 16 9 11 13 15 17 2 17 15 13 11 9 16 7 12 17 5 18 13 8 11 14 17 3 16 13 10 17 7 18 11 15 4 17 13 9 14 5 16 11 17 6 13 7 15 8 17 9 10 11 12 13 14 15 16 17 18 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 81;\r\ny = Farey(n);\r\nf = y(1,:)./y(2,:);\r\nindx73 = find(y==73)';\r\nindx73_correct = [20 102 162 220 276 332 386 446 502 552 608 670 722 776 828 886 940 1006 1052 1104 1158 1214 1274 1340 1380 1440 1494 1552 1614 1662 1720 1772 1828 1886 1942 2012 2032 2102 2158 2216 2272 2324 2382 2430 2492 2550 2604 2664 2704 2770 2830 2886 2940 2992 3038 3104 3158 3216 3268 3322 3374 3436 3492 3542 3598 3641 3658 3693 3712 3735 3768 3787 3824 3835 3882 3889 3942 3945 4024 4025];\r\nwidth_correct = 2021;\r\nsum_correct = [54882; 109764];\r\nnprime_correct = 1648;\r\nassert(all(diff(f)\u003e0))\r\nassert(isequal(indx73,indx73_correct))\r\nassert(isequal(width(y),width_correct))\r\nassert(isequal(sum(y,2),sum_correct))\r\nassert(isequal(length(find(isprime(y))),nprime_correct))\r\n\r\n%%\r\nn = 188;\r\ny = Farey(n);\r\nf = y(1,:)./y(2,:);\r\nindx173 = find(y==173)';\r\nindx173_correct = [34 222 358 490 618 742 870 996 1124 1242 1370 1494 1624 1752 1876 1996 2124 2250 2378 2496 2618 2746 2876 3000 3120 3244 3374 3498 3606 3746 3872 3998 4118 4246 4364 4498 4624 4742 4868 4986 5116 5244 5388 5486 5614 5742 5868 5996 6118 6240 6366 6484 6618 6746 6870 6990 7124 7210 7360 7488 7616 7738 7852 7988 8100 8236 8358 8486 8630 8736 8860 8994 9114 9250 9362 9488 9602 9738 9862 9984 10112 10232 10366 10492 10618 10782 10814 10978 11104 11230 11364 11484 11612 11734 11858 11994 12108 12234 12346 12482 12602 12736 12860 12966 13110 13238 13360 13496 13608 13744 13858 13980 14108 14236 14386 14472 14606 14726 14850 14978 15112 15230 15356 15478 15600 15728 15854 15982 16110 16208 16352 16480 16610 16728 16854 16972 17098 17232 17350 17478 17598 17724 17850 17990 18098 18222 18352 18476 18596 18720 18850 18978 19100 19218 19346 19472 19600 19720 19844 19867 19972 19975 20089 20102 20195 20226 20309 20354 20417 20472 20527 20600 20643 20726 20761 20854 20877 20978 20997 21106 21117 21238 21245 21374 21377 21562 21563];\r\nwidth_correct = 10797;\r\nsum_correct = [678175; 1356350];\r\nnprime_correct = 7550;\r\nassert(all(diff(f)\u003e0))\r\nassert(isequal(indx173,indx173_correct))\r\nassert(isequal(width(y),width_correct))\r\nassert(isequal(sum(y,2),sum_correct))\r\nassert(isequal(length(find(isprime(y))),nprime_correct))\r\n\r\n%%\r\nn = 811;\r\ny = Farey(n);\r\nf = y(1,:)./y(2,:);\r\nindx773 = find(y==773)';\r\nindx773_correct = [80 892 1458 2014 2538 3078 3588 4124 4644 5160 5682 6200 6724 7254 7768 8266 8792 9298 9844 10358 10880 11388 11918 12434 12950 13470 13986 14504 15018 15540 16062 16592 17106 17628 18142 18656 19176 19700 20204 20720 21238 21772 22264 22802 23320 23846 24364 24888 25400 25916 26438 26956 27476 27992 28514 29028 29542 30046 30584 31098 31632 32136 32656 33174 33688 34202 34724 35240 35766 36286 36794 37318 37838 38360 38874 39396 39920 40428 40942 41452 41986 42500 43026 43554 44062 44534 45098 45618 46134 46652 47142 47692 48196 48722 49236 49754 50258 50778 51308 51826 52342 52860 53410 53900 54420 54940 55462 55980 56508 57032 57528 58054 58570 59092 59606 60104 60646 61164 61646 62196 62708 63256 63744 64270 64786 65310 65828 66354 66792 67376 67904 68406 68932 69454 69976 70492 71008 71544 72050 72566 73078 73586 74122 74632 75128 75678 76196 76718 77232 77738 78268 78784 79300 79836 80308 80848 81378 81892 82406 82924 83466 83968 84484 84992 85522 86040 86566 87100 87602 88118 88638 89138 89670 90188 90714 91228 91748 92266 92786 93306 93820 94330 94858 95394 95890 96412 96936 97456 97964 98486 99004 99532 100146 100532 101062 101586 102110 102626 103140 103664 104174 104702 105220 105732 106256 106786 107294 107812 108326 108848 109350 109878 110396 110916 111436 111946 112462 112996 113512 114030 114480 115056 115570 116094 116616 117132 117648 118168 118698 119202 119726 120196 120754 121278 121796 122314 122836 123342 123870 124384 124904 125422 125940 126470 126978 127480 128014 128536 129054 129576 130090 130616 131120 131646 132172 132694 133246 133570 134214 134744 135270 135788 136300 136820 137350 137862 138378 138894 139416 139934 140454 140968 141488 142014 142536 143080 143566 144092 144600 145124 145678 146154 146674 147192 147712 148236 148752 149262 149792 150250 150826 151348 151858 152382 152898 153420 153936 154450 154966 155490 156006 156524 157044 157564 158082 158602 159130 159644 160248 160668 161194 161712 162226 162762 163262 163792 164304 164826 165344 165860 166374 166930 167414 167934 168456 168974 169508 170004 170526 171040 171578 172068 172586 173118 173620 174160 174668 175194 175708 176226 176748 177254 177780 178288 178812 179332 179854 180368 180894 181406 181932 182438 182958 183476 183996 184512 185026 185552 186066 186588 187100 187622 188142 188660 189188 189708 190222 190742 191254 191768 192306 192816 193330 193860 194366 194886 195402 195924 196438 196958 197482 197998 198528 199054 199592 200282 200362 201052 201590 202116 202646 203162 203686 204206 204720 205242 205758 206278 206784 207314 207828 208338 208876 209390 209902 210422 210936 211456 211984 212502 213022 213544 214056 214578 215092 215618 216132 216648 217168 217686 218206 218712 219238 219750 220276 220790 221312 221832 222356 222864 223390 223896 224418 224936 225450 225976 226484 227024 227526 228058 228576 229066 229604 230118 230640 231136 231670 232188 232710 233230 233714 234270 234784 235300 235818 236340 236852 237382 237882 238418 238932 239450 239976 240396 241000 241514 242042 242562 243080 243600 244120 244638 245154 245678 246194 246708 247224 247746 248262 248786 249296 249818 250394 250852 251382 251892 252408 252932 253452 253970 254490 254966 255520 256044 256552 257078 257564 258108 258630 259156 259676 260190 260710 261228 261750 262266 262782 263294 263824 264344 264856 265374 265900 266430 267074 267398 267950 268472 268998 269524 270028 270554 271068 271590 272108 272630 273164 273666 274174 274704 275222 275740 276260 276774 277302 277808 278330 278848 279366 279890 280448 280918 281442 281946 282476 282996 283512 284028 284550 285074 285588 286164 286614 287132 287648 288182 288698 289208 289728 290248 290766 291294 291796 292318 292832 293350 293858 294388 294912 295424 295942 296470 296980 297504 298018 298534 299058 299582 300112 300498 301112 301640 302158 302680 303188 303708 304232 304754 305250 305786 306314 306824 307338 307858 308378 308896 309416 309930 310456 310974 311506 312006 312526 313042 313544 314078 314604 315122 315652 316160 316676 317178 317720 318238 318752 319266 319796 320336 320808 321344 321860 322376 322906 323412 323926 324448 324966 325516 326012 326522 327058 327566 328078 328594 329100 329636 330152 330668 331190 331712 332238 332740 333268 333852 334290 334816 335334 335858 336374 336900 337388 337936 338448 338998 339480 339998 340540 341038 341552 342074 342590 343116 343612 344136 344664 345182 345704 346224 346744 347234 347784 348302 348818 349336 349866 350386 350890 351408 351922 352448 352952 353502 353992 354510 355026 355546 356110 356582 357090 357618 358144 358658 359192 359702 360216 360724 361248 361770 362284 362806 363326 363850 364358 364878 365404 365920 366442 366956 367470 367988 368508 369012 369546 370060 370598 371102 371616 372130 372652 373168 373688 374206 374728 375244 375756 376280 376798 377324 377842 378380 378872 379406 379924 380440 380944 381468 381865 381988 382349 382502 382813 383016 383281 383538 383759 384052 384239 384582 384715 385104 385211 385626 385683 386140 386171 386645 386658 387125 387174 387609 387694 388101 388210 388579 388726 389073 389256 389561 389764 390045 390286 390541 390800 391039 391346 391549 391852 392043 392378 392537 392876 393019 393390 393517 393920 394035 394444 394535 394962 395045 395484 395547 396000 396055 396520 396563 397056 397091 397566 397589 398106 398125 398630 398641 399186 399193 399752 399755 400564 400565];\r\nwidth_correct = 200321;\r\nsum_correct = [54206832; 108413664];\r\nnprime_correct = 112653;\r\nassert(all(diff(f)\u003e0))\r\nassert(isequal(indx773,indx773_correct))\r\nassert(isequal(width(y),width_correct))\r\nassert(isequal(sum(y,2),sum_correct))\r\nassert(isequal(length(find(isprime(y))),nprime_correct))\r\n\r\n%%\r\nn = randi(134);\r\ny = Farey(n);\r\nm = width(y);\r\nb = y(2,:);\r\nassert(abs(sum(b(1:end-1)./b(2:end))-(3*m-4)/2)\u003c1e-5)\r\nassert(abs(sum(1./(b(1:end-1).*b(2:end)))-1)\u003c1e-5)\r\n\r\n%%\r\nfiletext = fileread('Farey.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'assert'); \r\nassert(~illegal)","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":46909,"edited_by":46909,"edited_at":"2024-06-10T05:07:53.000Z","deleted_by":null,"deleted_at":null,"solvers_count":10,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2024-06-10T05:03:32.000Z","updated_at":"2026-06-05T22:48:45.000Z","published_at":"2024-06-10T05:03:55.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eProblem statement\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Farey sequence of order \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"n\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e consists of fractions between 0 and 1 expressed in reduced form in increasing order and with no denominator greater than \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"n\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. For example, the Farey sequence of order 3 is {0/1, 1/3, 1/2, 2/3, 1/1}. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to compute the Farey sequence of order \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"n\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. Put the numerators in the first row of a two-row matrix and denominators in the second row. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eFurther comments\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFarey sequences are connected to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/60266\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eStern-Brocot\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e trees, but unlike some other problems of mine (e.g., CP \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/59791\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e59791\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/60311\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e60311\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e), this one was not inspired by a problem of minnolina’s. Instead it arose out of reading \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThe Riemann Hypothesis:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThe Greatest Unsolved Problem in Mathematics \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eby Karl Sabbagh. Geologist John Farey noted, in a half-page 1816 paper in \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ePhilosophical Magazine\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, a relationship between a fraction in the sequence and the fractions to its left and right. In discussing the connection between the Riemann hypothesis and Farey sequences, Sabbagh writes that, mathematician G.H. Hardy “said, somewhat cruelly:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eJust once in his life Mr. Farey rose above mediocrity, and made an original observation. He did not understand very well what he was doing, and he was too weak a mathematician to prove the quite simple theorem he had discovered. It is evident also that he did not consider his discovery…at all important…He had obviously no idea that this casual letter was the one event of real importance in his life. We may be tempted to think that Farey was very lucky; but a man who has made an observation that has escaped Fermat and Euler deserves any luck that comes his way.” \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eStunned by this mean-spirited statement—which Hardy read in a 1928 lecture in New York City, I decided to write a problem on the sequences named for Farey.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":364,"title":"Matrix spiral","description":"Make a spiral in a (n*n) matrix. The spiral has to start in the top left, and has to rotate clockwise to the center. The spiral has to have a padding of zeros between itself.\r\n\r\nThe (n*n) matrix is filled with zeros except for the spiral, that has to be made of 11 (elevens, for visual reference).\r\nThe final matrix has to have the same or more zeros than elevens.\r\n\r\nExample:\r\nn=8\r\n\r\nA =\r\n\r\n    11    11    11    11    11    11    11    11\r\n     0     0     0     0     0     0     0    11\r\n     0     0    11    11    11    11     0    11\r\n     0     0    11     0     0    11     0    11\r\n     0     0    11     0    11    11     0    11\r\n     0     0    11     0     0     0     0    11\r\n     0     0    11    11    11    11    11    11\r\n     0     0     0     0     0     0     0     0","description_html":"\u003cp\u003eMake a spiral in a (n*n) matrix. The spiral has to start in the top left, and has to rotate clockwise to the center. The spiral has to have a padding of zeros between itself.\u003c/p\u003e\u003cp\u003eThe (n*n) matrix is filled with zeros except for the spiral, that has to be made of 11 (elevens, for visual reference).\r\nThe final matrix has to have the same or more zeros than elevens.\u003c/p\u003e\u003cp\u003eExample:\r\nn=8\u003c/p\u003e\u003cp\u003eA =\u003c/p\u003e\u003cpre\u003e    11    11    11    11    11    11    11    11\r\n     0     0     0     0     0     0     0    11\r\n     0     0    11    11    11    11     0    11\r\n     0     0    11     0     0    11     0    11\r\n     0     0    11     0    11    11     0    11\r\n     0     0    11     0     0     0     0    11\r\n     0     0    11    11    11    11    11    11\r\n     0     0     0     0     0     0     0     0\u003c/pre\u003e","function_template":"function A = Matrix_Spiral(n)\r\n  A = n;\r\nend","test_suite":"%%\r\nn = 1;\r\ny_correct =[0];\r\nassert(isequal(Matrix_Spiral(n),y_correct))\r\n\r\n%%\r\nn = 2;\r\ny_correct =[11    11\r\n     0     0];\r\nassert(isequal(Matrix_Spiral(n),y_correct))\r\n\r\n%%\r\nn = 3;\r\ny_correct =[11    11    11\r\n     0     0    11\r\n     0     0     0];\r\nassert(isequal(Matrix_Spiral(n),y_correct))\r\n\r\n%%\r\nn = 4;\r\ny_correct =[11    11    11    11\r\n     0     0     0    11\r\n     0     0    11    11\r\n     0     0     0     0];\r\nassert(isequal(Matrix_Spiral(n),y_correct))\r\n\r\n\r\n%%\r\nn = 5;\r\ny_correct =[11    11    11    11    11\r\n     0     0     0     0    11\r\n     0     0    11     0    11\r\n     0     0    11    11    11\r\n     0     0     0     0     0];\r\nassert(isequal(Matrix_Spiral(n),y_correct))\r\n\r\n%%\r\nn = 10;\r\ny_correct =[11    11    11    11    11    11    11    11    11    11\r\n     0     0     0     0     0     0     0     0     0    11\r\n     0     0    11    11    11    11    11    11     0    11\r\n     0     0    11     0     0     0     0    11     0    11\r\n     0     0    11     0    11    11     0    11     0    11\r\n     0     0    11     0    11     0     0    11     0    11\r\n     0     0    11     0    11    11    11    11     0    11\r\n     0     0    11     0     0     0     0     0     0    11\r\n     0     0    11    11    11    11    11    11    11    11\r\n     0     0     0     0     0     0     0     0     0     0];\r\nassert(isequal(Matrix_Spiral(n),y_correct))\r\n\r\n\r\n%%\r\nn = 17;\r\ny_correct =[11    11    11    11    11    11    11    11    11    11    11    11    11    11    11    11    11\r\n     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0    11\r\n     0     0    11    11    11    11    11    11    11    11    11    11    11    11    11     0    11\r\n     0     0    11     0     0     0     0     0     0     0     0     0     0     0    11     0    11\r\n     0     0    11     0    11    11    11    11    11    11    11    11    11     0    11     0    11\r\n     0     0    11     0    11     0     0     0     0     0     0     0    11     0    11     0    11\r\n     0     0    11     0    11     0    11    11    11    11    11     0    11     0    11     0    11\r\n     0     0    11     0    11     0    11     0     0     0    11     0    11     0    11     0    11\r\n     0     0    11     0    11     0    11     0    11     0    11     0    11     0    11     0    11\r\n     0     0    11     0    11     0    11     0    11    11    11     0    11     0    11     0    11\r\n     0     0    11     0    11     0    11     0     0     0     0     0    11     0    11     0    11\r\n     0     0    11     0    11     0    11    11    11    11    11    11    11     0    11     0    11\r\n     0     0    11     0    11     0     0     0     0     0     0     0     0     0    11     0    11\r\n     0     0    11     0    11    11    11    11    11    11    11    11    11    11    11     0    11\r\n     0     0    11     0     0     0     0     0     0     0     0     0     0     0     0     0    11\r\n     0     0    11    11    11    11    11    11    11    11    11    11    11    11    11    11    11\r\n     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0];\r\nassert(isequal(Matrix_Spiral(n),y_correct))","published":true,"deleted":false,"likes_count":5,"comments_count":3,"created_by":872,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":98,"test_suite_updated_at":"2012-02-20T12:29:13.000Z","rescore_all_solutions":false,"group_id":18,"created_at":"2012-02-20T12:17:08.000Z","updated_at":"2026-04-28T18:06:26.000Z","published_at":"2012-02-20T12:29:13.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMake a spiral in a (n*n) matrix. The spiral has to start in the top left, and has to rotate clockwise to the center. The spiral has to have a padding of zeros between itself.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe (n*n) matrix is filled with zeros except for the spiral, that has to be made of 11 (elevens, for visual reference). The final matrix has to have the same or more zeros than elevens.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample: n=8\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA =\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[    11    11    11    11    11    11    11    11\\n     0     0     0     0     0     0     0    11\\n     0     0    11    11    11    11     0    11\\n     0     0    11     0     0    11     0    11\\n     0     0    11     0    11    11     0    11\\n     0     0    11     0     0     0     0    11\\n     0     0    11    11    11    11    11    11\\n     0     0     0     0     0     0     0     0]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":52664,"title":"List the Moran numbers","description":"The quotient of a Moran number and its digit sum is prime. For example, 117 and 481 are Moran numbers because 117/(1+1+7) is 13 and 481/(4+8+1) = 37, and both 13 and 37 are prime. \r\nWrite a function to list the Moran numbers less than or equal to the input number. ","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 72px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 36px; transform-origin: 407px 36px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 363px 8px; transform-origin: 363px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe quotient of a Moran number and its digit sum is prime. For example, 117 and 481 are Moran numbers because 117/(1+1+7) is 13 and 481/(4+8+1) = 37, and both 13 and 37 are prime. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 257px 8px; transform-origin: 257px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to list the Moran numbers less than or equal to the input number. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = Moran(n)\r\n  y = f(n);\r\nend","test_suite":"%%\r\nn = 500;\r\ny = Moran(n);\r\ny_correct = [18 21 27 42 45 63 84 111 114 117 133 152 153 156 171 190 195 198 201 207 209 222 228 247 261 266 285 333 370 372 399 402 407 423 444 465 481];\r\nassert(isequal(y,y_correct))\r\n\r\n%% \r\nn = 40332;\r\ny = Moran(n);\r\ny23_correct = [207 1679 3749 4577 8717 14099 18653 19067 22793 24449 25691 26519 26933 29417 29831 32729 33557 35627 37283];\r\nassert(isequal(y(mod(y,23)==0),y23_correct) \u0026\u0026 isequal(y(end),n))\r\n\r\n%%\r\nn = [100000 400000 700000 1e6 4e6 7e6 1e7];\r\ns = [383 1193 1870 2451 8080 12913 17271];\r\nlen_correct = [1915 5967 9352 12259 40403 64567 86356];\r\nsum_correct = [79699686 1044807776 2880495403 5339917218 73480226594 205122929098 389309242207];\r\nsd_correct  = [2.925215086021406e+04 1.171076738381341e+05 2.065163622127620e+05 2.944277010513903e+05 1.177431499460555e+06 2.057551640570258e+06 2.933705654924581e+06];\r\nys_correct  = [11354 28489 48992 71660 99972; 51489 125203 210051 300165 399477; 96325 220734 364473 524186 699739; 129627 308214 513837 741778 999219; 579189 1331117 2176042 3062214 3999644; 1046322 2330397 3782883 5322552 6999255; 1440693 3292137 5341677 7565613 9999882];\r\nfor k = 1:length(n)\r\n    disp(['Test 3.' num2str(k)])\r\n    y = Moran(n(k));\r\n    assert(isequal(length(y),len_correct(k)) \u0026\u0026 isequal(sum(y),sum_correct(k)) \u0026\u0026 abs(std(y)-sd_correct(k))\u003c1e-7 \u0026\u0026 isequal(y(s(k):s(k):end),ys_correct(k,:)));\r\nend\r\n\r\n%%\r\nfiletext = fileread('Moran.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'regexp') || contains(filetext, 'oeis') || contains(filetext, 'persistent'); \r\nassert(~illegal)","published":true,"deleted":false,"likes_count":3,"comments_count":2,"created_by":46909,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":25,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-09-05T13:52:35.000Z","updated_at":"2026-06-04T06:39:54.000Z","published_at":"2021-09-05T14:10:51.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe quotient of a Moran number and its digit sum is prime. For example, 117 and 481 are Moran numbers because 117/(1+1+7) is 13 and 481/(4+8+1) = 37, and both 13 and 37 are prime. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to list the Moran numbers less than or equal to the input number. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":42503,"title":"Generating random matrix with given probability mass function","description":"Inspired by \u003chttp://www.mathworks.com/matlabcentral/cody/problems/2356-simulating-the-selection-of-a-state-with-given-probabilities Problem 2356. Simulating the selection of a state with given probabilities\u003e, let's consider a similar yet more useful problem. Write a function\r\n\r\n                             x = rndsampling(m,n,prob)\r\n\r\nto generate an m-by-n matrix x, whose entries are drawn independently from integer symbols 1:numel(prob) according to the given probability mass function prob. Specifically, symbol k occurs with probability prob(k), k = 1, 2, ..., numel(prob), where all(prob\u003e0) == 1 and sum(prob) == 1.","description_html":"\u003cp\u003eInspired by \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/2356-simulating-the-selection-of-a-state-with-given-probabilities\"\u003eProblem 2356. Simulating the selection of a state with given probabilities\u003c/a\u003e, let's consider a similar yet more useful problem. Write a function\u003c/p\u003e\u003cpre\u003e                             x = rndsampling(m,n,prob)\u003c/pre\u003e\u003cp\u003eto generate an m-by-n matrix x, whose entries are drawn independently from integer symbols 1:numel(prob) according to the given probability mass function prob. Specifically, symbol k occurs with probability prob(k), k = 1, 2, ..., numel(prob), where all(prob\u0026gt;0) == 1 and sum(prob) == 1.\u003c/p\u003e","function_template":"function x = rndsampling(m,n,prob);\r\n  x = rand(m,n)\r\nend","test_suite":"%%\r\nrnd = sort(rand(randi([10,20]),1));\r\nprob = vertcat(rnd(1,:),diff(rnd,1,1),1-rnd(end,:));\r\nsz = [1 1e5;1e5 1;1e3 1e2;randi([100 200], 100, 2)];\r\nsz = sz(randi(size(sz,1)),:);\r\nx = rndsampling(sz(1),sz(2),prob);\r\nprob_est = histcounts(x,1:numel(prob)+1,'Normalization','probability').';\r\nerr = mean(abs(prob_est - prob))\r\nassert(err \u003c 0.005 \u0026\u0026 isequal(size(x),sz) \u0026\u0026 all(~isnan(x(:))));\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":1,"created_by":12569,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":132,"test_suite_updated_at":"2015-08-13T18:44:59.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2015-08-11T19:26:49.000Z","updated_at":"2026-05-14T19:22:17.000Z","published_at":"2015-08-11T19:26:49.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInspired by\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/2356-simulating-the-selection-of-a-state-with-given-probabilities\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 2356. Simulating the selection of a state with given probabilities\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, let's consider a similar yet more useful problem. Write a function\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[                             x = rndsampling(m,n,prob)]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eto generate an m-by-n matrix x, whose entries are drawn independently from integer symbols 1:numel(prob) according to the given probability mass function prob. Specifically, symbol k occurs with probability prob(k), k = 1, 2, ..., numel(prob), where all(prob\u0026gt;0) == 1 and sum(prob) == 1.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":196,"title":"love is an n-letter word","description":"Given a list of *N words*, return the *N-letter word* (choosing one letter from each word) with the property of having the least distance between each pair of two consecutive letters (if there are multiple optimal solutions return any one of them).\r\n\r\nExample: s1 = {'abcd','bcde','cdef','defg'}; should return s2 = 'dddd'; (with total letter-distance = 0)\r\n\r\nExample: s1={'aldfejk','czoa','vwy','abcde'}; should return s2='love'; (with total letter-distance = 27: _|'l'-'o'|_=3 + _|'o'-'v'|_=7 + _|'v'-'e'|_=17 ; compare for example to the possible word 'aave' which has a total letter-distance of 38)\r\n\r\n\r\n\r\n","description_html":"\u003cp\u003eGiven a list of \u003cb\u003eN words\u003c/b\u003e, return the \u003cb\u003eN-letter word\u003c/b\u003e (choosing one letter from each word) with the property of having the least distance between each pair of two consecutive letters (if there are multiple optimal solutions return any one of them).\u003c/p\u003e\u003cp\u003eExample: s1 = {'abcd','bcde','cdef','defg'}; should return s2 = 'dddd'; (with total letter-distance = 0)\u003c/p\u003e\u003cp\u003eExample: s1={'aldfejk','czoa','vwy','abcde'}; should return s2='love'; (with total letter-distance = 27: \u003ci\u003e\u003ctt\u003e'l'-'o'\u003c/tt\u003e\u003c/i\u003e=3 + \u003ci\u003e\u003ctt\u003e'o'-'v'\u003c/tt\u003e\u003c/i\u003e=7 + \u003ci\u003e\u003ctt\u003e'v'-'e'\u003c/tt\u003e\u003c/i\u003e=17 ; compare for example to the possible word 'aave' which has a total letter-distance of 38)\u003c/p\u003e","function_template":"function s2 = gobbledigook(s1)\r\n  s2 = '';\r\nend","test_suite":"%%\r\ns1 = {'abcd','bcde','cdef','defg'}; \r\nassert(isequal(gobbledigook(s1),'dddd'))\r\ns2_correct = 'dddd';\r\n%%\r\ns1 = {'aldfejk','czoa','vwy','abcde'}; \r\nassert(isequal(gobbledigook(s1),'love'))\r\ns2_correct = 'love';\r\n%%\r\ns1 = {'some','help','check','viterbi','algorithm'}; \r\nassert(isequal(gobbledigook(s1),'eeeeg'))\r\ns2_correct = 'eeeeg';\r\n%%\r\ns1 = {'ldjfac','deamv','fka','idlw','pqmfjavs'}; \r\nassert(isequal(gobbledigook(s1),'lmklm')|isequal(gobbledigook(s1),'aaadf'))\r\ns2_correct = 'lmklm';\r\ns2_correct = 'aaadf';\r\n%% \r\n% avoids look-up table hack\r\ns1 = cellfun(@(x)char('a'-1+randi(26,1,5)),cell(1,7),'uniformoutput',false);\r\nassert(all(any(bsxfun(@eq,gobbledigook(s1),cell2mat(cellfun(@(x)x',s1,'uniformoutput',false)))))\u0026all(sum(abs(diff(double(gobbledigook(s1)))))\u003c=sum(abs(diff(double(cell2mat(cellfun(@(x)x(randi(numel(x),1,1000))',s1,'uniformoutput',false))),1,2)),2)));","published":true,"deleted":false,"likes_count":4,"comments_count":5,"created_by":43,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":65,"test_suite_updated_at":"2012-03-08T02:36:04.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2012-01-31T08:36:36.000Z","updated_at":"2026-06-10T20:34:10.000Z","published_at":"2012-03-08T03:14:35.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a list of\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN words\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, return the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN-letter word\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (choosing one letter from each word) with the property of having the least distance between each pair of two consecutive letters (if there are multiple optimal solutions return any one of them).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample: s1 = {'abcd','bcde','cdef','defg'}; should return s2 = 'dddd'; (with total letter-distance = 0)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample: s1={'aldfejk','czoa','vwy','abcde'}; should return s2='love'; (with total letter-distance = 27:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e'l'-'o'\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e=3 +\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e'o'-'v'\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e=7 +\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e'v'-'e'\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e=17 ; compare for example to the possible word 'aave' which has a total letter-distance of 38)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44345,"title":"MATLAB Counter","description":"Write a function f = counter(x0,b) to construct a counter handle f that counts with an initial value x0 and a step size b. \r\n\r\nE.g.,\r\n\r\n  \u003e\u003e f = counter(0,1)  % Initialize a counter f() with initial_count = 0 and step_size = 1\r\n  \u003e\u003e f()\r\n  ans =\r\n       0\r\n  \u003e\u003e f()\r\n  ans =\r\n       1\r\n  \u003e\u003e f()\r\n  ans =\r\n       2\r\n\r\n\r\n","description_html":"\u003cp\u003eWrite a function f = counter(x0,b) to construct a counter handle f that counts with an initial value x0 and a step size b.\u003c/p\u003e\u003cp\u003eE.g.,\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e\u0026gt;\u0026gt; f = counter(0,1)  % Initialize a counter f() with initial_count = 0 and step_size = 1\r\n\u0026gt;\u0026gt; f()\r\nans =\r\n     0\r\n\u0026gt;\u0026gt; f()\r\nans =\r\n     1\r\n\u0026gt;\u0026gt; f()\r\nans =\r\n     2\r\n\u003c/pre\u003e","function_template":"function y = counter(x,b)\r\n  y = x;\r\nend","test_suite":"%%\r\nassessFunctionAbsence({'regexp','regexpi','regexprep','str2num'},'FileName','counter.m')\r\n\r\n%%\r\nf = counter(0,1);\r\nassert(isequal(f(),0))\r\nassert(isequal(f(),1))\r\nassert(isequal(2,f()))\r\nassert(isequal(3,f()))\r\n\r\n%%\r\nf = counter(1,0);\r\nassert(isequal(f(),1))\r\nassert(isequal(f(),1))\r\nassert(isequal(1,f()))\r\nassert(isequal(1,f()))\r\n\r\n%%\r\nf = counter(10,2);\r\nassert(isequal(f(),10))\r\nassert(isequal(f(),12))\r\nassert(isequal(14,f()))\r\nassert(isequal(16,f()))\r\n\r\n%%\r\nf = counter(0,5);\r\ny_correct = [0, 5, 10, 15, 20, 55];\r\nassert(isequal([f() f() f() f() f() f()+f()],y_correct))\r\n\r\n%%\r\nx0 = randi(10);\r\nb = randi(10);\r\nf = counter(x0,b);\r\ny_correct = x0 + (0:1000)*b;\r\nassert(isequal(arrayfun(@(n)f(),0:1000),y_correct))","published":true,"deleted":false,"likes_count":23,"comments_count":9,"created_by":12569,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":305,"test_suite_updated_at":"2017-10-17T00:19:49.000Z","rescore_all_solutions":false,"group_id":34,"created_at":"2017-09-24T01:58:21.000Z","updated_at":"2026-05-28T04:25:58.000Z","published_at":"2017-10-16T01:45:08.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function f = counter(x0,b) to construct a counter handle f that counts with an initial value x0 and a step size b.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eE.g.,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[\u003e\u003e f = counter(0,1)  % Initialize a counter f() with initial_count = 0 and step_size = 1\\n\u003e\u003e f()\\nans =\\n     0\\n\u003e\u003e f()\\nans =\\n     1\\n\u003e\u003e f()\\nans =\\n     2]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":1241,"title":"PACMAT  - Ghosts maximize unique locations; 3 Lives","description":"The Classic PACMAN game brought to Cody.\r\n\r\nPACMAT Ghosts Random requires clearing the board of Yellow Dots while not bumping into the wandering ghosts in 3 lives. Adjacent Ghosts will capture PACMAT.  Ghosts do not use the tunnel. On Ghost capture everyone gets reset.\r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/PACMAT_300.jpg\u003e\u003e\r\n\r\nTo aid in development of your routine, a PACMAT_Ghosts_002.m file that creates a video has been posted at \u003chttps://sites.google.com/site/razapor/matlab_cody/PACMAT_Ghosts_002.m PACMAT_Ghosts_002.m\u003e. (Right click, 'save link as'). Using patches thus enable/figure  disable/video for best results.\r\n\r\n\r\n\u003chttps://sites.google.com/site/razapor/matlab_cody/PACMAT_G002_video_ANC.mp4 Alfonso's Enhanced Ghost Avoider\u003e (MP4) Quite an impressive solution\r\n\r\n\r\nThe reference solution uses Alfonso's Local Optimum Algorithm with a Catch.\r\n\r\n*Inputs:* Map   Definitions: -1=Wall, 0=Empty, 1=Dot, 2=PACMAT, \u003e2=Ghost\r\n\r\n*Output:* Direction  Definitions: 1-Up, 2-Right, 3-Down, 4-Left, 0-No move\r\n\r\n*Scoring:* Total # of Moves to Clear the Yellow Dots +(LivesRemaining-3)*100\r\n\r\n\r\n*Near Future:* Ghosts with LOS Tracking.\r\n\r\n*Future:* Player will be Team Ghosts versus PACMAT_BOT","description_html":"\u003cp\u003eThe Classic PACMAN game brought to Cody.\u003c/p\u003e\u003cp\u003ePACMAT Ghosts Random requires clearing the board of Yellow Dots while not bumping into the wandering ghosts in 3 lives. Adjacent Ghosts will capture PACMAT.  Ghosts do not use the tunnel. On Ghost capture everyone gets reset.\u003c/p\u003e\u003cimg src=\"https://sites.google.com/site/razapor/matlab_cody/PACMAT_300.jpg\"\u003e\u003cp\u003eTo aid in development of your routine, a PACMAT_Ghosts_002.m file that creates a video has been posted at \u003ca href=\"https://sites.google.com/site/razapor/matlab_cody/PACMAT_Ghosts_002.m\"\u003ePACMAT_Ghosts_002.m\u003c/a\u003e. (Right click, 'save link as'). Using patches thus enable/figure  disable/video for best results.\u003c/p\u003e\u003cp\u003e\u003ca href=\"https://sites.google.com/site/razapor/matlab_cody/PACMAT_G002_video_ANC.mp4\"\u003eAlfonso's Enhanced Ghost Avoider\u003c/a\u003e (MP4) Quite an impressive solution\u003c/p\u003e\u003cp\u003eThe reference solution uses Alfonso's Local Optimum Algorithm with a Catch.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInputs:\u003c/b\u003e Map   Definitions: -1=Wall, 0=Empty, 1=Dot, 2=PACMAT, \u003e2=Ghost\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e Direction  Definitions: 1-Up, 2-Right, 3-Down, 4-Left, 0-No move\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring:\u003c/b\u003e Total # of Moves to Clear the Yellow Dots +(LivesRemaining-3)*100\u003c/p\u003e\u003cp\u003e\u003cb\u003eNear Future:\u003c/b\u003e Ghosts with LOS Tracking.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFuture:\u003c/b\u003e Player will be Team Ghosts versus PACMAT_BOT\u003c/p\u003e","function_template":"function  [newdir]=pacmat(map)\r\n% 314 move solver if Ghosts do not move\r\n persistent ptr\r\n if isempty(ptr)\r\n  ptr=['bbbbbbbcccbbbbbcccdddddddddddddddddddddddddaaa'...\r\n      'bbbbbaaaaaaaaaaaaaaaaaaaaaaaaadddddcccccccbbbbddddaaabbbbbbbb'...\r\n      'cccbbbdddaaabbbaaaadddddbbbbbccccbbbbbbbbbbbbbbaaaaddddddddddd'...\r\n      'ccccbbbcccdddbbbaaabbbaaaccccccbbbbbaaccdddddccccccccccccccaabbbbbcccddccc'...\r\n      'dddaaaaaaddddddcccbbbcccdddcccdddaaadddaaaddbbbbbaaadddddddddddcccbbccc'];\r\n  ptr=(ptr-'a')+1;\r\n end\r\n  \r\n newdir=ptr(1);\r\n ptr(1)=[];\r\n\r\n% usage of newdir=randi(4) will barely move\r\nend","test_suite":"%%\r\nfeval(@assignin,'caller','score',2000);\r\n%%\r\nmax_moves=2000; % Fixed path expect to succeed by 600 moves\r\n\r\nmap=[...\r\n      repmat('a',1,28);\r\n      'accccccccccccaacccccccccccca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acccccccccccccccccccccccccca';\r\n      'acaaaacaacaaaaaaaacaacaaaaca';\r\n      'acaaaacaacaaaaaaaacaacaaaaca';\r\n      'accccccaaccccaaccccaacccccca';\r\n      'aaaaaacaaaaabaabaaaaacaaaaaa';\r\n      'aaaaaacaaaaabaabaaaaacaaaaaa';\r\n      'aaaaaacaabbbbbbbbbbaacaaaaaa';\r\n      'aaaaaacaabaaabbaaabaacaaaaaa';\r\n      'aaaaaacaabalbbbblabaacaaaaaa';\r\n      'bbbbbbcbbbabbbbbbabbbcbbbbbb';\r\n      'aaaaaacaabalbbbblabaacaaaaaa';\r\n      'aaaaaacaabaaaaaaaabaacaaaaaa';\r\n      'aaaaaacaabbbbbbbbbbaacaaaaaa';\r\n      'aaaaaacaabaaaaaaaabaacaaaaaa';\r\n      'aaaaaacaabaaaaaaaabaacaaaaaa';\r\n      'accccccccccccaacccccccccccca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acccaacccccccbdcccccccaaccca';\r\n      'aaacaacaacaaaaaaaacaacaacaaa';\r\n      'aaacaacaacaaaaaaaacaacaacaaa';\r\n      'accccccaaccccaaccccaacccccca';\r\n      'acaaaaaaaaaacaacaaaaaaaaaaca';\r\n      'acaaaaaaaaaacaacaaaaaaaaaaca';\r\n      'acccccccccccccccccccccccccca';\r\n      repmat('a',1,28);];\r\n  \r\n  map=map-'b';\r\n  [nr, nc]=size(map);\r\n\r\n  gmap=map; % Map used by ghosts to simplify PAC Capture\r\n  gmap(15,6)=Inf; %No tunnel ghosts\r\n  gmap(15,26)=Inf;\r\n  gmap(map==-1)=Inf; % walls to Inf\r\n  gmap(map\u003e2)=Inf; % Elim start points as viable moves, quicker box exit\r\n\r\n\r\n  mapdelta=[-1 nr 1 -nr]; % Valid as long as not on an edge\r\n  gmovxy=[0 -1;1 0;0 1;-1 0];\r\n\r\n  tunnel=find(map(:,1)==0); % tunnelptr\r\n  tunnel=[tunnel tunnel+nr*(nc-1)]; % Entrance/Exit Tunnel\r\n\r\n  [pmr, pmc]=find(map==2); % pi 24 row  pj 15 column of map\r\n   ptrpac=find(map==2);\r\n\r\n  ptrpac=find(map==2);\r\n  ptrpac_start=ptrpac;\r\n  ptrg_start=find(map\u003e2);\r\n  map(ptrg_start)=[10 20 30 40];% use deal?\r\n  [gstartx, gstarty]=find(map\u003e2);\r\n  \r\n  lives=3; % Lives\r\n  movepac=0;\r\n\r\nwhile lives \u0026\u0026 any(mod(map(:),10)==1) \u0026\u0026 movepac\u003cmax_moves\r\n movepac=movepac+1;\r\n\r\n [curdir]=pacmat(map);\r\n% if curdir==0,continue;end % Inf loop error\r\n [pmr, pmc]=find(map==2);\r\nif curdir\u003e0\r\n if map(ptrpac+mapdelta(curdir))==-1\r\n  % Do nothing - Ran into a Wall\r\n elseif map(ptrpac+mapdelta(curdir))\u003e2 % ran into ghost\r\n  map(ptrpac)=0; % remove PAC from the board\r\n  lives=lives-1;\r\n  if lives==0,break;end\r\n  % reset the board\r\n  [ptrgx, ptrgy]=find(map\u003e2);\r\n  ptrg=find(map\u003e2);\r\n  map(ptrg)=mod(map(ptrg),10);\r\n  map(ptrpac_start)=2;\r\n  map(ptrg_start)=[10 20 30 40];\r\n  ptrpac=find(map==2);\r\n  continue;\r\n else % legal move\r\n  map(ptrpac)=0; % Eat Dot and clear PAC\r\n  ptrpac=ptrpac+mapdelta(curdir);\r\n  if ptrpac==tunnel(1),ptrpac=tunnel(2)-nr;end\r\n  if ptrpac==tunnel(2),ptrpac=tunnel(1)+nr;end\r\n  map(ptrpac)=2;\r\n end\r\nend % curdir \u003e0\r\n\r\n% Ghosts\r\n for i=1:4\r\n\r\n   gmapT=gmap;\r\n   ptrg=find(map\u003e2); % Find all ghosts\r\n   gmapT(ptrg)=Inf; % Rule out moving onto a ghost\r\n\r\n\r\n  dot=false;\r\n  [gptrx, gptry]=find(map==10*i);\r\n  gidx=find(map==10*i);\r\n  if isempty(gidx)\r\n   [gptrx, gptry]=find(map==10*i+1); % ghost must be on a dot\r\n   gidx=find(map==10*i+1);\r\n   dot=true;\r\n  end\r\n\r\n% Find valid ghost moves using gmap\r\n% mapdelta=[-1 nr 1 -nr]; \r\n  gmov=find(map(gidx+mapdelta)==2); % adjacent to PACMAT\r\n  if ~isempty(gmov) % PAC adjacent\r\n   lives=lives-1;\r\n   if lives==0,break;end\r\n   % reset the board\r\n   [pmr, pmc]=find(map==2); % PACMAT erase coords\r\n   map(map==2)=0;\r\n      \r\n   [ptrgx, ptrgy]=find(map\u003e2);\r\n   ptrg=find(map\u003e2);\r\n   map(ptrg)=mod(map(ptrg),10);\r\n   map(ptrpac_start)=2;\r\n   map(ptrg_start)=[10 20 30 40];\r\n   ptrpac=find(map==2);     \r\n   break; % Ghost move loop\r\n      \r\n  else % gmap/gmapT avoids tunnel,other ghosts, Walls\r\n \r\n   gmap(gidx)=gmap(gidx)+1;\r\n   ghost_adj=gmapT(gidx+mapdelta);\r\n   if min(ghost_adj)\u003cInf\r\n    if rand\u003c0.5 % Push ghosts away from each other\r\n     gmov=find(ghost_adj==min(ghost_adj),1,'first');\r\n    else\r\n     gmov=find(ghost_adj==min(ghost_adj),1,'last');\r\n    end\r\n   else\r\n    gmov=[];\r\n   end\r\n \r\n   if ~isempty(gmov) % valid g move : ghost may not stand on ghost\r\n    map(gptrx,gptry)=mod(map(gptrx,gptry),10);\r\n    map(gidx+mapdelta(gmov))=map(gidx+mapdelta(gmov))+10*i;     \r\n   end % ~isempty(gmov) standard move - no capture\r\n\r\n  end % ~isempty(gmov) PACMAT adjacent\r\n  \r\n end % i ghost moves\r\nend % while alive\r\n\r\nfprintf('moves %i\\n',movepac)\r\n\r\nassert(lives\u003e0,sprintf('Three Captures\\n'))\r\nassert(~isempty(any(mod(map(:),10)==1)),sprintf('Moves\\n',movepac)) % Test Move Timeout\r\n\r\n\r\nfeval( @assignin,'caller','score',floor(min( 2000,300-100*lives+movepac )) );\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":15,"test_suite_updated_at":"2013-02-02T05:09:50.000Z","rescore_all_solutions":false,"group_id":33,"created_at":"2013-02-02T00:36:11.000Z","updated_at":"2026-04-23T18:03:04.000Z","published_at":"2013-02-02T01:21:05.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.JPEG\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Classic PACMAN game brought to Cody.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePACMAT Ghosts Random requires clearing the board of Yellow Dots while not bumping into the wandering ghosts in 3 lives. Adjacent Ghosts will capture PACMAT. Ghosts do not use the tunnel. On Ghost capture everyone gets reset.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo aid in development of your routine, a PACMAT_Ghosts_002.m file that creates a video has been posted at\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://sites.google.com/site/razapor/matlab_cody/PACMAT_Ghosts_002.m\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ePACMAT_Ghosts_002.m\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. (Right click, 'save link as'). Using patches thus enable/figure disable/video for best results.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://sites.google.com/site/razapor/matlab_cody/PACMAT_G002_video_ANC.mp4\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eAlfonso's Enhanced Ghost Avoider\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (MP4) Quite an impressive solution\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe reference solution uses Alfonso's Local Optimum Algorithm with a Catch.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInputs:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Map Definitions: -1=Wall, 0=Empty, 1=Dot, 2=PACMAT, \u0026gt;2=Ghost\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Direction Definitions: 1-Up, 2-Right, 3-Down, 4-Left, 0-No move\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Total # of Moves to Clear the Yellow Dots +(LivesRemaining-3)*100\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNear Future:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Ghosts with LOS Tracking.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eFuture:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Player will be Team Ghosts versus PACMAT_BOT\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" 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\"}]}"},{"id":81,"title":"Mandelbrot Numbers","description":"The \u003chttp://en.wikipedia.org/wiki/Mandelbrot_set Mandelbrot Set\u003e is built around a simple iterative equation.\r\n\r\n z(1)   = c\r\n z(n+1) = z(n)^2 + c\r\n\r\nFor any complex c, we can continue this iteration until either\r\nabs(z(n+1)) \u003e 2 or n == lim, then return the iteration count n.\r\n\r\n* If c = 0   and lim = 3, then z = [0 0 0] and n = 3.\r\n* If c = 1   and lim = 5, then z = [1 2], and n = length(z) or 2.\r\n* If c = 0.5 and lim = 5, then z = [0.5000 0.7500 1.0625 1.6289] and n = 4.\r\n\r\nFor a matrix of complex numbers C, return a corresponding matrix N such\r\nthat each element of N is the iteration count n for each complex number c\r\nin the matrix C, subject to the iteration count limit of lim.\r\n\r\nIf C = [0 0.5; 1 4] and lim = 5, then N = [5 4; 2 1]\r\n\r\nCleve Moler has a whole chapter on the Mandelbrot set in his book Experiments\r\nwith MATLAB: \u003chttp://www.mathworks.com/moler/exm/chapters/mandelbrot.pdf \r\nChapter 10, Mandelbrot Set (PDF)\u003e","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 296.167px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 148.083px; transform-origin: 407px 148.083px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 12.5px 8px; transform-origin: 12.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"http://en.wikipedia.org/wiki/Mandelbrot_set\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eMandelbrot Set\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 133px 8px; transform-origin: 133px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is built around a simple iterative equation.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 40.8667px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 20.4333px; transform-origin: 404px 20.4333px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 44px 8.5px; transform-origin: 44px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e z(1)   = c\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 80px 8.5px; transform-origin: 80px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e z(n+1) = z(n)^2 + c\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 379.5px 8px; transform-origin: 379.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor any complex c, we can continue this iteration until either abs(z(n+1)) \u0026gt; 2 or n == lim, then return the iteration count n.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 61.3px; counter-reset: list-item 0; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 30.65px; transform-origin: 391px 30.65px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 142.5px 8px; transform-origin: 142.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf c = 0 and lim = 3, then z = [0 0 0] and n = 3.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 176.5px 8px; transform-origin: 176.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf c = 1 and lim = 5, then z = [1 2], and n = length(z) or 2.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 226.5px 8px; transform-origin: 226.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf c = 0.5 and lim = 5, then z = [0.5000 0.7500 1.0625 1.6289] and n = 4.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 377px 8px; transform-origin: 377px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor a matrix of complex numbers C, return a corresponding matrix N such that each element of N is the iteration count n for each complex number c in the matrix C, subject to the iteration count limit of lim.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 149.5px 8px; transform-origin: 149.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf C = [0 0.5; 1 4] and lim = 5, then N = [5 4; 2 1]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 127px 8px; transform-origin: 127px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eCleve Moler has a whole chapter on the \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/content/dam/mathworks/mathworks-dot-com/moler/exm/chapters/mandelbrot.pdf\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eMandelbrot set\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 38px 8px; transform-origin: 38px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e in his book \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/moler/exm/chapters.html\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eExperiments with MATLAB\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function N = mandelbrot(C,lim)\r\n  N = ones(size(C));\r\nend","test_suite":"%%\r\nC = 0;\r\nlim = 5;\r\nN_correct = 5;\r\nassert(isequal(mandelbrot(C,lim),N_correct))\r\n\r\n%%\r\nC = [0 0.5; 1 4];\r\nlim = 5;\r\nN_correct = [5 4; 2 1];\r\nassert(isequal(mandelbrot(C,lim),N_correct))\r\n\r\n%%\r\ni = sqrt(-1);\r\nC = [i 1 -2*i -2];\r\nlim = 10;\r\nN_correct = [10 2 1 10];\r\nassert(isequal(mandelbrot(C,lim),N_correct))","published":true,"deleted":false,"likes_count":17,"comments_count":9,"created_by":1,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":1781,"test_suite_updated_at":"2012-01-26T03:21:20.000Z","rescore_all_solutions":false,"group_id":2,"created_at":"2012-01-18T01:00:28.000Z","updated_at":"2026-05-05T05:05:17.000Z","published_at":"2012-01-18T01:00:28.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Mandelbrot_set\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMandelbrot Set\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is built around a simple iterative equation.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ z(1)   = c\\n z(n+1) = z(n)^2 + c]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor any complex c, we can continue this iteration until either abs(z(n+1)) \u0026gt; 2 or n == lim, then return the iteration count n.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf c = 0 and lim = 3, then z = [0 0 0] and n = 3.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf c = 1 and lim = 5, then z = [1 2], and n = length(z) or 2.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf c = 0.5 and lim = 5, then z = [0.5000 0.7500 1.0625 1.6289] and n = 4.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor a matrix of complex numbers C, return a corresponding matrix N such that each element of N is the iteration count n for each complex number c in the matrix C, subject to the iteration count limit of lim.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf C = [0 0.5; 1 4] and lim = 5, then N = [5 4; 2 1]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCleve Moler has a whole chapter on the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/content/dam/mathworks/mathworks-dot-com/moler/exm/chapters/mandelbrot.pdf\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMandelbrot set\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e in his book \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/moler/exm/chapters.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eExperiments with MATLAB\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":1740,"title":"Find number patterns in rows of a matrix...","description":"So I think this may be a hard one, may be it was only hard for me!  So here is what it is, I will give a matrix, with some number of rows by 5 columns, to make it simpler I will not repeat any number in a *single row*, so all numbers in a *single rows* are unique. So you need to find up to 2, 3 or 4 numbers that are found in a *single row* that repeat in *other rows* and the amount of times they repeat in a given matrix. To make it a little better, you only need to display the rows/numbers that repeat at lease twice or more. Examples are the best way to show what to do:\r\n  \r\n          %p is the given matrix\r\n          p = [1 2 3 4 5;\r\n               2 3 4 5 1;\r\n               3 4 6 7 8;\r\n               1 2 4 3 5;\r\n               4 6 3 2 1];\r\n          %v is the amount of elements per row that are being checked to see if they repeat in another row\r\n          v = 4;\r\n        %Output:\r\n        out =\r\n        \r\n             1     2     3     4     4\r\n             1     2     3     5     3\r\n             1     2     4     5     3\r\n             1     3     4     5     3\r\n             2     3     4     5     3\r\n      %So the first to the fourth column are the four number that were found to repeat in rows of matrix p (any and all combination that can appear in the rows of the matrix p). The fifth column is the amount of times the that particular set of matrix out(?,1:4) appear in matrix p. for for row one in matrix out, 1,2,3 and 4 in any combination appear 4 time in the rows of matrix p.\r\n      \r\n      So more examples...\r\n      \r\n      p = [1 2 3 4 9;\r\n           2 3 1 4 8]\r\n      v = 4;\r\n      \r\n      out =\r\n           1     2     3     4     2\r\n    %so any combination of 1,2,3 and 4 appear 2 times in matrix p\r\n    \r\n    and another:\r\n    \r\n    p = [1 2 3 4 9;\r\n         2 3 1 4 8;\r\n         3 4 2 7 1]\r\n    v = 3;\r\n      \r\n    out =\r\n         1     2     3     3\r\n         1     2     4     3\r\n         1     3     4     3\r\n         2     3     4     3\r\n    %so this uses any combo of 3 values, so in this case out(?,1:3) are the number combos that can be found in matrix p, and the out(?,4) is the amount of times that those combos are found\r\n    \r\n    last example:\r\n    \r\n    p = [1 2 3 4 9;\r\n         2 3 1 4 8]\r\n    v = 2;\r\n    \r\n    out =\r\n         1     2     2\r\n         1     3     2\r\n         1     4     2\r\n         2     3     2\r\n         2     4     2\r\n         3     4     2\r\n    %so in this case only combos of out(?,1:2) are found and out(?,3) is the amounts of times that combo of out(?,1:2) is found in matrix p. so row four of matrix out (so out(4,:)...) shows that the combo of 2 and 3 are found 2 times in matrix p.\r\n\r\nNOTE: only display any row number combos that occur at least 2 times or more!\r\n\r\nI hope I gave enough instructions and explanation, if not please let me know and I will add to it! Thanks and good luck!","description_html":"\u003cp\u003eSo I think this may be a hard one, may be it was only hard for me!  So here is what it is, I will give a matrix, with some number of rows by 5 columns, to make it simpler I will not repeat any number in a \u003cb\u003esingle row\u003c/b\u003e, so all numbers in a \u003cb\u003esingle rows\u003c/b\u003e are unique. So you need to find up to 2, 3 or 4 numbers that are found in a \u003cb\u003esingle row\u003c/b\u003e that repeat in \u003cb\u003eother rows\u003c/b\u003e and the amount of times they repeat in a given matrix. To make it a little better, you only need to display the rows/numbers that repeat at lease twice or more. Examples are the best way to show what to do:\u003c/p\u003e\u003cpre\u003e          %p is the given matrix\r\n          p = [1 2 3 4 5;\r\n               2 3 4 5 1;\r\n               3 4 6 7 8;\r\n               1 2 4 3 5;\r\n               4 6 3 2 1];\r\n          %v is the amount of elements per row that are being checked to see if they repeat in another row\r\n          v = 4;\r\n        %Output:\r\n        out =\u003c/pre\u003e\u003cpre\u003e             1     2     3     4     4\r\n             1     2     3     5     3\r\n             1     2     4     5     3\r\n             1     3     4     5     3\r\n             2     3     4     5     3\r\n      %So the first to the fourth column are the four number that were found to repeat in rows of matrix p (any and all combination that can appear in the rows of the matrix p). The fifth column is the amount of times the that particular set of matrix out(?,1:4) appear in matrix p. for for row one in matrix out, 1,2,3 and 4 in any combination appear 4 time in the rows of matrix p.\u003c/pre\u003e\u003cpre\u003e      So more examples...\u003c/pre\u003e\u003cpre\u003e      p = [1 2 3 4 9;\r\n           2 3 1 4 8]\r\n      v = 4;\u003c/pre\u003e\u003cpre\u003e      out =\r\n           1     2     3     4     2\r\n    %so any combination of 1,2,3 and 4 appear 2 times in matrix p\u003c/pre\u003e\u003cpre\u003e    and another:\u003c/pre\u003e\u003cpre\u003e    p = [1 2 3 4 9;\r\n         2 3 1 4 8;\r\n         3 4 2 7 1]\r\n    v = 3;\u003c/pre\u003e\u003cpre\u003e    out =\r\n         1     2     3     3\r\n         1     2     4     3\r\n         1     3     4     3\r\n         2     3     4     3\r\n    %so this uses any combo of 3 values, so in this case out(?,1:3) are the number combos that can be found in matrix p, and the out(?,4) is the amount of times that those combos are found\u003c/pre\u003e\u003cpre\u003e    last example:\u003c/pre\u003e\u003cpre\u003e    p = [1 2 3 4 9;\r\n         2 3 1 4 8]\r\n    v = 2;\u003c/pre\u003e\u003cpre\u003e    out =\r\n         1     2     2\r\n         1     3     2\r\n         1     4     2\r\n         2     3     2\r\n         2     4     2\r\n         3     4     2\r\n    %so in this case only combos of out(?,1:2) are found and out(?,3) is the amounts of times that combo of out(?,1:2) is found in matrix p. so row four of matrix out (so out(4,:)...) shows that the combo of 2 and 3 are found 2 times in matrix p.\u003c/pre\u003e\u003cp\u003eNOTE: only display any row number combos that occur at least 2 times or more!\u003c/p\u003e\u003cp\u003eI hope I gave enough instructions and explanation, if not please let me know and I will add to it! Thanks and good luck!\u003c/p\u003e","function_template":"function out = findRowPattern(p,v)\r\n  out = [p v];\r\nend","test_suite":"%%\r\np = [1 2 3 4 5;\r\n     2 3 4 5 1;\r\n     3 4 6 7 8;\r\n     1 2 4 3 5;\r\n     4 6 3 2 1];\r\nv = 4;\r\nout = [1 2 3 4 4;\r\n       1 2 3 5 3;\r\n       1 2 4 5 3;\r\n       1 3 4 5 3;\r\n       2 3 4 5 3]\r\nassert(isequal(findRowPattern(p,v),out))\r\n%%\r\np = [1 2 3 4 9;\r\n     2 3 1 4 8]\r\nv = 4;\r\nout = [1 2 3 4 2]\r\nassert(isequal(findRowPattern(p,v),out))\r\n%%\r\np = [1 2 3 4 9;\r\n     2 3 1 4 8;\r\n     3 4 2 7 1]\r\nv = 3;\r\nout = [1 2 3 3;\r\n       1 2 4 3;\r\n       1 3 4 3;\r\n       2 3 4 3]\r\nassert(isequal(findRowPattern(p,v),out))\r\n%%\r\np = [1 2 3 4 9;\r\n     2 3 1 4 8]\r\nv = 2;\r\nout = [1 2 2;\r\n       1 3 2;\r\n       1 4 2;\r\n       2 3 2;\r\n       2 4 2;\r\n       3 4 2]\r\nassert(isequal(findRowPattern(p,v),out))\r\n%%\r\np = [15 23 68 49 88;\r\n     69 58 78 21 35;\r\n     10 23 21 35 88;\r\n     99 58 63 24 10;\r\n     64 28 14 33 58;\r\n     85 69 21 45 55;\r\n     99 24 76 49 33;\r\n     89 69 33 98 21;\r\n     99 10 21 55 58;\r\n     35 68 69 44 21;\r\n     21 69 35 46 33];\r\nv = 3;\r\nout = [21 35 69 3;\r\n     10 58 99 2;\r\n     21 33 69 2]\r\nassert(isequal(findRowPattern(p,v),out))\r\n%%\r\np = [15 23 68 49 88;\r\n     69 58 78 21 35;\r\n     10 23 21 35 88;\r\n     99 58 63 24 10;\r\n     64 28 14 33 58;\r\n     85 69 21 45 55;\r\n     99 24 76 49 33;\r\n     89 69 33 98 21;\r\n     99 10 21 55 58;\r\n     35 68 69 44 21;\r\n     21 69 35 46 33];\r\nv = 2;\r\n\r\nout = [21    69     5;\r\n       21    35     4;\r\n       35    69     3;\r\n       10    21     2;\r\n       10    58     2;\r\n       10    99     2;\r\n       21    33     2;\r\n       21    55     2;\r\n       21    58     2;\r\n       23    88     2;\r\n       24    99     2;\r\n       33    69     2;\r\n       58    99     2]\r\nassert(isequal(findRowPattern(p,v),out))","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":15013,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":12,"test_suite_updated_at":"2013-07-23T17:11:41.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-07-23T16:51:20.000Z","updated_at":"2026-05-27T04:01:28.000Z","published_at":"2013-07-23T17:11:41.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSo I think this may be a hard one, may be it was only hard for me! So here is what it is, I will give a matrix, with some number of rows by 5 columns, to make it simpler I will not repeat any number in a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esingle row\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, so all numbers in a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esingle rows\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e are unique. So you need to find up to 2, 3 or 4 numbers that are found in a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esingle row\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e that repeat in\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eother rows\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and the amount of times they repeat in a given matrix. To make it a little better, you only need to display the rows/numbers that repeat at lease twice or more. Examples are the best way to show what to do:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[          %p is the given matrix\\n          p = [1 2 3 4 5;\\n               2 3 4 5 1;\\n               3 4 6 7 8;\\n               1 2 4 3 5;\\n               4 6 3 2 1];\\n          %v is the amount of elements per row that are being checked to see if they repeat in another row\\n          v = 4;\\n        %Output:\\n        out =\\n\\n             1     2     3     4     4\\n             1     2     3     5     3\\n             1     2     4     5     3\\n             1     3     4     5     3\\n             2     3     4     5     3\\n      %So the first to the fourth column are the four number that were found to repeat in rows of matrix p (any and all combination that can appear in the rows of the matrix p). The fifth column is the amount of times the that particular set of matrix out(?,1:4) appear in matrix p. for for row one in matrix out, 1,2,3 and 4 in any combination appear 4 time in the rows of matrix p.\\n\\n      So more examples...\\n\\n      p = [1 2 3 4 9;\\n           2 3 1 4 8]\\n      v = 4;\\n\\n      out =\\n           1     2     3     4     2\\n    %so any combination of 1,2,3 and 4 appear 2 times in matrix p\\n\\n    and another:\\n\\n    p = [1 2 3 4 9;\\n         2 3 1 4 8;\\n         3 4 2 7 1]\\n    v = 3;\\n\\n    out =\\n         1     2     3     3\\n         1     2     4     3\\n         1     3     4     3\\n         2     3     4     3\\n    %so this uses any combo of 3 values, so in this case out(?,1:3) are the number combos that can be found in matrix p, and the out(?,4) is the amount of times that those combos are found\\n\\n    last example:\\n\\n    p = [1 2 3 4 9;\\n         2 3 1 4 8]\\n    v = 2;\\n\\n    out =\\n         1     2     2\\n         1     3     2\\n         1     4     2\\n         2     3     2\\n         2     4     2\\n         3     4     2\\n    %so in this case only combos of out(?,1:2) are found and out(?,3) is the amounts of times that combo of out(?,1:2) is found in matrix p. so row four of matrix out (so out(4,:)...) shows that the combo of 2 and 3 are found 2 times in matrix p.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNOTE: only display any row number combos that occur at least 2 times or more!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI hope I gave enough instructions and explanation, if not please let me know and I will add to it! Thanks and good luck!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2448,"title":"Back to the future","description":"See test suite\r\n\r\nSee also:\r\nhttp://www.mathworks.com/matlabcentral/cody/problems/2449-back-to-the-future-ii","description_html":"\u003cp\u003eSee test suite\u003c/p\u003e\u003cp\u003eSee also: \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/2449-back-to-the-future-ii\"\u003ehttp://www.mathworks.com/matlabcentral/cody/problems/2449-back-to-the-future-ii\u003c/a\u003e\u003c/p\u003e","function_template":"function y = your_solution(x)\r\n  y = x;\r\nend","test_suite":"%%\r\n% Create necessary files\r\nfid = fopen('myfunction.m', 'w');fprintf(fid, 'function [a, b]=myfunction(), a=rand; b=@()1;');fclose(fid);\r\nrehash;\r\n\r\n% Execute my function 10 times\r\ny=cell(10,1);\r\nfor iter=1:10\r\n[x y{iter}]=myfunction();\r\nend\r\n\r\n% Ouch! I have overwritten the value of \"x\"\r\n% Your task is to recover the value of the 3rd \"x\"\r\n\r\n% Now I am downloading my secret solver\r\nurlwrite('https://github.com/masdeseiscaracteres/files4codygame/blob/master/my_secret_solver.p?raw=true','my_secret_solver.p');\r\nrehash;\r\n\r\n% I compare your solution with my secret solver to see how you have done\r\nif my_secret_solver~=your_solution, fail{-1}, end","published":true,"deleted":false,"likes_count":2,"comments_count":4,"created_by":5925,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":15,"test_suite_updated_at":"2014-07-18T19:16:07.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-07-18T18:11:07.000Z","updated_at":"2026-05-28T03:13:43.000Z","published_at":"2014-07-18T19:12:51.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSee test suite\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSee also:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/2449-back-to-the-future-ii\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehttp://www.mathworks.com/matlabcentral/cody/problems/2449-back-to-the-future-ii\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44243,"title":"Ternary Conditional Operator","description":"Returns one of two expressions depending on a condition.\r\n\r\n  (test) : (expression1) : (expression2)\r\n\r\n*test:* \r\nAny Boolean expression.\r\n\r\n*expression1:* \r\nA function handle called if test is true. \r\n\r\n*expression2:* \r\nA function handle called if test is false. \r\n\r\n*Example*\r\n\r\n  \u003e\u003e a = (2 \u003e 1) : (@() 1) : (@() 2)\r\n     a =\r\n          1\r\n  \u003e\u003e a = (1 \u003e 2) : (@() 1) : (@() 2)\r\n     a =\r\n          2\r\n\r\nThe |colon.m| you submitted will be moved to the class folder |@function_handle|:\r\n  \r\n  mkdir @function_handle\r\n  movefile submission/colon.m @function_handle","description_html":"\u003cp\u003eReturns one of two expressions depending on a condition.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e(test) : (expression1) : (expression2)\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003etest:\u003c/b\u003e \r\nAny Boolean expression.\u003c/p\u003e\u003cp\u003e\u003cb\u003eexpression1:\u003c/b\u003e \r\nA function handle called if test is true.\u003c/p\u003e\u003cp\u003e\u003cb\u003eexpression2:\u003c/b\u003e \r\nA function handle called if test is false.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample\u003c/b\u003e\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e\u0026gt;\u0026gt; a = (2 \u0026gt; 1) : (@() 1) : (@() 2)\r\n   a =\r\n        1\r\n\u0026gt;\u0026gt; a = (1 \u0026gt; 2) : (@() 1) : (@() 2)\r\n   a =\r\n        2\r\n\u003c/pre\u003e\u003cp\u003eThe \u003ctt\u003ecolon.m\u003c/tt\u003e you submitted will be moved to the class folder \u003ctt\u003e@function_handle\u003c/tt\u003e:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003emkdir @function_handle\r\nmovefile submission/colon.m @function_handle\r\n\u003c/pre\u003e","function_template":"function y = colon1(test, expr1, expr2)\r\n  y = expr1;\r\nend","test_suite":"%%\r\nmkdir @function_handle\r\nmovefile colon1.m @function_handle/colon.m\r\n\r\n%%\r\nassert(isequal((2 \u003e 1) : (@() 1) : (@() 2), 1))\r\n\r\n%%\r\nassert(isequal((1 \u003e 2) : (@() 1) : (@() 2), 2))\r\n\r\n%%\r\nfib = @(f, n) (n \u003e 2) : (@() f(f, n - 1) + f(f, n - 2)) : (@() 1);\r\nassert(fib(fib, 20) == 6765)\r\n\r\n%%\r\nx = magic(3);\r\n[m,I] = (x(1) \u003e 0) : (@() max(x)) : (@() min(x)) \r\nassert(isequal(m, [8 9 7]) \u0026\u0026 isequal(I, [1 3 2]))\r\n","published":true,"deleted":false,"likes_count":6,"comments_count":5,"created_by":1434,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":13,"test_suite_updated_at":"2020-04-22T14:42:35.000Z","rescore_all_solutions":false,"group_id":52,"created_at":"2017-06-27T15:11:40.000Z","updated_at":"2026-05-29T03:45:36.000Z","published_at":"2017-06-27T15:11:40.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eReturns one of two expressions depending on a condition.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[(test) : (expression1) : (expression2)]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etest:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Any Boolean expression.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eexpression1:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e A function handle called if test is true.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eexpression2:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e A function handle called if test is false.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[\u003e\u003e a = (2 \u003e 1) : (@() 1) : (@() 2)\\n   a =\\n        1\\n\u003e\u003e a = (1 \u003e 2) : (@() 1) : (@() 2)\\n   a =\\n        2]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecolon.m\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e you submitted will be moved to the class folder\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e@function_handle\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[mkdir @function_handle\\nmovefile submission/colon.m @function_handle]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":47453,"title":"Slitherlink I: Trivial","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 540.65px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 270.333px; transform-origin: 407px 270.333px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.1333px 7.91667px; transform-origin: 80.1333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Slitherlink\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137.7px 7.91667px; transform-origin: 137.7px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e pencil puzzles. An essential starter guide is \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink Techniques\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55.2167px 7.91667px; transform-origin: 55.2167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 78.5333px 7.91667px; transform-origin: 78.5333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eThis Slitherlink I: Trivial\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 258.667px 7.91667px; transform-origin: 258.667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is for the cases of s with a 4 or a pair of adjacent 3s forming a unique solution loop.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 368.367px 7.91667px; transform-origin: 368.367px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink II: Gimmes will use the Starting Techniques from Slitherlink Techniques. Adjacent 3s  yields R 3 R 3 R board values if trivial did not already solve. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4333px 7.91667px; transform-origin: 19.4333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 333.35px 7.91667px; transform-origin: 333.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2667px 7.91667px; transform-origin: 25.2667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 334.583px 7.91667px; transform-origin: 334.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.1167px 7.91667px; transform-origin: 31.1167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 7.91667px; transform-origin: 1.95px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 358.05px 7.91667px; transform-origin: 358.05px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 365.75px 7.91667px; transform-origin: 365.75px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 250.25px 7.91667px; transform-origin: 250.25px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 177.1px 7.91667px; transform-origin: 177.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 4 8 12 16 20]                       %to path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 132.917px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 66.4667px; text-align: left; transform-origin: 384px 66.4667px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 66.9px 7.91667px; transform-origin: 66.9px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Challenges:\u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 241px;height: 127px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"241\" height=\"127\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 373.267px 7.91667px; transform-origin: 373.267px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check (large)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n [nr,nc]=size(s);\r\n [nrc,ncc]=size(c);\r\n% p=p'  as a 1-2 seg is also a 2-1 seg. rows/cols are path nodes and c indices\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% sum of p starts as 2 for corners, 3 for edges, and 4 for mid-points\r\n%The display tool, show_pfigs, makes segments Red for p(i,j)=5, Black if 0, grey if 1\r\n% Final nodes of p are either 5 or 0 with sum(p) being 0 or 10\r\n% Nodes in a path have an entry/exit path thus a sum of 10\r\n\r\np=trivial_solve(p,bsegs,s);\r\n\r\n[sv,valid]=pcheck(s,p,bsegs);\r\n\r\n  %show_pfig(s,p,c,emap,pmap,1)\r\n  fprintf('sv trivial solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n')\r\n \r\nend % sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n\r\n\r\n\r\nfunction p=trivial_solve(p,bsegs,s)\r\n if nnz(s==4)\r\n  p=p*0;\r\n  %p(?)=5\r\n  p=p+p';\r\n  return\r\n end\r\n \r\n ptr3=find(s==3); % adjacent 3s  check if box around solves\r\n %p(?)=5\r\n p=p+p'; \r\nend %p=trivial_solve(p,bsegs,s)\r\n\r\n\r\n\r\nfunction [v,valid]=pcheck(s,p,bsegs)\r\n%creates the sv vector and tells valid status\r\n valid=0;\r\n v=[];\r\n if nnz(sum(p,2)==10)\u003c4,return;end\r\n  \r\n sv=s(:);\r\n schk=sv*0; % will add seg walls to schk and compare to sv using bsegs while ignore sv==5\r\n p(p\u003c5)=0; % clear non-segments\r\n v=find(sum(p,2)==10,1,'first'); %first index,  indices of corners; valid if v(1)=v(end)\r\n vnext=find(p(v,:)==5,1,'first');\r\n p(v,vnext)=0;\r\n p(vnext,v)=0;\r\n v=[v vnext];\r\n while v(1)~=v(end)\r\n  vnext=find(p(v(end),:)==5);\r\n  if isempty(vnext),return;end % No connector - no solution\r\n  p(v(end),vnext)=0;\r\n  p(vnext,v(end))=0;\r\n  v=[v vnext];\r\n end\r\n % v(1)==v(end)  [1 2 4 3 1]\r\n vsegs=sort([v(1:end-1);v(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(sv) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % bsegs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(sv==5)=5;\r\n if isequal(schk,sv)\r\n  valid=1;\r\n end\r\n \r\nend % pcheck\r\n\r\n\r\n\r\nfunction show_pfig(s,p,c,emap,pmap,fignum)\r\n%Create display of current solution status using p\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% emap/pmap contain info on what segments are part of the puzzle p(1,end) is not a real segment\r\n [nr,nc]=size(s);\r\n \r\n figure(fignum);plot([0,nc,nc,0,0],[0,0,nr,nr,0],'color',[192 192 192]/255,'LineWidth',5);hold on\r\n axis tight\r\n set (gca,'Ydir','reverse')\r\n set (gca,'Xtick',[]);\r\n set (gca,'Ytick',[]);\r\n for i=0:nr\r\n  plot([0,nc],[i,i],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n for i=0:nc\r\n  plot([i,i],[0,nr],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n\r\n for i=1:nr\r\n  for j=1:nc\r\n   txt=num2str(s(i,j));\r\n   t=text(j-.6,i-.5,txt); % reverse i,j  j is y-row, i is col  graph [col,row]\r\n   t.FontSize=20; %https://www.mathworks.com/help/matlab/creating_plots/add-text-to-specific-points-on-graph.html\r\n  end\r\n end\r\n \r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if pv==0\r\n    plot([b,d],[a,c],'k','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n \r\n %Draw RED on top\r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if b==d\r\n    if a\u003cc\r\n     a=max(0,a-.05);\r\n     c=min(nr,c+.05);\r\n    else % a\u003ec\r\n     a=min(nr,a+.05);\r\n     c=max(0,c-.05);\r\n    end\r\n   else %a==c\r\n    if b\u003cd\r\n     b=max(0,b-.05);\r\n     d=min(nc,d+.05);\r\n    else % b\u003ed\r\n     b=min(nc,b+.05);\r\n     d=max(0,d-.05);\r\n    end\r\n   end\r\n   if pv==5\r\n    plot([b,d],[a,c],'r','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n hold off \r\nend %show_pfig(s,p,c,emap,pmap,fignum)\r\n\r\nfunction [c,bsegs,p,pmap]=create_p(nr,nc)\r\n%This is provided by the calling routine.  Included here for reference info\r\n%p is matrix of connections from r2c,c2r\r\n%0 is no connect, 1 is possible, 5 is connected\r\n%p row sums to 0 or 10\r\n%p_row_sum of 1 evolves to 0\r\n%p_row_sum of 6 evolves to 10\r\n%p_row_sum 1:4,6:8 has multiple options\r\n% transpose values always match\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n %[nr*nc,8]  four C segments about each s index\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc];\r\n p=p+p';\r\n \r\n %c\r\n %bsegs\r\n %p\r\n \r\n%1 4 2x1   1 4 7  1 5 9\r\n% A         A C    A D\r\n%2 5       2 5 8  2 6 10\r\n% B         B D    B E\r\n%3 6       3 6 9  3 7 11\r\n%                  C F\r\n%                 4 8 12\r\nend %[c,bsegs,p,pmap]=create_p(nr,nc)\r\n","test_suite":"%%\r\ns = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns = [5 5 5;5 4 5;5 5 5];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns = [5 5;4 5;5 5];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns = [3 3];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns = [3;3];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns =[0 5 5;5 3 5;5 3 5;5 5 0];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns =[5 1 1 5;1 3 3 1;5 1 1 5];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\n% anti-hack case\r\ns=zeros(randi(4,1,2)+2)+5;\r\ns(randi(prod(size(s)))) = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":2,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-12T00:38:37.000Z","updated_at":"2026-05-30T19:54:00.000Z","published_at":"2020-11-12T23:27:09.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Slitherlink\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e pencil puzzles. An essential starter guide is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink Techniques\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis Slitherlink I: Trivial\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is for the cases of s with a 4 or a pair of adjacent 3s forming a unique solution loop.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink II: Gimmes will use the Starting Techniques from Slitherlink Techniques. Adjacent 3s  yields R 3 R 3 R board values if trivial did not already solve. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\\n% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\\n% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\\n% 4 8 12 16 20]                       %to path]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"127\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"241\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check (large)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"re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function","description":"Given a function handle f, generate a function that evaluates the derivative of f\r\n\r\n\r\nExamples:\r\n    \r\n   f = @sin;\r\n   df = Derivative(f);\r\n   df([0 pi])\r\n   ans =\r\n        1    -1\r\n\r\nDerivative of sin is cos, cos([0 pi]) = [1 -1]\r\n\r\nHint(Added 2015/9/17):\r\n\u003chttps://en.wikipedia.org/wiki/Numerical_differentiation\u003e","description_html":"\u003cp\u003eGiven a function handle f, generate a function that evaluates the derivative of f\u003c/p\u003e\u003cp\u003eExamples:\u003c/p\u003e\u003cpre\u003e   f = @sin;\r\n   df = Derivative(f);\r\n   df([0 pi])\r\n   ans =\r\n        1    -1\u003c/pre\u003e\u003cp\u003eDerivative of sin is cos, cos([0 pi]) = [1 -1]\u003c/p\u003e\u003cp\u003eHint(Added 2015/9/17): \u003ca href = \"https://en.wikipedia.org/wiki/Numerical_differentiation\"\u003ehttps://en.wikipedia.org/wiki/Numerical_differentiation\u003c/a\u003e\u003c/p\u003e","function_template":"function df = Derivative(f)\r\n  df = f;\r\nend","test_suite":"%%\r\nf = @sin;\r\ndf = Derivative(f);\r\nx = 10*rand(10000,1);\r\ndy = cos(x);\r\nassert(max(abs((dy-df(x))./dy))\u003c1e-13)\r\n%% \r\nf = @log;\r\ndf = Derivative(f);\r\nx = exp(500*rand(10000,1));\r\ndy = 1./x;\r\nassert(max(abs((dy-df(x))./dy))\u003c1e-13)\r\n%%\r\nf = @exp;\r\ndf = Derivative(f);\r\nx = 50*(rand(10000,1)-0.5);\r\ndy = exp(x);\r\nassert(max(abs((dy-df(x))./dy))\u003c1e-13)\r\n%%\r\nt = 10*rand;\r\nf = @(x)1./(x+t);\r\ndf = Derivative(f);\r\nx = 100*(rand(10000,1)-0.5); x(x==-t) = 1;\r\ndy = -1./(x+t).^2;\r\nassert(max(abs((dy-df(x))./dy))\u003c1e-13)\r\n%%\r\nt = 10*rand-5;\r\nf = @(x)atan(t*x);\r\ndf = Derivative(f);\r\nx = 100*(rand(10000,1)-0.5);\r\ndy = t./(t^2*x.^2 + 1);\r\nassert(max(abs((dy-df(x))./dy))\u003c1e-13)\r\n%%\r\nfor t = 2:6\r\n    f = @(x)t.^x./(sin(x).^t+cos(x).^t);\r\n    df = Derivative(f);\r\n    x = 3+rand(10000,1);\r\n    dy = (t.^x.*log(t))./(cos(x).^t+sin(x).^t)-...\r\n        (t.^x.*(t.*cos(x).*sin(x).^(t-1)-...\r\n        t.*cos(x).^(t-1).*sin(x)))./(cos(x).^t+sin(x).^t).^2;\r\n    assert(max(abs((dy-df(x))./dy))\u003c1e-13)\r\nend","published":true,"deleted":false,"likes_count":3,"comments_count":0,"created_by":1434,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":12,"test_suite_updated_at":"2015-09-08T18:13:33.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2015-09-07T05:46:00.000Z","updated_at":"2026-05-28T13:39:34.000Z","published_at":"2015-09-07T05:47:35.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a function handle f, generate a function that evaluates the derivative of f\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExamples:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[   f = @sin;\\n   df = Derivative(f);\\n   df([0 pi])\\n   ans =\\n        1    -1]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDerivative of sin is cos, cos([0 pi]) = [1 -1]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHint(Added 2015/9/17):\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Numerical_differentiation\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://en.wikipedia.org/wiki/Numerical_differentiation\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2837,"title":"Modify subscripts","description":"MATLAB supports object-oriented programming. Let's take an advantage of it in cody.\r\n\r\nThis problem starts \u003chttp://uk.mathworks.com/matlabcentral/cody/problems/2836-class-ics-modify-subscripts-easy here\u003e.\r\n\r\nLet's take things a bit more seriously. There are _subsasgn_ and _subsref_ to overload:\r\n\r\n  \u003e\u003e A = sub_double([ 1 4 7\r\n                      2 5 8\r\n                      3 6 9]);\r\n  \u003e\u003e A([1 3],[3 1])\r\n  \r\n  ans = \r\n\r\n    3x3 sub_double:\r\n\r\n    double data:    \r\n       7    3\r\n  \r\n  \u003e\u003e A([1 2 3],[3 2 1]) = 0\r\n  \r\n  A = \r\n\r\n    3x3 sub_double:\r\n\r\n    double data:\r\n       1     2     0\r\n       4     0     6\r\n       0     8     9\r\n\r\n  \u003e\u003e A(:)'\r\n  \r\n  ans = \r\n  \r\n    1x9 sub_double:\r\n  \r\n    double data:\r\n       1     4     0     2     0     8     0     6     9\r\n  \r\n  \u003e\u003e A(:,:)\r\n  \r\n  ans = \r\n  \r\n    1x3 sub_double:\r\n  \r\n    double data:\r\n       1     0     9\r\n\r\n\r\n","description_html":"\u003cp\u003eMATLAB supports object-oriented programming. Let's take an advantage of it in cody.\u003c/p\u003e\u003cp\u003eThis problem starts \u003ca href = \"http://uk.mathworks.com/matlabcentral/cody/problems/2836-class-ics-modify-subscripts-easy\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\u003cp\u003eLet's take things a bit more seriously. There are \u003ci\u003esubsasgn\u003c/i\u003e and \u003ci\u003esubsref\u003c/i\u003e to overload:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e\u0026gt;\u0026gt; A = sub_double([ 1 4 7\r\n                    2 5 8\r\n                    3 6 9]);\r\n\u0026gt;\u0026gt; A([1 3],[3 1])\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eans = \r\n\u003c/pre\u003e\u003cpre\u003e    3x3 sub_double:\u003c/pre\u003e\u003cpre\u003e    double data:    \r\n       7    3\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003e\u0026gt;\u0026gt; A([1 2 3],[3 2 1]) = 0\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eA = \r\n\u003c/pre\u003e\u003cpre\u003e    3x3 sub_double:\u003c/pre\u003e\u003cpre\u003e    double data:\r\n       1     2     0\r\n       4     0     6\r\n       0     8     9\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003e\u0026gt;\u0026gt; A(:)'\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eans = \r\n\u003c/pre\u003e\u003cpre\u003e    1x9 sub_double:\u003c/pre\u003e\u003cpre\u003e    double data:\r\n       1     4     0     2     0     8     0     6     9\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003e\u0026gt;\u0026gt; A(:,:)\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eans = \r\n\u003c/pre\u003e\u003cpre\u003e    1x3 sub_double:\u003c/pre\u003e\u003cpre\u003e    double data:\r\n       1     0     9\u003c/pre\u003e","function_template":"classdef sub_double \u003c double\r\n  methods\r\n    function obj = sub_double(in)\r\n      obj = obj@double(in);\r\n    end\r\n    function out = subsref(obj,S)\r\n        \r\n    end\r\n  end \r\nend","test_suite":"%%\r\n% first some basic tests\r\nA = 1;\r\nB = sub_double(A);\r\ny = B(1);\r\ny_correct = 1;\r\nassert(isequal(y,y_correct))\r\n%%\r\nA = [1 2 3\r\n     4 5 6\r\n     7 8 9];\r\nB = sub_double(A);\r\ny = B([1 3],[3 1]);\r\ny_correct = [3 7];\r\nassert(isequal(y,y_correct))\r\n%%\r\nA = [1 2 3\r\n     4 5 6\r\n     7 8 9];\r\nB = sub_double(A);\r\ny = B([1 3],[3 end]);\r\ny_correct = [3 9];\r\nassert(isequal(y,double(y_correct)))\r\n%%\r\nA = [1 2 3\r\n     4 5 6\r\n     7 8 9];\r\nB = sub_double(A);\r\ny = B(1,[3 1]);\r\ny_correct = [3 1];\r\nassert(isequal(y,y_correct))\r\n%%\r\nA = [1 2 3\r\n     4 5 6\r\n     7 8 9];\r\nB = sub_double(A);\r\ny = B(2:end);\r\ny_correct = [4 7 2 5 8 3 6 9];\r\nassert(isequal(y,y_correct))\r\n%%\r\nA = magic(5);\r\nB = sub_double(A);\r\ny = B([1 5 4 3 2],[3 4 5 1 2]);\r\ny_correct = [1:5];\r\nassert(isequal(y,y_correct))\r\n%%\r\nA = magic(7);\r\nB = sub_double(A);\r\nrows = randi(7,1,10);\r\ncols = randi(7,1,10);\r\ny = B(rows,cols);\r\ny_correct = arrayfun(@(R,C)A(R,C),rows,cols);\r\nassert(isequal(y,y_correct))\r\n%%\r\nA = 1;\r\nB = sub_double(A);\r\nB(1) = 3;\r\nA = 3;\r\nassert(isequal(A,B))\r\n%%\r\nA = [1 2 3\r\n     4 5 6\r\n     7 8 9];\r\nB = sub_double(A);\r\nB([1 2 3],[3 2 1]) = 0;\r\nA = [1 2 0\r\n     4 0 6\r\n     0 8 9];\r\nassert(isequal(A,B))\r\n%%\r\nA = [1 2 3\r\n     4 5 6\r\n     7 8 9];\r\nB = sub_double(A);\r\nB([1 3],[3 end]) = [7 7];\r\nA = [1 2 7\r\n     4 5 6\r\n     7 8 7];\r\nassert(isequal(double(A),double(B)))\r\n%%\r\nA = [1 2 3\r\n     4 5 6\r\n     7 8 9];\r\nB = sub_double(A);\r\ny = B(:,:);\r\ny_correct = [1 5 9];\r\nassert(isequal(y,y_correct))\r\n%%\r\nA = [1 2 3\r\n     4 5 6\r\n     7 8 9];\r\nB = sub_double(A);\r\nB(2:end) = [2:9];\r\nA = A';\r\nassert(isequal(B,A))\r\n%%\r\nA = magic(5);\r\nB = sub_double(A);\r\nB([1 5 4 3 2],[3 4 5 1 2])=0;\r\nassert(isequal(sum(~[B B']),ones(1,10)))\r\n%%\r\nA = magic(7);\r\nB = sub_double(A);\r\nrows = randi(7,1,10);\r\ncols = randi(7,1,10);\r\nvals = randi(7,1,10);\r\nB(rows,cols) = vals;\r\nfor k = 1:10\r\n  A(rows(k),cols(k))=vals(k);\r\nend\r\nassert(isequal(A,B))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":14358,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":10,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2015-01-18T00:53:33.000Z","updated_at":"2026-05-28T04:22:20.000Z","published_at":"2015-01-18T01:36:53.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMATLAB supports object-oriented programming. Let's take an advantage of it in cody.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem starts\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://uk.mathworks.com/matlabcentral/cody/problems/2836-class-ics-modify-subscripts-easy\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehere\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eLet's take things a bit more seriously. There are\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esubsasgn\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esubsref\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to overload:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[\u003e\u003e A = sub_double([ 1 4 7\\n                    2 5 8\\n                    3 6 9]);\\n\u003e\u003e A([1 3],[3 1])\\n\\nans = \\n\\n    3x3 sub_double:\\n\\n    double data:    \\n       7    3\\n\\n\u003e\u003e A([1 2 3],[3 2 1]) = 0\\n\\nA = \\n\\n    3x3 sub_double:\\n\\n    double data:\\n       1     2     0\\n       4     0     6\\n       0     8     9\\n\\n\u003e\u003e A(:)'\\n\\nans = \\n\\n    1x9 sub_double:\\n\\n    double data:\\n       1     4     0     2     0     8     0     6     9\\n\\n\u003e\u003e A(:,:)\\n\\nans = \\n\\n    1x3 sub_double:\\n\\n    double data:\\n       1     0     9]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":46938,"title":"Numerical computation of the optimal shooting angle of a catapult","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 879.667px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 406.5px 439.833px; transform-origin: 406.5px 439.833px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 64.3333px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 32.1667px; text-align: left; transform-origin: 383.5px 32.1667px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eConsider a capapult that fires a projects into the air with an initial velocity\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"56.5\" height=\"20\" style=\"width: 56.5px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e. The free-flying projectile is subjected to air friction and a gravitional force. Given a desired target \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"109\" height=\"21\" style=\"width: 109px; height: 21px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e and an initial velocity \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"56.5\" height=\"20\" style=\"width: 56.5px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e, find the optimal shooting angle \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"16\" height=\"18.5\" style=\"width: 16px; height: 18.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eof the catapult that minimizes the distance between the target and the trajectory of the fired projectile. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 21.25px; text-align: left; transform-origin: 383.5px 21.25px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003etip 1:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e Consider the states \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"14.5\" height=\"20\" style=\"width: 14.5px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"14.5\" height=\"20\" style=\"width: 14.5px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e as the x- and y-position of the projectile, and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"14.5\" height=\"20\" style=\"width: 14.5px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"14.5\" height=\"20\" style=\"width: 14.5px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e as the x- and y-velocity. Then, the trajectory of the projectile can be found by solving the following ordinary differential equation (ODE):\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 22px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 11px; text-align: left; transform-origin: 383.5px 11px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e        \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"45\" height=\"22\" style=\"width: 45px; height: 22px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e,     \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 22px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 11px; text-align: left; transform-origin: 383.5px 11px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e        \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"45.5\" height=\"22\" style=\"width: 45.5px; height: 22px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e      \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 22px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 11px; text-align: left; transform-origin: 383.5px 11px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; 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height: 19.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e, and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"47.5\" height=\"17.5\" style=\"width: 47.5px; height: 17.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eis the friction coefficient between the air and the projectile. Use the ode45.m function to compute the trajectory of the projectile with initial conditions \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" 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Plotting \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"14.5\" height=\"20\" style=\"width: 14.5px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e vs. \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"14.5\" height=\"20\" style=\"width: 14.5px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e will result in the x-y trajectory of the projectile, as shown in the figure below.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.8333px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.9167px; text-align: left; transform-origin: 383.5px 10.9167px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003etip 2: \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eUse the following update law, to incrementally update the shooting angle \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAB4AAAAoCAYAAADpE0oSAAAC8klEQVRYR+3WTagWdRQG8J8UWUS1Ma021UItSUOMPoTIRUptlESpLDUoFQr60EpRzEySKKiUNMSPiErpQyylTVnaIjFaZFBUm9CFSpJkiJqIGAfOXF7mzvveudzh3s2d3cz8/+c5H895zhligJ4hA4RrELjfMj+Y6p5SfTHG4U5cgV+wF6d6ulj+XzfVce4uvI3v8AZuwRYcwAL81RvwOsBx5gG8iS/xYkYYEa/DbDyLtbhQF7wO8D14H38myOEW48vxCj7DE/i3KeCR+ABjEvSLkuECeB9m4VATwJfjdTyJT7OO/7QB/hkP4o8mgKdltGEr6liO9hK8hufQGPBwbMRU7MDjKEd7Gd7KTERLPYIjfY14OrankYUJULZ5Nbbi3mR7ZKXsXFs/qlgdtX0Hj+E3zMSvFRZuwicYi/VYhP/6EnEoU7RHweincLLCYJShqHu7rPQq4qjVh3U9T6dCYL7pxZ1ui0ArUyPK+/F9hcGrsAkzsBtzcLQvwK0GOzH1dnyOa7O2we6Qy2D6lbgZo7MjjlU5VCZXK1M3ZI+eKV28CCsQqlUmXwCHym3G7wh+HK8DHF5+jFvxElZVXLoR23AHlqa6nW85V7A9MrISrf+6jpUj7gk4oo3ptBp7KoZGGJ6MrxBaEOJT+ZSBr8NHmNQm4tuy1cLY3JzNrYbD3rIkXUftLgMPzdQ9nax9BqfT8ghE3e/O2kVJyvO3IGfcid6OTSWIFs9+/IBz8VKlXEVUJ1omzjXpUIhLKNS3bYZ+IT7v5fkg6wuI91C/LkergOPbeCzJvSouXJ/1CqXqtF+F+Lybqf4JD2dpug2POhtIXV0oxGdidkOkOCZc5VbSJPCwnN/3paexPMzrD+AJ2IU1CCeexxR8XUdA6qa16lysuKFoD+V4DAGJrTQ649IUkq60N5XqYhsZlaLyN17FfDyKG7ATBwuPmwIO1sc28iMW42x2QgyPSPvLqXQd26kv6a59t6mIawM2nepB4B4zMGA1/h/Q3qEpP5IZBgAAAABJRU5ErkJggg==\" width=\"15\" height=\"20\" style=\"width: 15px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.8333px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.9167px; text-align: left; transform-origin: 383.5px 10.9167px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e        \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAATMAAAAoCAYAAACSPh2yAAAR5UlEQVR4Xu3dB7A1S1UF4IUZIwgGzFYRFDFizmIExEgyoJhFzDlHzBkDKiKYURCzICbAgKAIiqKoZc4551Tfq97PZpg5M+eeOeem6aq//vf+O6Fnd/faa6+9u+9NsrXNApsFNgtcAQvc5Ap8w/YJmwU2C2wWyAZm2yTYLLBZ4EpYYAOzKzGM20dsFtgssIHZNgc2C2wWuBIW2MDsSgzj9hGX3AIvmuQ1k/xUkv+5wN/yskleOsmTkvzvRevnBmYXbUS2/lw3C9wmyYcl+dwkf37BP/45krx7kudO8q1J/usi9XdfMHuuJK+W5PWTvFCSX03yuCT/fJE+6pL25aZJXivJ6yZ5ziS/mOSJSf79kn7P1u15C9wuyUcn+fQ9gezF2xp85ST/mOQnkvzmidgSzLh3kue7aIC2FMxc9wZJviLJ45N8cZI7JPmmJE9L8kF7Dsb8MF+fK3i7d0nymUkeluTrk7xjkq9tk+Xjr5CzeLHGQN4+yRcm+eok/73yUD9vko9L8iFJPqXZ9MKFREnY4ivbeFtTSxoC8YlJ7pzkE5I8OcnnJ3nPJB+c5DtPBGgcLyb5Q0l+cknHT3HNEjBzzTsn+bIkP5ykFhfDfk2S+yT5yCQPPJEhT2GXU70D0zUJP69Nji9t1P2lknx7kjs2+37/qTp0xPcAGQvAXMI8/y7JvZI8c+V3YjvfleTVk/xokvdI8tcrv+PQx/l+jIxNvmBhuAb8zI83TfLejVTox1sneWySn22g9nuHdm7h/ezMIVn7p3rnzq4tAbM3S/LNSX6nLaw/7p74aUk+O8kjk7x/kr9faIjtstxQ4/duSb4uyfc23eQfmmF4vi9vjNffvPF/XHKjAWYM3re8TJKHJ/mIJD+28nddBmb2Kkm+Ksn9FoL5CyT5osY2PzDJN3bEoQdvjP4HVrbn1OPM349KcrMkn3UEhr33Z8yBGXGS0Hf7CYZQYPZzTRj8/b17cH1veKPGvljg7k0jK2v0YHYKRwFosENhC/b0b0cYlg9tz/eOF2kL8vs6GxzhlRfykVjZZyTByq2fuTC7WBwwe0RzCH/bfVkPZtge53eqRj9/UBIA+2uneukudJ36We8NxozovgKzXz5SyHDe9jnW+1+i08aEDrSdXujvwewUoRJg/ZnWJ972X4/14QPm+dPXEMxePsl3NI10CSutyEj5BklnKDn0YCaR8DlHHrv+8SU10c2B6Llqk7uYGcqKlWljRnyeFu+b/BuYLZ9BbP7hLZmCyQ5ZmScVc/GzDcyW2/YyXPkOLXspI/jbMx2+eXMw95hgZW7Hqn8wya3ac08JZt7/SS0Lf+4y0xSYSf0+OAnD03PeL0lPbX1Ezx6UZxBa/+QyzKZz7mPvSSVNJFSG5Re3aIzlbZtDuX9LwR+r6+fFzDCUJyz4KPYgehP1abe3TvJXTW9SwPnUkWeYn2QSCYax8hZZ5NdoutUrJMGWf72J6RJdar6GTIMDdw8g8nMCuHvVid21lSs8qrGUPxzpk5AR2OjXksVvTX1be867JvHsYaux8++SSbLhp2ww4gFJ7pnkN0754uG7psBMqcD3tIun4nDZFZPxrVqWE3sbAt55fttFfHeJpkJLbUqwfaUk353kVVuJxsccSccqG50azNRHKSMA0rJwu5p5ZoH+Upf5Y8e3a3qNBV/PoEOppPdv5uMvTGQzK4uMIX9AywwKmYATUb5vFXUoTVICoQ7QtXQvdZbGRuX+LZNgUMLBKVmmwjJAPJfUwcoe0rK/uzTp92oJOn0GsjK5p2zFDDmbJWFz3zfjKOxm11dM8k9JOHBz40v2LUkaAzNamfqf+zZPZYDGxL1TL7i1B6j3eoc8+40XLMh6vlDgW5oD+PEkJuKfjrxc+r1qjz651RId0se5e08JZi/cMnlKNN4myc/PdK7GaWhngCSL9ozGYrEmACeqkGzYVZrhvRIr5nkvwltQFtLrNRlADdd/Nlas2r0P//+iJTGUVihctZb0lTTj/32fYta+FQHARufCwbdsUREAdO1YxtA7gao/3rnEnnNzYd+fV6ShdMvcXtrYgiNAnEhVj27MVu0d1rp3ZnYMzGQoDHRlMqdCHPSyxMhTZ1GWGmzXdecBZlUTpF8WylQ2y+CaHNreg3oG45wKzACQCQwAluqslWQaliT4TAseeAGgav0CH9MblW7IDGJlQ0dRYSAdSKg5jDZ6aUXBuBorIFKtxP03bHVkw8xiLXwZwF3hYN8PzwZSY6ynB9fzqigQ8iuzgQVzAF12YgesU4lOXzPXs9Gpb56c3mNgtu8in/JCZ1hTV/oWtpa1XDrgjEHDmWLGaxrrVGBWmTmLfimY0WvVVUmWcKw8eG3Gxlowvb72kV0KAMfArNcjx7J/Nf/H+teDGTAaZn7nnl1gZgeNouipJmTF8DDNpc3uEYz01FsL65uF+kvmdgGZOUcv5rSVp/R1l6IWNYl/ufTjXTcEsz5DCaRsmxjTNHqPsCtc2qcvV/3a3ma7FnLv3deaoH3S4RA7H5L672sW6YEv2bzyXHW+Uxos7LdoHRfKCKt2VZ0vBbMxdlzA/pSmQfUZx1OBWZ+hHAPNGsM+OhKaYTunbvuAWV/uRWfEfDkiOGR8sd0/akC2d93aEMz6BbcrQ0kEVfBIAxI2VI2JweYpCbwWkEwobeEYTTaKpxd+2Ne49raYtfvcg5TFORW+98mXPoP1gq3a2p5Y250IvUu98HmDWT+JZckll7CapVuNLG5b52hZ2t+0DNo3TNhgF5j12/DGMvUFZmPFymuB2VyY2YPUVIayD5dldO0m+d0DJi0JgG3MU/bmcB12MNdqbpmPnMOuVuVe3lO4QTt7n7Yb5KFNq+wZmXUOlzg/yR1j/5ixlwzBrM9QTnmEqmA2YYZhkMG2W4CHkKa1YOc875yxhj/XZ+KurJM6LCnwY+zx27dfc9f3CZMphtMnX4aMF5gRwXkvzMZG47VO1DhmmGm8sAbAY4FYdFgar7ykPKHsajLbOidjZzFo9iQKrX5rYPxdYObSvobSHPKcasIbW8xk2Iah4KFgtjQB0GcopxJMtkTJmiIOUwmCuTnZ/xyY6R8bIwn6MJeccf/S0Lmf2wiOSgisW/kLvc18HyM+wIyWBkskQTh7TujZ2hDMllQT9xkfAioxtd+SUYsWc1u6Z6vQV+ZIenZX02deyd8YoeOIzgJm+2qDU31ams3s64HGForn94x37JrKcq4dUhwTzHqdDIABCGdivcmI5jS3+Iw572xeOXlDM7GH+socmFm4xp9eAwgJ+co4HMFkTkko0OmGzuJQMFtamjG3s6Yv8VmqPc7Z1s+fv53koXZuKtM+fE7NHeO8q2awEgVYn8hEacqfLTyMspI6atkmNeR9wQwrI9rZx9fHvP0HVsZuEkFHrLpP3F2318S6KmDWLxS6kELM2njeswb0fKnXXDKBXXMsMOu3bQkvhZZCY/2X8SsBHbiYi5zZsCkRUuLQF6FyZuQF+0gBxDDzNQdm3uGdnkFspw/Ti0Ua2MiUNHIomFUSSGSxi5XOgVlPOsayvEvHfXhdndYCIJcyf0TCkUtzOxqsUwzYeO2rvZYTMEaT0d4QzOpj3nziha/dyjYYoU+pllFqsFD3fdjSeYDZWQf8rPf1JS9jrKtCH3VTNAQLq2+1kOiUCj3X1CKPAWb6i7ULAyu8rG+yWF+u6SRYPS8t2TF2UMGntkMqhzVbdBQ6GoY1LBadAzNgiI39StNflu4pPBTMjCdnL5u5a31UqDvGuuooJU7NmXdrnndnHsgWk3B2ZVtrXlYJiTk5l0ntkxqYtT9Du4vQRDrE/16e6nW5YSR44xoZglkvKqLZjmj5l3Z1eVnhAXQk+A07UwkE96g9g8Zieo3HI1SOed/rAGZ9Dc1wMNmI+Ok4FZ52jK5XAkFiRqW67TPOZBcKKbB9+oITGKaAeG0w69PsvGqFlzVfgI3+08/IFqq+/dvYMUf+HXsabvuqkEjfh6HHLjDTNzVmkiv6tc9JL2uAWRVOSwKMbU8yRr3c0IdvvV2J4Nj71FHbgEHCxM4FjgM4CKt3JY0wZY6Sc9BkGJ0oK7GgzGV4xFeVkEiWzGVS+4LxsaiOXvexbZuaUL8/kltCxLYuJIkWf7ckNGRhqvq7G7ZRjtWZFfvqD88jvkJE7IJHcLrkmDcr9mFhul4HUVD/D22nPOB1ADP2LvZlMItd0RhVPWuovRMIxlqF78XqAOD7tmr6PzgrXWz3rQ1m5oFCSn3sw8vqZjEPoSPHp5ZsalsTYKqTY4nGVWNmkXu2UNNEL912Lssn2y4ZgRlp9nVW9kymjFMAqmPHlvcOaSwj3YvyU/tuS+/inMb25epTz75Kl7Z2ADBWR/iXOdTfsWa9sot5YR2+TnN4uwpRK5RDSMxNz0Zm7IX9kYlkk6Jl40MKmDugcejgaJSYtVIMmAOwgBiA73+pS7G/u7Qxczw4J2gcjcGN4DwGZiWyov4+EAgxPKFV1mEXsqP8PA4ENUm8FGrPbUC/LmDGW96phVUmi61MAJ9GBuCmspMVvhPOeU0AANws7jUOxFwTzHqdbBhe1sJzjcMJMQa7AcyrKUcH+HhpLJQMgklhc8BLZtf5aO4FAGxLqwVUlfHEKGR/AUBtO8IEHQEvfNnV3FPsByBY5P5oniU6kSwAxIBCxOLZmvGVFeXIh6djENhVCyiinip/kP2jqwF6cwNrt6dTxISdTNmr7G+fNOJBdxUJiKholVOgU6GcnQ82jgNOjMzOgrF3ldOwP7U/LHKXPWv+65c5Z4xEFTCCHccqH4r9kVUwamGoZlyf5ReqjIHZWZ18FdwSdqWKyysPF9u+NU9TJSKHJgDO+p3ncV+F7yaVQTfA2Mha546d4nDG87Db1DttBregHJ3D8wtZ1e+Zw5q/hXrmsoW99oGHxVJkZmmCa5XYVIIOAGPwQJZs4TAIgIZgTIFg1bZh+4BT+AaopprMut+z4F17VervORFKaxO9iBbVkwLAZ/uONcFsuAWDV+PFhmAGjWVzZJOq+TdeiiCLbfTNB4zpQdcJzIYOgPaBOY/pj3vOlWt3uSjDBnNsYu53K2B6NowLB9dyHGVw85f2ifkIyddoNU+EyaQg60qIOPcb1IZb7YT/79ROKhnrF/YHXJxssaSw9pBvG5ZQTe5VXhPMegQFbMS8pZtFr0uYedZBLa8p7KAVSawI5YH/1pZboE7sEK4K2ecYBe3PoqY/zR1vvbwX/38lhkgnpf3sYkFLn13zRPJNGL/091r2zF+Y6d7hiSI9CMsES+YJf5dmgpd+Q39dH+1x4Hb6AM/R3TNrghltw545mo4z5BXNMgwRUUbEZJjSdzYwmx7qoQBKZLYdqDKixF7sdW3mcJbJd9HvKYdLf6HnDstf+v7TrMglIoVjso8KeQnf9oMe0orFDLdAWV/0qgJv/+37FKgDoz5xh7GSdhS5KlKlmyEn9lBik8JKOuVoqHdI50furVIxoaWTTIAZ3YxDF0LbQYBF3gCoa4FZhXy3beluQiVvJl4XuxM8/daYKfHxLGBWqXnC7D41bSvb++iP6wVQEwm4mWw0HpqFTBAhfCqzdfQOXqIX9LtXZNM4BL/7oJws6YNzUJJgTmEnhOZjsg/mI6YrXZDcOORdTsFFIsgyasUk3oTKkkVCQmAGxGiAJCBJENlSSZNK3Clsray772cfOCFctU71dXhKybGmQNW90TeBLGckWQBI1SUCMyH6DQx0LTCrGigTpCqH/RujWYwQdRcl3RfMJBccj4L1eY80NbYie7aUWh9rANZ+brGJ/pfm2nYj9Y9h2Es3Vc6xdl8u+/PMd6K+kgC67ViTpfSrFZ0GPFducNHs4fsAkTnhlGKhIJACAFWFgHjYwSNUxATV2gE2dqnN6q4BIIq3zTOlLOfB/NW9AeXa8VLb0DBmobkxutGJrwVmhw6qAjgMDmUUmm5ts8AxLWCxKlWQkcNmNA7hiU2Qvw7HvysJkklFNK7E914UMDvmxN2evVlgs8CzWqAYDuZZx7NfehttYHbph3D7gM0Ce1tANlzhNW3tEI1u7xcf84YNzI5p3e3ZmwU2C5zMAhuYnczU24s2C2wWOKYFNjA7pnW3Z28W2CxwMgv8Hzpz12VmMIZCAAAAAElFTkSuQmCC\" 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width=\"44\" height=\"20\" style=\"width: 44px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e the smallest Euclidean distance between the trajectory of the projectile and the target \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg 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\" width=\"298\" height=\"20\" style=\"width: 298px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eis a difference angle, and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"55.5\" height=\"17.5\" style=\"width: 55.5px; height: 17.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003ean update parameter.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.6667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.3333px; text-align: left; transform-origin: 383.5px 10.3333px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.6667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.3333px; text-align: left; transform-origin: 383.5px 10.3333px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.6667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.3333px; text-align: left; transform-origin: 383.5px 10.3333px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample of algorithm's numerical result:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 80px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 403.5px 40px; transform-origin: 403.5px 40px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 403.5px 10px; transform-origin: 403.5px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003etheta = catapult(25,3,25)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 403.5px 10px; transform-origin: 403.5px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003etheta = \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 403.5px 10px; transform-origin: 403.5px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e    0.8431\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 403.5px 10px; transform-origin: 403.5px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e    \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 264.333px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 383.5px 132.167px; text-align: left; transform-origin: 383.5px 132.167px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" width=\"570\" height=\"259\" style=\"vertical-align: baseline;width: 570px;height: 259px\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function theta = catapult(xd,yd,v0) \r\n  \r\n    global g nu;\r\n    \r\n    g   = -9.81;  % grav. acceleration\r\n    nu  = 0.5;    % air friction coeff.\r\n    k   = 0;      % solver increments\r\n    dt  = 1e-2;   % timesteps\r\n    T   = 10;     % simulation time\r\n    TOL = 1e-2;   % absolute tolerance\r\n    \r\n    [~,y] = ode45(@ODECatapult,0:dt:T,[v0,0,0]); \r\n    \r\n    % solver for optimal angle\r\n    while (e \u003e= TOL) \u0026\u0026 (k \u003e 150)        \r\n        \r\n        %theta = theta + beta;\r\n        \r\n        k = k+1;    % add increment\r\n    end\r\n  \r\n    function dx = ODECatapult(t,x)\r\n        global g nu;\r\n        %% fill in ordinary differential equation %%\r\n    end\r\n    \r\n    function e = EuclideanDistance(y,xd,yd)\r\n        %% fill in computation of smallest euclidean distance %%\r\n    end\r\n    \r\n    function beta = UpdateLaw(y,e,lambda)\r\n        %% fill in update law to update the shooting angle %%\r\n    end\r\nend","test_suite":"xd = 8;\r\nyd = 2;\r\nv0 = 35;\r\ny_correct = 1.446;\r\n\r\nassert(isequal(round(catapult(xd,yd,v0),3),y_correct))\r\n\r\n%%\r\nxd = 15;\r\nyd = 5;\r\nv0 = 35;\r\ny_correct = 1.33;\r\n\r\nassert(isequal(round(catapult(xd,yd,v0),2),y_correct))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":2,"created_by":636373,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-10-19T12:41:43.000Z","updated_at":"2026-05-30T17:02:13.000Z","published_at":"2020-10-19T13:39:36.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eConsider a capapult that fires a projects into the air with an initial velocity\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ev_0 \\\\in \\\\mathbb{R}_{\\\\ge 0}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. The free-flying projectile is subjected to air friction and a gravitional force. Given a desired target \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e$z_d = [x_d, y_d] \\\\in \\\\mathbb{R}^2$\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e and an initial velocity \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ev_0 \\\\in \\\\mathbb{R}_{\\\\ge 0}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, find the optimal shooting angle \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\theta^*\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003eof the catapult that minimizes the distance between the target and the trajectory of the fired projectile. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etip 1:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Consider the states \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex_1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex_2\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e as the x- and y-position of the projectile, and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex_3\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex_4\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e as the x- and y-velocity. Then, the trajectory of the projectile can be found by solving the following ordinary differential equation (ODE):\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e        \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\dot{x_1} = x_3\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e,     \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e        \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\dot{x}_2 = x_4\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e      \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e        \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\dot{x}_3 = -\\\\nu x_3\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e,  \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e    \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e        \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\dot{x}_4 = -g - \\\\nu x_4\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e.   \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhere \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eg = 9.81\\\\; (\\\\text{m/s}^2)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\nu = 0.5\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003eis the friction coefficient between the air and the projectile. Use the ode45.m function to compute the trajectory of the projectile with initial conditions \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex(t = 0) = (0,0,v_0 \\\\cos(\\\\theta_k), v_0 \\\\sin(\\\\theta_k))\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. Plotting \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex_1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e vs. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex_2\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e will result in the x-y trajectory of the projectile, as shown in the figure below.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etip 2: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eUse the following update law, to incrementally update the shooting angle \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\theta_k\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e        \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\theta_{k+1} = \\\\theta_k + \\\\lambda \\\\, \\\\text{sign}(\\\\theta_{e,k})\\\\,e_k\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhere \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ee_k \\\\in \\\\mathbb{R}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e the smallest Euclidean distance between the trajectory of the projectile and the target \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ez_d\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\theta_{e,k} = \\\\text{atan2}(d_y,d_x) - \\\\text{atan2}(v_0\\\\sin(\\\\theta_k),v_0\\\\cos(\\\\theta_k))\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003eis a difference angle, and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e$\\\\lambda = 0.01$\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003ean update parameter.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample of algorithm's numerical result:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[theta = catapult(25,3,25)\\ntheta = \\n    0.8431\\n    ]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"259\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"570\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":52911,"title":"Easy Sequences 41: Boxes with Integer Edges","description":"For this problem, we are asked to write a function that will count the number of boxes with integer edges, that has the same given volume .\r\nFor example for , the possible boxes are shown in the figure below:\r\n                                               \r\nTherefore, in this case, the function should return .","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.440000534057617px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: normal; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 344px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003eFor this problem,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"font-weight: bold; \"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003ewe are asked to \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"font-weight: bold; \"\u003ewrite a function that will count the number of boxes with integer edges, that has the same given volume \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: normal; color: rgb(0, 0, 0);\"\u003ev\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"font-weight: bold; \"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003eFor example for \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"36\" height=\"18\" style=\"width: 36px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e, the possible boxes are shown in the figure below:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 233px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e                                               \u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" width=\"367\" height=\"227\" style=\"vertical-align: baseline;width: 367px;height: 227px\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003eTherefore, in this case, the function should return \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: normal; font-weight: normal; color: rgb(0, 0, 0);\"\u003e3\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function n = numPosBoxes(v)\r\n    n = floor(log(v)/log(2));\r\nend","test_suite":"%%\r\nv = 8;\r\nn_correct = 3;\r\nassert(isequal(numPosBoxes(v),n_correct))\r\n%%\r\nv = 1234;\r\nn_correct = 2;\r\nassert(isequal(numPosBoxes(v),n_correct))\r\n%%\r\nv = 10000;\r\nn_correct = 42;\r\nassert(isequal(numPosBoxes(v),n_correct))\r\n%%\r\nv = 9999999;\r\nn_correct = 10;\r\nassert(isequal(numPosBoxes(v),n_correct))\r\n%%\r\nv = 9876543210;\r\nn_correct = 164;\r\nassert(isequal(numPosBoxes(v),n_correct))\r\n%%\r\nv = 123123123123;\r\nn_correct = 365;\r\nassert(isequal(numPosBoxes(v),n_correct))\r\n%%\r\nv = 100000000000000;\r\nn_correct = 2432;\r\nassert(isequal(numPosBoxes(v),n_correct))\r\n%%\r\nv = double(intmax('uint32'))*12345\r\nn_correct = 488;\r\nassert(isequal(numPosBoxes(v),n_correct))\r\n%%\r\nvs = floor(flintmax ./ (1:100));\r\nns = arrayfun(@(v) numPosBoxes(v),vs);\r\nss = floor([sum(ns) mean(ns) mode(ns) median(ns) std(ns)])\r\nss_correct = [389499 3894 5 89 7993];\r\nassert(isequal(ss,ss_correct))\r\n%%\r\nfiletext = fileread('numPosBoxes.m');\r\nnot_allowed = contains(filetext, 'persistent') || contains(filetext, 'global') || contains(filetext, 'BigInteger') || contains(filetext, 'java'); \r\nassert(~not_allowed)","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":255988,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-10-11T18:45:59.000Z","updated_at":"2026-05-24T22:04:42.000Z","published_at":"2021-10-17T18:48:44.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor this problem,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ewe are asked to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ewrite a function that will count the number of boxes with integer edges, that has the same given volume \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ev\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example for \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ev=8\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, the possible boxes are shown in the figure below:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e                                               \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"227\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"367\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTherefore, in this case, the function should return \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e3\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":42580,"title":"Conic equation","description":"A conic of revolution (around the |z| axis) can be defined by the equation\r\n\r\n   s^2 – 2*R*z + (k+1)*z^2 = 0\r\n\r\nwhere |s^2=x^2+y^2|, |R| is the vertex radius of curvature, and |k| is the conic constant: |k\u003c-1| for a hyperbola, |k=-1| for a parabola, |-1\u003ck\u003c0| for a tall ellipse, |k=0| for a sphere, and |k\u003e0| for a short ellipse.\r\n\r\nWrite a function |z=conic(s,R,k)| to calculate height |z| as a function of radius |s| for given |R| and |k|.  Choose the branch of the solution that gives |z=s^2/(2*R)+...| for small values of |s|.  This defines a concave surface for |R\u003e0| and a convex surface for |R\u003c0|.  \r\n\r\nThe trick is to get full machine precision for all values of |s| and |R|.  The test suite will require a relative error less than |4*eps|, where |eps| is the machine precision.\r\n\r\nHint (added 2015/09/03): the straightforward solution is \r\n\r\n   z = (R-sqrt(R^2-(k+1)*s^2))/(k+1), \r\n\r\nbut this does not work if |k=-1|, gives the wrong branch of the solution if |R\u003c0|, and is subject to severe roundoff error if |s^2| is small compared to |R^2|.  It is possible, however, to find a mathematically equivalent form of the solution that solves all three problems at once.\r\n","description_html":"\u003cp\u003eA conic of revolution (around the \u003ctt\u003ez\u003c/tt\u003e axis) can be defined by the equation\u003c/p\u003e\u003cpre\u003e   s^2 – 2*R*z + (k+1)*z^2 = 0\u003c/pre\u003e\u003cp\u003ewhere \u003ctt\u003es^2=x^2+y^2\u003c/tt\u003e, \u003ctt\u003eR\u003c/tt\u003e is the vertex radius of curvature, and \u003ctt\u003ek\u003c/tt\u003e is the conic constant: \u003ctt\u003ek\u0026lt;-1\u003c/tt\u003e for a hyperbola, \u003ctt\u003ek=-1\u003c/tt\u003e for a parabola, \u003ctt\u003e-1\u0026lt;k\u0026lt;0\u003c/tt\u003e for a tall ellipse, \u003ctt\u003ek=0\u003c/tt\u003e for a sphere, and \u003ctt\u003ek\u0026gt;0\u003c/tt\u003e for a short ellipse.\u003c/p\u003e\u003cp\u003eWrite a function \u003ctt\u003ez=conic(s,R,k)\u003c/tt\u003e to calculate height \u003ctt\u003ez\u003c/tt\u003e as a function of radius \u003ctt\u003es\u003c/tt\u003e for given \u003ctt\u003eR\u003c/tt\u003e and \u003ctt\u003ek\u003c/tt\u003e.  Choose the branch of the solution that gives \u003ctt\u003ez=s^2/(2*R)+...\u003c/tt\u003e for small values of \u003ctt\u003es\u003c/tt\u003e.  This defines a concave surface for \u003ctt\u003eR\u0026gt;0\u003c/tt\u003e and a convex surface for \u003ctt\u003eR\u0026lt;0\u003c/tt\u003e.\u003c/p\u003e\u003cp\u003eThe trick is to get full machine precision for all values of \u003ctt\u003es\u003c/tt\u003e and \u003ctt\u003eR\u003c/tt\u003e.  The test suite will require a relative error less than \u003ctt\u003e4*eps\u003c/tt\u003e, where \u003ctt\u003eeps\u003c/tt\u003e is the machine precision.\u003c/p\u003e\u003cp\u003eHint (added 2015/09/03): the straightforward solution is\u003c/p\u003e\u003cpre\u003e   z = (R-sqrt(R^2-(k+1)*s^2))/(k+1), \u003c/pre\u003e\u003cp\u003ebut this does not work if \u003ctt\u003ek=-1\u003c/tt\u003e, gives the wrong branch of the solution if \u003ctt\u003eR\u0026lt;0\u003c/tt\u003e, and is subject to severe roundoff error if \u003ctt\u003es^2\u003c/tt\u003e is small compared to \u003ctt\u003eR^2\u003c/tt\u003e.  It is possible, however, to find a mathematically equivalent form of the solution that solves all three problems at once.\u003c/p\u003e","function_template":"function z=conic(s,R,k)\r\nz=0;\r\nend","test_suite":"%%\r\nR=5;\r\nk=-1;\r\ns=-5:5;\r\nz=[25 16 9 4 1 0 1 4 9 16 25]/10;\r\nt=arrayfun(@(x)conic(x,R,k),s);\r\nassert(all(abs(t-z)\u003c=4*eps*abs(z)))\r\n%%\r\nR=-5;\r\nk=-1;\r\ns=-5:5;\r\nz=-[25 16 9 4 1 0 1 4 9 16 25]/10;\r\nt=arrayfun(@(x)conic(x,R,k),s);\r\nassert(all(abs(t-z)\u003c=4*eps*abs(z)))\r\n%%\r\nR=6;\r\nk=0;\r\ns=0:0.125:2;\r\nz=[0 0.001302224649086391 0.005210595859100573 ...\r\n   0.01173021649825800 0.02086962844930099 ...\r\n   0.03264086885999461 0.04705955010467117 ...\r\n   0.06414496470811713 0.08392021690038396 ...\r\n   0.1064123829368584 0.1316527028472488 ...\r\n   0.1596768068881667 0.1905249806888747 ...\r\n   0.2242424739260392 0.2608798583755018 ...\r\n   0.3004934424110011 0.3431457505076198];\r\nt=arrayfun(@(x)conic(x,R,k),s);\r\nassert(all(abs(t-z)\u003c=4*eps*abs(z)))\r\n%%\r\nR=6800;\r\nk=-2;\r\ns=10.^(-9:9);\r\nz=[7.352941176470588e-23 7.352941176470588e-21 ...\r\n   7.352941176470588e-19 7.352941176470588e-17 ...\r\n   7.352941176470588e-15 7.352941176470588e-13 ...\r\n   7.352941176470548e-11 7.352941176466613e-9 ...\r\n   7.352941176073046e-7 0.00007352941136716365 ...\r\n   0.007352937201052538 0.7352543677216725 ...\r\n   73.13611097583313 5292.973166264779 93430.93334894173 ...\r\n   993223.1197327390 9.993202311999733e6 9.99932002312e7 ...\r\n   9.9999320002312e8];\r\nt=arrayfun(@(x)conic(x,R,k),s);\r\nassert(all(abs(t-z)\u003c=4*eps*abs(z)))\r\n%%\r\nR=exp(1);\r\nk=pi;\r\ns=10.^(-7:0);\r\nz=[1.839397205857214e-15 1.839397205857469e-13 ...\r\n   1.839397205882986e-11 1.839397208434684e-09 ...\r\n   1.839397463604480e-07 0.00001839422981299153 ...\r\n   0.001841981926630790 0.2212216213343403];\r\nt=arrayfun(@(x)conic(x,R,k),s);\r\nassert(all(abs(t-z)\u003c=4*eps*abs(z)))\r\n%%\r\nt=fileread('conic.m');\r\nassert(isempty(findstr(t,'roots')))\r\nassert(isempty(findstr(t,'fzero')))\r\n\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":245,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":25,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":37,"created_at":"2015-08-26T21:39:35.000Z","updated_at":"2026-05-25T04:09:40.000Z","published_at":"2015-08-26T22:21:10.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA conic of revolution (around the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ez\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e axis) can be defined by the equation\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[   s^2 – 2*R*z + (k+1)*z^2 = 0]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhere\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es^2=x^2+y^2\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eR\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is the vertex radius of curvature, and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is the conic constant:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u0026lt;-1\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e for a hyperbola,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek=-1\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e for a parabola,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e-1\u0026lt;k\u0026lt;0\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e for a tall ellipse,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek=0\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e for a sphere, and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u0026gt;0\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e for a short ellipse.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ez=conic(s,R,k)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to calculate height\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ez\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e as a function of radius\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e for given\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eR\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. Choose the branch of the solution that gives\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ez=s^2/(2*R)+...\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e for small values of\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. This defines a concave surface for\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eR\u0026gt;0\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and a convex surface for\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eR\u0026lt;0\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe trick is to get full machine precision for all values of\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eR\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. The test suite will require a relative error less than\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e4*eps\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, where\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eeps\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is the machine precision.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHint (added 2015/09/03): the straightforward solution is\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[   z = (R-sqrt(R^2-(k+1)*s^2))/(k+1),]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ebut this does not work if\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek=-1\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, gives the wrong branch of the solution if\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eR\u0026lt;0\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and is subject to severe roundoff error if\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es^2\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is small compared to\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eR^2\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. It is possible, however, to find a mathematically equivalent form of the solution that solves all three problems at once.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":46696,"title":"Number Theoretic Transform (NTT)","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 63px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 31.5px; transform-origin: 407px 31.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eGiven an input polynomial (r) of length \u0026lt;=n and a prime number (p) such that an nth root of unity exists and the modular inverse of (n) modulus p exists. Convert the polynominal coefficents by Number Theoretic Transform (NTT) using the primitive nth root of unity as the generator mod p.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function rhat = numberTheoreticTransform(r,n,p)\r\n  rhat=r;\r\nend","test_suite":"%%\r\nn=210;\r\np=5881;\r\nr=[8669,57047,39514,17386,56675,3808,29999,47330,22216,26294,34536,58604,51010,4546,18270,24862,...\r\n    56667,27522,15720,39167,31418,58887,61257,53601,46459,48707,58963,4275,22014,284,54270,33255,...\r\n    23996,14853,35050,18971,4480,5568,4478,26857,8085,29033,58912,23176,7875,37297,57346,22844,...\r\n    2747,9328,5019,48531,29918,43794,45825,37444,41202,57525,43407,57371,30639,9262,4465,46808,...\r\n    20184,43985,42757,34802,46865,33083,31981,32626,61340,25511,7677,15756,44886,55001,63579,14101,...\r\n    49829,38279,26407,33426,32482,42688,48739,19788,5872,54130,25531,50810,11755,7167,59320,57432,...\r\n    65522,56639,2416,35696,65379,33489,57246,4602,64719,60470,36979,28276,22140,47233,894,24514,...\r\n    60469,35814,31056,32541,20248,62314,64355,33656,65050,29874,27921,13973,12664,54575,47620,34717,...\r\n    54334,33546,36173,13977,38523,9356,3422,44781,39882,14395,26625,41281,36392,8361,11088,65,...\r\n    27404,32013,10477,43701,1174,7843,62398,63953,2026,32367,56539,15917,54674,53319,41220,146,...\r\n    24885,59271,44587,24826,41415,15942,37448,64338,55684,18575,44725,23470,64679,5504,16404,53172,...\r\n    5532,34816,52469,48419,9284,28697,22962,31358,38496,9555,59331,41955,10678,37087,61054,51321,...\r\n    44937,30554,17060,37307,16303,20925,59690,58013];\r\nrhat=[4391        3275        5259        1238        4797        2486         919        3786        5067        4389\r\n        2255        3368         968        3027        4274        3358        2953        3646        2967        4281\r\n        5348         229        5498        5448        3419        3720         100        5376        2968        3437\r\n         404        3686        3576        4743        4840        4942        4237        1414         102        2806\r\n        3832        5075        5084        1139        2483        2021        5541        1332        5022        5155\r\n        5235        4306         797        2739         911        3669        4133         547        5678        5162\r\n        1905        4772        2478        1657        1905        2539        4053        3185        3567         113\r\n        1985         674        1359         182        1645        1249        1752         922        1178        5427\r\n        2476        1241        2496        1703        3691        5044         802         304         905        2280\r\n        2211        4163        4845         690        4246        3016        3159        1555        3762        1416\r\n         105        4332        2706         308        2230        2117         787        1189        5549          72\r\n        4092        4428        4103        3397        5700        5630        1803         840        4153        4385\r\n        5537        1564        5437        5553         377        3468        2885        4827        3523        2618\r\n        2238        3633        2254        4028        3660        1909        5874        1850        3153        2253\r\n           3        2286        1953          81        5161        5764        5063        2887        4983        3481\r\n        5044         770        1225        4832        1673        5556        2434          21        1735        5422\r\n        1003        2672        3262         982        2344         269        2638        3485        3278        4882\r\n        4604        5118        4587        2136        3361         478        5289         808        3170        3752\r\n        2862        4604        4813        3077        5849         813        1214         135        3691        4774\r\n        1668        4361        5564        3949        5019          71        2441        4481         722        5462\r\n        4925        2693          41        3294        3971         361        1224         445        5386         186];\r\nassert(isequal(numberTheoreticTransform(r,n,p),rhat(:)'))\r\n%%\r\nn=1024\r\np=12289;\r\nr=[52074       13446       46646        7881       15396       46708        6135       41514       35944       46632\r\n       60673       28779       56867       40989       26142       30972       42638       64624       10831       31518\r\n       11720        1785        7753       22717       17571       46438       14101       13576       32367       47787\r\n       33917       57421        2557       21929       54559       62787       15982       49616       35069       61443\r\n       41091       39983       39203       37658       65232       33146       22261       58086       13029       33898\r\n       59846       13342       39604       56619       42582       19991       12967       30948       40839       59183\r\n       43513       34073       33844       13013       46134       51761       33215       10414        1724       14299\r\n       25506        3527         492       44069       61099       15491       62308       53144       20892       57227\r\n       48497       56504       45149       59102       45065       15355       25860       31228       34930        5419\r\n       53584       29028       61998       13051       37247       30454       38303        7621       21415       30500\r\n       39344       35914       57248       19548       24959       40592       39750       57391       39465        1437\r\n        5570       37149        7423       32539       41587       40326       46834       41627       23719       52971\r\n       60447       44590       23237       58320       23804        8036       26315        6375        8842       11744\r\n        3512       24338       15855       32860       26713        8112       56275       59535       59887       10839\r\n       34539        5126       36722       18153       24163       18642       60324        2294       41979       11901\r\n        7789       29907       40155       34993       30696       48216       49206        2605       43173       45313\r\n       24913        3135       19713       37634       32991       26955       18716       64786       44258       14009\r\n       53269       48382       52307       27053       59672       54328       52220       44969       48795       19536\r\n       15997        2490       52143         967       13528       61283        9356       24686       55192       50353\r\n       57961       62537       51189       46056       22190       26153       33066       33051       33859       32843\r\n       46704       48652       23009       33210       37625        3421       40022       50036        9952       59602\r\n       24782       61436        3558       24986       31911       37433       46124        3203       24947        3791\r\n       16313       33643       46445        4255       17184       48999       25122       47574       53806       28622\r\n       16571       15787       65072       23499       37984       20987       47754       45962       11230       37503\r\n       50282       17037       10648       15351       57562       32304       58149       30073       21625       37032\r\n        3267       49740        7442       13336        3994       14526        3660       38161       63338       53989\r\n       44911       65099       59826       53331       28893       61556        9058       22222       52841        8264\r\n       40650       23377       31565       25784        5521       31608       56561       11182       14561       19668\r\n       48934       49339       55823        3511       36912       35389       27639       26161       65521         139\r\n       64045        7212       53078       24579       35344       14487       26955       60278        4177       62331\r\n       25160       39127       12239       50790       50335        6287       62858       14814       27884       50220\r\n       17052       28219       16200       10832       15275        3943       49168       23658       26498       49237\r\n       57505       47888        3551       59783       38493       53707       64290       21270       26233        9100\r\n       52828       17116       39908       20919       30079       50559       15303        5477        7334       22893\r\n       30220        6213       50936       21612       56425       12825        6306       33598       27807        9918\r\n        5961       29554       33493       13384       43308       58662       25203       54582       40209       32553\r\n       36979       41947        1818       50280       23191       44846       32785       59284       64753       52995\r\n       12280        8653       64905        4585       22753       43047       37372       47421       14411       41475\r\n       34844       29676       32829       62261       16627       64905       64004       25100       23205       45115\r\n       23267       42742       21757       10368       62424        2208       32299       19530       17448       41914\r\n       20629       54198       11395       18772       19542       27803       26272       45332       19103       47796\r\n       47627       20190       41001       45031       10381       32111       65207       57701       12346       56350\r\n       33801       26369       37692        9250       23677       38240       17104       60591        1498       41088\r\n       51815       57948       49216       33560       48603        5457       43602        5324       29452       11835\r\n       13401       45913       10061       47272       46261       43263       63193       31632       15967       37572\r\n       44440       15851       23382       60872       45933        3427       43984        8405       56932       10719\r\n        3439       49796        9433       47979         408       36492       19606       16574       34643       59379\r\n       52505       19066       55745       49142       24533       46663       34807       57931       59908        5068\r\n       44470       18182       22142       26694       59080       31975          95       12863       63827       22186\r\n       61997         400       18035       15695       20863       40475       57919        7953       38366       38051\r\n        6000       24557         393       34134       39130       14010       26501       35631        7797       31145\r\n       59535       28634       52554       14357       19516       42313       19739       20618       60721       52777\r\n       33420       19942       32598       55206        8192       24945       62297       25037       38899       34785\r\n       40298       19061       35248       43445       25451        6796       30189       51874       57908       14897\r\n       20714       15893       57076       53492       53587       24740       18851       54996       27818       46496\r\n        5078       61386       47372       52027       64302       17226        5546       44579       39797        9740\r\n       55745       56373       43783       30743       56491       15812       38153       27323        4637       43130\r\n        9471       26032       11719       20285        5493       40823       10031       42132       60605       41548\r\n       24280       31419       36077       45061       22132       34270        4790       14030       42079       15027\r\n       40789       37027       62906       64674       15474       27081       38047       40453        6848       11942\r\n       65375       32087       39060       50458       20827       14273       18809       44249       45889       10902\r\n       33904       17682       52990       54367       64516       56266       23718       39388       25939        9804\r\n       64914       64863       64522       46273       35930       56427       47502       22695        5564       13287\r\n       14846       12037       58059       39015       49102       18608       56250       23881       14056       62584\r\n       26083       56469       14014       49340       55171       40330       22801       11238       16305        1042\r\n       45650        2138        2269       32553       10937       51084       63028       52124       14853       62751\r\n        4236       21755       29564       56697       59185       62576       62493       32287       46072        1683\r\n       48998       49069         904        4458        6889       60267       13502       23240       49424       63642\r\n       27551       42229       31045       63474       48830       25219       50347       50794       35866       19503\r\n       53170       11091       62337        6472       47800       10658       40339       15519       36273       34411\r\n       24877       62403       16315       35846       47020       52215       60222       55367       41325       56514\r\n       20910       35603       25324       26409        8744        7459       39487       53511       64582       58746\r\n       64621       16476       28274        7014       29215       10408       46015       55458       41568       12386\r\n       47066       37917       54452       47458       33343       23319       48737       24260       39351       43300\r\n       27078       59996       54044       40218       34766       55558       25238       25115       59584       61684\r\n        6463       58693       29687       51312       56342       38193       16482       56448       37410       63943\r\n       48140       31621       24940       37134       44415       38415        2409       30402       21982        7073\r\n       41766       29015       60677       53170       52811       60675       30941       37391       62727       11724\r\n        4839       20431       48551       37799       34815       37688       42275       45567       28830       48925\r\n        7897        3625       48341       61867       62645         653       18282       62974       39422        3241\r\n       64329       49400       62057       57111        4369       53043       33938       35803       47203        4671\r\n       32558        8647       33429       33266       35488       39898       16100       41718       44484       32055\r\n        1468       23325       51896       51696       18458       31451       19497       37414       13943       55698\r\n        3527       25943       29633       31000       31516       17592       42629       60759        5349       65342\r\n        9232       58033       55653       54316       44883       16914       58418       56607       17988         287\r\n       58554        1391       25587       21134       13648       31523       56433       11130       56853       35560\r\n       30527       55317       48390       63972       39856       14899       13756       11711       36658       56449\r\n       36756       18878       63991       18232       21376        3185       26155       15958       30449       59581\r\n       32404       16406       34294        4773       57727       11091       58188       49268       28200       55400\r\n        4442       32006       28174       49232        8742       16937       16811       13050       50723       57597\r\n       58828       47778       13576       54472        6711       12970       63360       64417       42855       48901\r\n       18911       13278       21194       60446       62856       39694       40577       46506       43104        7699\r\n       17632       14173        7265       21431       10020       53982       10836       11497       10552       33359\r\n       38941       63985       24589       52695        9996       53124       54145       56249       28336       11064\r\n       31187       38878       21620       35274       10194       52575       42971       59599       33101       54467\r\n       24137       19949       22420       30362        5870       46406       35812       63023       24597       60818\r\n       42966       63419       53550       53788       29781       56320       16471       37394       31481       11107\r\n       61485       58718       34844       62384       43836       51189        2631       36888       22440       57916\r\n       40660       12453       34152        4998       54480       13356       15294       11577       50931       25418\r\n       18536         117       50745       46443       51788       65099       23665       33664       25162       25072];\r\nrhat=[8149       10593         825        1418        8962        1559        3161         152\r\n        5086        9561       11949        5217        5798       11589        3150        5329\r\n        8984        3998        7095         390        2183        9925        6985       11431\r\n        3249        1053        7833        4160          13        1692        1976        8920\r\n        9291        9284        9074        2974        5068       10375          80        4023\r\n       11492        5804        6645        1044        7322          19        7192        7839\r\n        9217        4466       10449        3187        2137         119        9226        6202\r\n        7603        5162        8758       12104        7195        7646        6527        5506\r\n        6225       12166        3612        9329         470        7699        9996       11300\r\n       10722        1986        5330       10948         843        4540       11096        7298\r\n         446        7352        2390        5148        9522       11654        1156        8749\r\n        3736       11720       11276        6342        9275       11013         782        1253\r\n        3336        5994       12271       10025        4567        8321        1619        6205\r\n        2010        3243        4122        2079        9240         989       12020        7584\r\n        9610        6632        9036       11327       11665        9187        1417        4616\r\n        3768        1590       10149        3400        4707        5141        3973        5828\r\n        9623        3436         997        6446       10458        5845        8785        4056\r\n       10876        4226        7775        4853        8562        9793        8662        8391\r\n        6182        3663       10851        2565        5271        5953       11059         715\r\n        7529        4284        2244        8402       12053        7689         541        5153\r\n        8110        5951        6609         335        3517        6766        6683        3012\r\n        4955        3196        8713        6749         246        5847        2747        7607\r\n        8485        2656        4214         985        4827        2550       11484       11457\r\n        3124        1611        3563         382        1919        3058        8499        5773\r\n        1423        2757        1327       11084        6349         455        1929         417\r\n        9081       10604         613       10502        7325        8929        7125        8483\r\n        9465       10411        2134        3556         986         530        9950        1448\r\n       10951        8652        3309        7995        6767         315        3390         507\r\n       11937        8868       12228        5740        6625        1837         388       11209\r\n        3858         829        1248       12057        7320        3536        2756        5663\r\n       10974        3976        8957       11721        4338        9767       11429        8609\r\n        6564        1713        9831       10924         912        9766        6373        7233\r\n        3700        1269        4315        8607        4546        8196        9941        7644\r\n         878       11980       10491        2706        3912        9513        2567        8190\r\n         643         379        4709        9262       11112        1860        4737       11022\r\n        1323        3739        8110        8196       11511        2236        8007        9557\r\n       12024       10027       11760       11831        2394         500        6894         389\r\n        8560        4490         168        8370        1272        5988        6261        8753\r\n        4943        2775        2362        2464       10956       12172        2558        5757\r\n        1707        7697        6861        2490        6732        4772        9887        8195\r\n        4315        3792        1287         580        3928        6061        9571        1812\r\n         783        5756        2086        9790        5118        7639        6449        7058\r\n       11898        3862        2787       12173        4520        5695         212        1507\r\n        5346        9561         654        9830        5535        5850        4702        3448\r\n        5173        7945         740        8601        2757        3408        1153        7755\r\n        8707        6923        4891       11539        7076       10827       10657        1702\r\n       11981        8090        5301         856       10035        6307       10356       11679\r\n        9453        2222        1223        9948       10545        6152        6122        5565\r\n       10032         113        4049        4053        8376       11269       10270        1305\r\n        2126        1286        6659         881        3528        8590       10609        9754\r\n       11887        3876        1784        6936        1034        6381        1763       12219\r\n        5357        1336        7692        2667        7716        9060        8531        5530\r\n        5945       11985        2654        2560        6865        4738       11695         801\r\n        5824        6645        6710        6940        2131        2231        1940        4950\r\n         746        3214        6012        8276        8247        3992        9384        5457\r\n        7807        4112        9317        2273        1130       10350        8548        8193\r\n        3642        8624        8082        4908       10678        1835        7296         848\r\n       10867       11073        9696        2669        5746        1260        7347        4877\r\n       11495        5170         991       10986        8479        5253        2646       10197\r\n        5326        2942       11788        1621        2984         294       10203        9804\r\n        4494        2383         294        5111        1339       10478        8541        9356\r\n        2187         172        5127        8875        8915         789        2201        5140\r\n        5160       10441        4096        9010       10106       11593        6362        9775\r\n        4295       10907        1824        1894        5510        5337        9922        3371\r\n        8127        5345        4006        5209       10033        2859        4601        5742\r\n        3793        8908        6314        7883        8653       11031       10085        5403\r\n        3859        7385        4404        8611        5490        3923        8187         480\r\n        9766       12182         901        3569       10257        6668        3709        6931\r\n        2481        9841        9271        2472       11437         391       12053        4980\r\n       11992        3545       10994        9307       11791         322        3678        9815\r\n        1904        6151       10952        4868       10935        8320        9856        9570\r\n        6752         254        9868        7270        9019        3920        1414        1270\r\n       10364        4598       11037        3621        3234        9263       10271       11024\r\n        5000        1675        5510        7650         440        5417       10452        1974\r\n        5922        4450        6056        5245        1126        2867        2645        7398\r\n        1290        5398        5692        8986        3751        7506        2269         488\r\n        1909        5213        4327        8275        5794         534       11630       11908\r\n          53        6833        5782        5954        9629        5503        2239        5699\r\n       10980       10804        4193       10615         236        6096        2895        8953\r\n        5824       11122        7883        2053        6154        4548        5701        5575\r\n        1747        6519        6826        4063       11574        7423        9720        4636\r\n       11375        6605        1810       10176        6039         587       10520       10310\r\n        1100        3029        3862        8091       10427        4614       10870        2274\r\n        8785       11559        6971        4077         925        4907        5804        7863\r\n        3296       12221        8683        3445        3022        7680       11201        3591\r\n         773        3117        2404         968        4600        2668       10518         468\r\n         215        6056        7487       11731        2328        6050        2762        8744\r\n         677        1468        5443        8970        9622        8609         391        9362\r\n         850        9053        2676       10194        5487       10187        1917        5679\r\n        2586         415        8841        4002        8329        9123        1706        6923\r\n        4346        8013       11694        7230        4849        5346         958        9474\r\n        5828       10768        3592       11256       12214        6209        5772        4820\r\n        8719        3551        5014         187        8099        3338        9425        8455\r\n       11193        3088        2025        7193        3726        6393        4237       10983\r\n        7306        3335        9019        6401       11140        9163        2214        8141\r\n        2901        2598        7927        9127        3635        7468        2931        9271\r\n        6188          83        8923        5051         213        3750       10987       11744\r\n        3529         829        6394        6310        8428       11624         392       10014\r\n       10407        6190        4717       10647        8578        4959        8400        9476\r\n       11730        3977        3410       10130        8954        3171        6364        1588\r\n        9146        6763       11717        2289       12045         329        1489        4830\r\n        6135        7211         568       10450        9554        3037        5529           9\r\n        3995        1662       12272       11074        5671        1469        7367        4854\r\n        4549        8081        2025        3242        8687        5009        9142        2004\r\n       10161        2020        4083        1333        4743        5254         194        6885\r\n         226       11966        6259        3939        5004       11828        3961       10006\r\n        3966        8223        9013        2152        7274       11391         120        3271\r\n       12189        1703        2227        5125        5269        7245        8442        8137\r\n        4036        7892        8282        9848        5536        4409        7306        8365\r\n        4788       10993       10891        2650        5180        9379        5748        3115\r\n        1578       10933        4035        6497        8116        7574        4788        9184\r\n        7292        2637        2829        2197        6402        5607        8814        3297\r\n       12163        6864        3905        4982        6710        7802       10831        1012\r\n        8629        6883        3876        2358        3998        1465        7663        5577\r\n        1940        6499        1233       11847        8131        8283         577        8690\r\n        8536        1709       11555        7614        1606        7021        2479       10991\r\n        7673        6724        9074        5707        3131       11008       10835        5567\r\n        6706         321       12276        3407        4790       11852        3340        4209\r\n         887        6216         706        7716        9370        2905        8846         170\r\n        9563        7171        5219        7938        3769        7095          68        2976\r\n        3914        3867       11656         347        7395        8967        5517         894\r\n        2589        4130       10205        5493        3040        4057       10259        5751\r\n         248       10770        3157        1900        1428       11496        3608        7411\r\n       11099        5378        8023        1291       11427        1273       10747        9407\r\n        1600         586        9694        4493        2681        3073        2814        9289\r\n       11724       11813        7932        7385        3639        1582         135        5276\r\n        8990       10675       10619       10670        6193       10730        4885       10384\r\n        5511        2999        4965        9346         562        3131       11572        6417];\r\nassert(isequal(numberTheoreticTransform(r(:)',n,p),rhat(:)'))\r\n%%\r\nn=256;\r\np=3329;\r\nr=[17789       14064       31398        7235        4452       54042       17029        9244       41506       19221       25101       62219       54529       40064       62923       42789\r\n       56877       51841       65136       14198       13625       50288       57544       47986       60999        3380       64958       16802       49403        3404       61808       25001\r\n       48595       42905       39615       53151        2595       65348       12338       45289       64079       33038       18797       64871       40754       37730        7385       19662\r\n       29351        1713       61925        9087       30759       14919       49754        2260        6133       50356       46280       22925       25827       55203       42486       22291\r\n       46506       51496       32141       57796        9836       60263        2076       32037       43367       18545       35075       13665       23545       32750       31509       60222\r\n       61887       60461       28701       60526       64966       42074       42096       63661       39503       14769       12662       43635        5823       28771        4359       29901\r\n       11410       32264       50636         835       27987        6902       37150        7369       31052       21711       45182       63789       22392        9768       58836       28999\r\n       16029       54657       48763       24717       62611       17574       24668       48707       23347       29704        3306       40809       35957        1853       32586       29765\r\n       42003        8608       29026       10997       47464       50059       13929       41847       31167       48325       12087        4164       30182       49589       50548       61950\r\n       52993       49793        3473       35404       38069       52789       51914       38940       43976       33415        2992       24478       42300       52173        3955       14360\r\n       55926       60669        5755        6662       35406        6832        9531       32677       62891       25068       58002       10895       33654       19238       17200       57829\r\n       26091       54572       52296        2573       46231       30786       32056       37214        5838       59341       55036       15157       53374        7550       42668        1302\r\n        7569       17000       42964       61160         329       14356         841       27951       52280       63259        7743        3421        6368       24582        8755       22397\r\n        5261       13960        2119       63674       51282       60470       12229        4996       38717       41174       26896       59097       30389       54322       41847       50202\r\n       23623       34230       36507       23653       60742       20992       31800       19043       59781        8652        7879       51989       38654       55166       25227       22465\r\n       54323       26041       47172       42218         543       56199       54933       36787        6627       40521       37492       24445       12266       43597       50180       40554];\r\nrhat=[1004        2562          12        2074         386         769        3081         923         234         324        3276        3310        1457        2786        1538        1203\r\n        2725         438        1145         992        1049        2056          79        1027        2483        2538           4        2926        2235        1721        1323        1176\r\n         545         183        3074         501        2640         855        1161        2419        1203         813        3106        2589        2852        1456        1682        1723\r\n          69         623        2891         274         813        1126        3161         654        3073         693        2162        3089        2845         612        3031         293\r\n         592        1068        1284         749         404        1253         426        1999        2639        2516        1092        2077         453        1649        2336        3163\r\n        2039         495          51        1711        1959        1829         200        3273        2348         141        1326         760        3201         400        2955        3279\r\n        1452        1853        1912         973         666        1696        2042         965        1095         210        3132         160         766         740        1804        2075\r\n        2315        2977        2197        3084        1811         340         140        1425        1279        3194         758        2224        2692        1839        2400        1477\r\n        2429        1148        2851        2684        3141        1255        2870        3295        2652         899         716        1549         406         517        1155        2856\r\n        3116        1361        1678        2120        1646         915        1518        1631        1685        2535        2169        1417        1796         828        1899         911\r\n         309         738         754        2490        1194        2757        1105        1086        1929        1892        1032        1186        1805        1880        2454         621\r\n        1514        1448        2271        2505         856          47        1949        2456        1700        1041         112        2844        2723        1705         981         781\r\n        3026        1470         145        2240         171        2116        3322        2831        2726        1800        1228         873        2341        2557         246         887\r\n        1275        2019        1990        1422        1937         820         605         925        2552          93        1790        1408        1989        2718        2353        3141\r\n        1249         376         831        1305        2941        1750        2254        2190        2703         774        1527        1776        1829        2916         661        2171\r\n        1503        1689        2004         368         643        2582        2079        2419         999        2909        3317         764        1712        2256        2638        3065];\r\nassert(isequal(numberTheoreticTransform(r(:)',n,p),rhat(:)'))\r\n\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":145982,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-10-06T20:08:10.000Z","updated_at":"2026-05-25T03:12:04.000Z","published_at":"2020-10-06T20:17:36.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven an input polynomial (r) of length \u0026lt;=n and a prime number (p) such that an nth root of unity exists and the modular inverse of (n) modulus p exists. Convert the polynominal coefficents by Number Theoretic Transform (NTT) using the primitive nth root of unity as the generator mod p.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44961,"title":"RSA decryption","description":"Decrypt a large integer string using RSA decryption given the public key (n) and private key (d). Convert the large integer decryption into an output message string with UTF-8 representation.\r\n\r\nExample:\r\n\r\n encrypted_message = '158037161019988039882393476857386648994978438821991287680442802412825849535544067751541256843540494019';%input\r\nn='418336393847020647250825879743341651032293545176800777981294580200903315345456262337972725306797613061';%input\r\nd='8444986024072025211908427894173383040354675378319105204646840203847580180874615752845913488969020869';%input\r\ndecrypted_message = 'I like to swim!';%output\r\n\r\n\r\n","description_html":"\u003cp\u003eDecrypt a large integer string using RSA decryption given the public key (n) and private key (d). Convert the large integer decryption into an output message string with UTF-8 representation.\u003c/p\u003e\u003cp\u003eExample:\u003c/p\u003e\u003cpre\u003e encrypted_message = '158037161019988039882393476857386648994978438821991287680442802412825849535544067751541256843540494019';%input\r\nn='418336393847020647250825879743341651032293545176800777981294580200903315345456262337972725306797613061';%input\r\nd='8444986024072025211908427894173383040354675378319105204646840203847580180874615752845913488969020869';%input\r\ndecrypted_message = 'I like to swim!';%output\u003c/pre\u003e","function_template":"function decrypted_message = RSA_Decrypt(encrypted_message,d,n)\r\n  decrypted_message = 'I like to swim!';\r\nend","test_suite":"%%\r\nm='158037161019988039882393476857386648994978438821991287680442802412825849535544067751541256843540494019';\r\nn='418336393847020647250825879743341651032293545176800777981294580200903315345456262337972725306797613061';\r\nd='8444986024072025211908427894173383040354675378319105204646840203847580180874615752845913488969020869';\r\nmessage = 'I like to swim!';\r\nassert(isequal(RSA_Decrypt(m,d,n),message))\r\n%%\r\nm='99761240327251194937668282784881225946299704960682605372368300120848092908591455333889649815054891832920433772438140697229743047905660648016064750862552787515723449901585029566883857331537912668878918706229773460108043130645834201085405226084010413433457258525704626839479467337463012332940952234541291286602023412313079939518264986360860440514106228995186076871512459300140814669747884371890194124';\r\nn='2044371069952561243871813747701535503388267616657953475148898181142012590397809234167373308955772082860082985954286137615597515257087506574051530104475374974920093127841789408014496870693507622812332673504654584870100580476794800708440785082437228308551107726064054828640053321250498545183042994878498928173976370185712833904492317580152665428272199317847097773542066059565512439224992672101163367819';\r\nd='131358569595346680469722564224349085322306406056832660693226883146757023027681686453130430199929142940992255578581548217026735077095312421736286269892515739496597527524021166625708322359741224036234988705082882699895245449017415856831226734459122764117157172961113990706104659539690141481103935528273973382371183154438463086325519326121228923730136467766036183357672350586091588640276470994058874793';\r\nmessage = 'First, have a definite, clear practical ideal; a goal, an objective. Second, have the necessary means to achieve your ends; wisdom, money, materials, and methods.';\r\nout=RSA_Decrypt(m,n,d);\r\nassert(isequal(RSA_Decrypt(m,d,n),message))\r\n%%\r\nm='23055330962704323769878549529115024711681463755869375992447358448257597289666397997393945478297323372847937346015110864832142828615195632399869833059964395947034849804307978652470092440225822549097020437344501330227622390881049097656181781980124142659454562923055085607225529541369345564097544873012395850084756321027482195140106724706102857159831541577386517426252851135917933602432485339455137853480425755360413522537180403768732770990453717801905433194599201771294559164174484366213507079896512750859909040228686293902666244968559905065091421969981123917913807285202549025830873320919273470513619143052127674709353494599047667391749001044180280253098309127910967801244777547067797257847396027373615105278074126621948502699483945442597921239032762535839481741386676784123570880434889025648597593711911259097408384527597630246903648267481820555872340696607290773027177673329986283293168575528258578575033034179306629426942699054620008867304832513427178319015491321861291086200676826846740503213061372327825278234127270730394825241275398344604061667061892125034848354745243512927194104643800196788262228848172689053393410972717679060779374106225778531311917434399927265718394014531619654762559111657295163989321031700962115070401533540746801357628868609255941469541707984326602981168016264720519340003693219403163278843126513912057794102645161814569893320900148891952732032827189210372803453777673309727629845360314416264926243414888253428149538146304399700653423118791724424837155323935319171814705469113037576054893140065594406360362408167462563925199225169055130480445976569760198217276692195730775524801021060293503580957364539210146595506177369243830063105693396808209631409860229448190041996912370291877581261016834769083953407597967437841733812344230896457097759403613339974363380556576775192238298546579676296359906794868505900966056565863994904406875527092603782089107591766239203672780399852597928423220774857864301715176837587724591257928112610743311445186658958007825888475800717425136849205837547536117562642680';\r\nn='44842008376687803367401704855862174057751658874412319214254366772513580664834338103711248053800336314605433363839051280089244765499067546768593375603554906634041005882836398715141891796137918851678901692978242458027064144613729242304549721699241688731628849316717564354846304644497408201649639245333007226999680183209765262168096747256549932760714835163829498814356014506466022681051474161134416048209239076204778228261950266231966892099194904195589855692846886804440024833800891898474520523721428667143505915995217245366095168045283186412220277535584022415882288464175935131208288146822299727657938865855853054174274144036081729604573534323903573564345990066095029031133576597298513883268061041004553754062342541175017746399640362184228727837219293246060058323563065371426108553463224921179093569734439828568435658070810483844709529476015493154462587917722806971578721201741643550136095893915598053953011321347017522943202801820158823344592288227452688958887872572522782675644714289349442555731461112275849176682816627205790162163242534462778708459909915826953853537650009043468930450287584181263070727575137424307931682948818963610116434419852585517456098152109913486868606969385133300794635427715727859479612757775192862801547635779921771426233897718528292667112148507798722127322607462037572360649156150337532940752245966765201596748120586383127825681873289878451591957019880886587602576426267683951317718936847177641679183717321862429721190212389237395890951104472937261743929446841800523276218816808066432194473964294708564511969507018959711475230849760902049071415838600456901108854663197095849833602808088094456341330276320302662577163562657750920481798540333412112880539002713127873625438212479154166226846436038698572615482646490520911334607730484144343834476612603467400722323562384577281087071440008144012534847478593412147312743274523095010025887231371129897419306984829625295595332434377173711414555784685658774002600022998291909172576630780759729269867715816511099791566815282862210319017784751883446582453959';\r\nd='6565816445407878925089441991498747612160839198603241148969054176252198301108538816900577327681584864655900309129187116952811278662878254707896639032786256375181309679291058212467790617143590175026484896254317631677185521639888090836542092702228103896557828982761215001435908561096741216458335569193212038242350596427680660630865867932219252556141104387324837429582683296520255026128293117631961432452139374326884841820676484348718346835892309697741432400454075190738155214690226263908349465883864526754491093121291860880617807231984031261908018115438062149668224668541927056762969514272957080528641551440449912383177971011340773567381595669197227397095749281691989236351340479846140946699426488082757798251098448588056674770754671643802109836061405587518383808732642252532232749119323532411226969424472963287039513173436339822906642733903667734412972727748722945805719038892729473535651253460092450459024621811281873600606895132005494130257832946742084673535377347237875132451694131873556170785954511701761479583203082125987308962572083138475191433479413530168163642321800669575367485309537509082240852273955663933547032858727060003363699057579114677450927657082252183166216411898446363400905797006665507108575986344278988745322935954278566001664060256959781130851422164231215979527541890717097899466088129536122753240075629363188908753526503257284825826946057815889269456925910606842810401392503996936381736547569711529540423604489529836981870198914784848089842823822656591932437295439032050213520000125621624734133380868707883468507784350355251698659006842220673722867592264770652693364873913361956132623339827358272992906883218249723187904101702943978747751691915678242891407493339828656260188711974558580485287404900248093520605466868886511014909072710668999090169680693653605673085931199903788603401848851956524927526592113342563256817891019122155938256416456381627581783190684277329685396205824988569434587069719626936811661218907123975873968219302538615272967013401476757929690613750478379916399826582503908605565505';\r\nmessage = 'People who succeed have momentum. The more they succeed, the more they want to succeed and the more they find a way to succeed. Similarly, when someone is failing, the tendency is to get on a downward spiral that can even become a self-fulfilling prophecy.';\r\nout=RSA_Decrypt(m,n,d);\r\nassert(isequal(RSA_Decrypt(m,d,n),message))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":3,"created_by":145982,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":"2019-09-08T23:48:25.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2019-08-29T18:26:28.000Z","updated_at":"2026-05-25T03:04:28.000Z","published_at":"2019-09-05T14:41:54.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDecrypt a large integer string using RSA decryption given the public key (n) and private key (d). Convert the large integer decryption into an output message string with UTF-8 representation.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ encrypted_message = '158037161019988039882393476857386648994978438821991287680442802412825849535544067751541256843540494019';%input\\nn='418336393847020647250825879743341651032293545176800777981294580200903315345456262337972725306797613061';%input\\nd='8444986024072025211908427894173383040354675378319105204646840203847580180874615752845913488969020869';%input\\ndecrypted_message = 'I like to swim!';%output]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44963,"title":"Mask Generation Function (MGF1) for PKCS #1 Standard utilizing Optimal Asymmetric Encryption Padding for RSA Cryptography","description":"Create Mask Generation Function (MGF1) from PKCS #1 v2.2 standard (B.2.1 page 50) at below link. Input will be character array (mgfSeed) which will need to be converted to octet array using UTF-8 representation. Output must be octet array (uint8) of length, maskLen (input). Hashing must use SHA-1 (160 - 20 byte).\r\n\r\nFor example:\r\n\r\n  mgfSeed = 'I like to swim.';%input\r\n  maskLen = 20;%input\r\n  mask = [170,251,101,210,23,101,10,242,193,163,174,148,104,138,228,245,52,234,0,195];%output\r\n\r\n\r\n\u003chttps://www.emc.com/collateral/white-papers/h11300-pkcs-1v2-2-rsa-cryptography-standard-wp.pdf\u003e","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 194px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 97px; transform-origin: 407px 97px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eCreate Mask Generation Function (MGF1) from PKCS #1 v2.2 standard (B.2.1) at below link. Input will be character array (mgfSeed) which will need to be converted to octet array using UTF-8 representation. Output must be octet array (uint8) of length, maskLen (input). Hashing must use SHA-1 (160 - 20 byte).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eFor example:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 60px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 30px; transform-origin: 404px 30px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003emgfSeed = \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); \"\u003e'I like to swim.'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e;\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); \"\u003e%input\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003emaskLen = 20;\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); \"\u003e%input\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003emask = [170,251,101,210,23,101,10,242,193,163,174,148,104,138,228,245,52,234,0,195];\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); \"\u003e%output\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eSee: https://www.foo.be/docs/opensst/ref/pkcs/pkcs-1/pkcs-1v2-1d1.pdf\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function mask = mgf(mgfSeed,maskLen)\r\n    mask = []%octet array of length maskLen\r\nend","test_suite":"%%\r\nmgfSeed = 'I like to swim.';\r\nmaskLen = 20;\r\nmask = [170,251,101,210,23,101,10,242,193,163,174,148,104,138,228,245,52,234,0,195];\r\nassert(isequal(mgf(mgfSeed,maskLen),mask))\r\n%%\r\nmgfSeed = 'People who succeed have momentum. The more they succeed, the more they want to succeed and the more they find a way to succeed. Similarly, when someone is failing, the tendency is to get on a downward spiral that can even become a self-fulfilling prophecy.';\r\nmaskLen = 825;\r\nmask = [40    55   251    83   143   211    49    16    87    56   109   116    17   188    72    54   231    67   125   182   223    53   149    36   246   233    71   113   113    29   176    61    74    77   205   167   179    99   172   172   212   249    53   148    13    89   253     3    85     0   155   181    94    15     8   122   120    92    29   229   229    46   192   162   252   119   243    94    52    12   133    98   161   249   152    68    69    22 156   155   229   132    30   163   102    90   217   160    36    53   185   128    74   224   105   214   134    90   188   101    98   220    43   162   251   183    72    54   219     4    44   185   199   193    31    42   129   112    74    25    81   133    70   156    38   225   243    24    50   132    79   130    42    72   145   158   105   253   190    62    73   107    95   133    78   238   214    39    88   245    40    46   166   201   173   209   234   194     3   117   226    32   176    12   139    83   211    67   101    99   174   126   103   130    83    56   157    52    76    21   189    99   217    43   150    92   219   148   173   138   184   183   203    62   231   125    42    79    29   248    30    26   127    50     9    31   219   212   160   157   243    15    52   217     7   144    43   241    70    98   247   231   174   111    96    91   108    59   164   146    77   180   212   155   165    18   150   198   100   233   255   129    44   170   150   243   244   126   203   255   231    28    94    77    58   198   147    84   212   248    36    34   107   210    77    79    80   224    24   126    88   125   255    11   124   227    44   253   242   234    59   176   108   146   170   132    20   128   230   141   187    81   139    26   119    47   159     8   182    40    94   213   107   223    21   187    35     1    40   145   154    92   218   252    46   201   203    65    48    94   118   110     5   158   212    19   129     9    59   124    19   190   201    55   141   124   210    85   240    34   150    81   154    11     3   116   204    37    73    39   169   234    50   241   131    94     9   133     0   209   190   171   206    93   165   145   142   117   230    34   245   223   205   153   227   202     3   218    56   163   203   137    46   114    78   248   196   136    19   177   126    95   231   213   251   130    39    57    59   228   251    73    48     9   109    11   172   152   223   120   153   108   233    76   117   219     8     6   124    44     7   249   116    43   198    98    55   235    52    47   146   239    76   142    88    19    64   169   209   206   255   101    91   197    36   180   253    52    54    63   198    63   113    58    43   229   141    59     4   147    34   200     7   184   108   176   153    95    94    18   235   178   160    51    63   154   227    39   231    71   246   109    13   199   185   220   180    50   146    85   194   176   175   159   103   177    47   149   252   182    33   224   226     1   232   167    94    93   202    55    60   201   228   107   135   142    22   165   168   124   219   216    34   230   174   179    66    67    91    92   186    30    48     9   177   192   254   190   236    52    84   114   255     3    81   150   112   131    49   157    48   243   218    11   132    40    13    68   214   159    36   109   152   219    32   219   193   182    69    37   104   217   115    54   158    79    90     3   223   116   127     8    28   233   223   194    30   241   180    45   209    77   180   184    52   132   164    49   206    50    57   149   118   105    44    25   212   141   253   150    45   244    73   203    10   137   161   118    49   165   248   105   215    58    90    67   180    78    22   135   102   173   139    62    31    63    82   187    78    47     2   176   144    83    22   143    31   155     8   160    12   252   164    52   159   102   113    95   132    49   168   194   137    32   131   189   139    77   143   248    36    30   154   239   163   149    86     9   178   122     6   248    25   208   211   142    59    23    72    43   158   195   244   232   229   245   148    49   163    28    81    93   106    33   239   249    26   220    73    65   112   249   254   251    91   106   204   113    10    59    34   114   189   185   188   181   100   185    22   188   221   187   109   170    35    57   181   234   157   230   206   169    97   140    51   170   128   215   188    14    50   190   167    41   173    19    10    98   165    12    77    49    21    33   147    93    84   163   106   151   234    76   252    91   165   248   223   136    85   124   174    90   188   130   174    83    92   181   134    65    91   105    15   103    91    15    27   201   162    58    84   164    33   137    63     0    84     0    78   180    31   218    47    84    43    13    35   122   117   205    59    81   146    97    14];\r\nassert(isequal(mgf(mgfSeed,maskLen),mask))\r\n%%\r\nmgfSeed = 'First, have a definite, clear practical ideal; a goal, an objective. Second, have the necessary means to achieve your ends; wisdom, money, materials, and methods.';\r\nmaskLen = 300;\r\nmask = [96   185   223   209   149    51   224   128   249   139    57   249   190    69   199   132    37    24    75   127    98    59   231   206    13    83    79   111   181   220   204   120    27   178   155   116   155    11   157   112    10   195   106     4   127   146   101   112   197    29    54    45    14    78    16   100     1   111   156    44   138   141   219   101   171     1   126   254    60    82   214    63    13    94   227   238    64   173    93   142     3   224    63    76     8   190   105     8    77   113   250   243   249    82    56   124   129   116   121   131   207   116    80   185    72   184   244   232   236   127    37   195   236   163   176   245    65    48   169   131    19    36   208   178   184   150   188    50   221    83   132   241    73   205    23     9    41    74    65   251    10    21   133   150   101    15    42   153   164   197   136    19   113   134   153   247    10   161   221   184   195   215   253   149   223    83   180   148   198     4    53   112   114    39    18   132   254   170   130    61     6   158   224   166    76   187   190   128    14   251   158   218   192    68    46   210   199   231   120    57   212    10   107     2    50    37   110    30    87    48     2   134    81   165   107    95   250   206    98    26    45   102   173    46    85   103   137     4   140   154   236   142   125   211    28   164    94    41     8    70   215    10    13   140   139    97   205    60   212   205    35   175   235   158    88   165    40    41   187   215    72   172    97   159   119    79    80    31   128   121   108    94   219   216    77   209   184   244    90   238   182   207   244    52   248     6   220   175   117   122   236   228   219    42    49   196   186    12    19    95];\r\nassert(isequal(mgf(mgfSeed,maskLen),mask))","published":true,"deleted":false,"likes_count":1,"comments_count":4,"created_by":145982,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":5,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2019-09-06T22:10:21.000Z","updated_at":"2026-05-25T02:42:38.000Z","published_at":"2019-09-06T22:31:17.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCreate Mask Generation Function (MGF1) from PKCS #1 v2.2 standard (B.2.1) at below link. Input will be character array (mgfSeed) which will need to be converted to octet array using UTF-8 representation. Output must be octet array (uint8) of length, maskLen (input). Hashing must use SHA-1 (160 - 20 byte).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[mgfSeed = 'I like to swim.';%input\\nmaskLen = 20;%input\\nmask = [170,251,101,210,23,101,10,242,193,163,174,148,104,138,228,245,52,234,0,195];%output]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSee: https://www.foo.be/docs/opensst/ref/pkcs/pkcs-1/pkcs-1v2-1d1.pdf\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44765,"title":"Lights Out 11 - 5x5, with wrapping, x moves","description":"\u003chttps://en.wikipedia.org/wiki/Lights_Out_(game) Lights Out\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves the first problem in the series\u003e for an introduction.\r\n\r\nThis problem contains boards that each require any number of moves to solve. However, now wrapping of the lights occurs. For example, if \r\n\r\n board = [1 0 0 0 1  \r\n          1 1 1 0 1  \r\n          0 1 1 1 0  \r\n          1 1 1 0 0  \r\n          0 0 0 1 1]\r\n\r\nthe answer is:\r\n\r\n moves = [2 4 13 25]\r\n\r\nPrev.: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44764 5x5, wrapping, 6 moves\u003e — \r\nNext: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44766 5x5, 3 stages, \u003c7 moves\u003e","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Lights_Out_(game)\"\u003eLights Out\u003c/a\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\"\u003ethe first problem in the series\u003c/a\u003e for an introduction.\u003c/p\u003e\u003cp\u003eThis problem contains boards that each require any number of moves to solve. However, now wrapping of the lights occurs. For example, if\u003c/p\u003e\u003cpre\u003e board = [1 0 0 0 1  \r\n          1 1 1 0 1  \r\n          0 1 1 1 0  \r\n          1 1 1 0 0  \r\n          0 0 0 1 1]\u003c/pre\u003e\u003cp\u003ethe answer is:\u003c/p\u003e\u003cpre\u003e moves = [2 4 13 25]\u003c/pre\u003e\u003cp\u003ePrev.: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44764\"\u003e5x5, wrapping, 6 moves\u003c/a\u003e — \r\nNext: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44766\"\u003e5x5, 3 stages, \u0026lt;7 moves\u003c/a\u003e\u003c/p\u003e","function_template":"function moves = lights_out_11(board) % 5x5 board, with wrapping, any number of moves\r\n moves = board;\r\nend","test_suite":"%% \r\n board = [1 0 0 0 1  \r\n          1 1 1 0 1  \r\n          0 1 1 1 0  \r\n          1 1 1 0 0  \r\n          0 0 0 1 1];\r\nmoves = lights_out_11(board); % [2 4 13 25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 0 1 1  \r\n          1 0 0 0 1  \r\n          0 0 0 0 0  \r\n          1 0 0 0 1  \r\n          1 1 0 1 1];\r\nmoves = lights_out_11(board); % [1 5 21 25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 0 1 0  \r\n          1 0 1 0 1  \r\n          0 1 0 1 0  \r\n          1 0 1 0 1  \r\n          0 1 0 1 0];\r\nmoves = lights_out_11(board); % [2 6 8 12 14 18 20 24]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 0 0 0  \r\n          0 1 0 1 1  \r\n          0 0 0 0 1  \r\n          0 0 0 1 1  \r\n          1 0 0 1 1];\r\nmoves = lights_out_11(board); % [3 7 17 21 23:25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          1 1 0 1 1  \r\n          1 0 1 0 1  \r\n          1 1 0 1 1  \r\n          0 1 1 1 0];\r\nmoves = lights_out_11(board); % [7:9 12:14 17:19]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          0 1 1 1 0  \r\n          0 1 1 1 0  \r\n          0 1 1 1 0  \r\n          0 1 1 1 0];\r\nmoves = lights_out_11(board); % [11:15]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 1 0 0  \r\n          0 0 1 0 0  \r\n          1 1 1 1 1  \r\n          1 0 0 1 0  \r\n          1 1 0 1 0];\r\nmoves = lights_out_11(board); % [9 11 14 21 23 25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 0 1 1  \r\n          1 1 0 1 1  \r\n          0 0 1 0 0  \r\n          1 1 0 1 1  \r\n          1 1 0 1 1];\r\nmoves = lights_out_11(board); % on your own\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 0 1 0 1  \r\n          0 0 1 0 1  \r\n          0 0 1 0 1  \r\n          0 0 1 0 1  \r\n          0 0 1 0 1];\r\nmoves = lights_out_11(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 0 0 0 1  \r\n          0 1 1 1 0  \r\n          0 1 1 1 0  \r\n          0 1 1 1 0  \r\n          1 0 0 0 1];\r\nmoves = lights_out_11(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":13,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":624,"created_at":"2018-10-31T16:37:46.000Z","updated_at":"2026-05-25T02:30:59.000Z","published_at":"2019-05-02T12:20:39.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lights_Out_(game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLights Out\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic game wherein all lights need to be turned off to complete each board. See\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethe first problem in the series\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for an introduction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem contains boards that each require any number of moves to solve. However, now wrapping of the lights occurs. For example, if\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ board = [1 0 0 0 1  \\n          1 1 1 0 1  \\n          0 1 1 1 0  \\n          1 1 1 0 0  \\n          0 0 0 1 1]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ethe answer is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ moves = [2 4 13 25]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrev.:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44764\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, wrapping, 6 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e — Next:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44766\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, 3 stages, \u0026lt;7 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":57477,"title":"Solve an equation involving primes and fractions","description":"Write a function to find pairs of primes  and  satisfying the equation\r\n\r\nwhere  is an integer. The function should take a number  as input and produce the triples , , and  such that . If there are no solutions, the function should return three empty vectors.\r\nThis problem is adapted from one in the 2012 European Girls’ Math Olympiad. ","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 146px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 343px 73px; transform-origin: 343px 73px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 320px 10.5px; text-align: left; transform-origin: 320px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eWrite a function to find pairs of primes \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ep\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eq\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e satisfying the equation\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 35px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 320px 17.5px; text-align: left; transform-origin: 320px 17.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"vertical-align:-15px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"136\" height=\"35\" alt=\"p/(p+1) + (q+1)/q = 2n/(n+2)\" style=\"width: 136px; height: 35px;\"\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 320px 21px; text-align: left; transform-origin: 320px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003ewhere \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e is an integer. The function should take a number \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ex\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e as input and produce the triples \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ep\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eq\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e, and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e such that \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"38\" height=\"18\" alt=\"p \u003c= x\" style=\"width: 38px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e. If there are no solutions, the function should return three empty vectors.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 320px 10.5px; text-align: left; transform-origin: 320px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThis problem is adapted from one in the \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.egmo2012.org.uk/\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"text-decoration: underline; text-decoration-line: underline; \"\u003e2012 European Girls’ Math Olympiad\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [p,q,n] = EGMO2012no5(x)\r\n  p = primes(x); q = primes(x); n = p./(p+1) + (q+1)./q; \r\nend","test_suite":"%%\r\nx = 1;\r\n[p,q,n] = EGMO2012no5(x);\r\nassert(isempty(p) \u0026\u0026 isempty(q) \u0026\u0026 isempty(n))\r\n\r\n%%\r\nx = 2;\r\n[p,q,n] = EGMO2012no5(x);\r\nassert(all(p==2) \u0026\u0026 isequal(q,[5 7]) \u0026\u0026 isequal(n,[28 19]))\r\n\r\n%%\r\nx = 20;\r\n[p,q,n] = EGMO2012no5(x);\r\ns_correct = [35 28 86 178 646 1402];\r\nassert(isequal(p+q+n,s_correct))\r\n\r\n%%\r\nx = 200;\r\n[p,q,n] = EGMO2012no5(x);\r\ns_correct = [35 28 86 178 646 1402 3778 7306 14758 21166 42226 47302 77002 90898 130678 148606 158002];\r\nassert(isequal(p+q+n,s_correct))\r\n\r\n%%\r\nx = 2000;\r\n[p,q,n] = EGMO2012no5(x);\r\nlen_correct = 63;\r\nsum_correct = 265170305;\r\nassert(isequal(length(p),len_correct) \u0026\u0026 isequal(sum(p+q+n),sum_correct))\r\n\r\n%%\r\nx = 20000;\r\n[p,q,n] = EGMO2012no5(x);\r\nlen_correct = 344;\r\nsum_correct = 150118037395;\r\nassert(isequal(length(p),len_correct) \u0026\u0026 isequal(sum(p+q+n),sum_correct))\r\nassert(all(isprime(p)) \u0026\u0026 all(isprime(q)))\r\n\r\n%%\r\nx = 2000000;\r\n[p,q,n] = EGMO2012no5(x);\r\nlen_correct = 14873;\r\nsum_correct = 27402595128;\r\nassert(isequal(length(p),len_correct) \u0026\u0026 isequal(sum(p+q),sum_correct))\r\nassert(all(isprime(p)) \u0026\u0026 all(isprime(q)))\r\n\r\n%%\r\nx = 2e8;\r\n[p,q,n] = EGMO2012no5(x);\r\nlen_correct = 813373;\r\nsum_correct = 152663390088360;\r\nassert(isequal(length(p),len_correct) \u0026\u0026 isequal(sum(p+q),sum_correct))\r\n\r\n%%\r\nfiletext = fileread('EGMO2012no5.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'regexp'); \r\nassert(~illegal)","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":46909,"edited_by":46909,"edited_at":"2022-12-30T13:15:49.000Z","deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-12-30T05:04:32.000Z","updated_at":"2026-05-25T01:33:07.000Z","published_at":"2022-12-30T05:05:56.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to find pairs of primes \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"p\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ep\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"q\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eq\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e satisfying the equation\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"p/(p+1) + (q+1)/q = 2n/(n+2)\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\frac{p}{p+1} + \\\\frac{q+1}{q} = \\\\frac{2n}{n+2}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhere \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is an integer. The function should take a number \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"x\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e as input and produce the triples \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"p\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ep\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"q\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eq\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"n\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e such that \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"p \u0026lt;= x\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ep \\\\le x\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. If there are no solutions, the function should return three empty vectors.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem is adapted from one in the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.egmo2012.org.uk/\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e2012 European Girls’ Math Olympiad\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44263,"title":"Multivariate polynomials - emulate symbolic form","description":"In \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44262-multivariate-polynomials-overload-multiplication Problem 44262\u003e I asked you to create a class |mPoly| with overloaded multiplication, so a product of two polynomials can be expressed in the form |p = p1*p2|. However, the method of constructing these polynomials is still somewhat unintuitive. In the \u003chttps://www.mathworks.com/products/symbolic.html Symbolic Math Toolbox\u003e, one can simply define some variables,\r\n\r\n  syms x y z\r\n\r\nand then create a polynomial:\r\n\r\n  p = 2*x*y + 3*x^5*z;\r\n\r\nWe would like to do something like that here. As a start, create a class |mPolySym| with properties |exponents| and |coefficients|, and |varnames|,  where the first two properties are the same as in previous problems and |varnames| is a \u003chttps://www.mathworks.com/help/matlab/characters-and-strings.html string array\u003e. The constructor should accept a numeric, char or string input, e.g.,\r\n\r\n  x = mPolySym('x')\r\n\r\n  x = \r\n\r\n  mPolySym with properties:\r\n\r\n        varnames: \"x\"\r\n       exponents: 1\r\n    coefficients: 1\r\n\r\n  r = mPolySym(pi)\r\n\r\n  r = \r\n\r\n  mPolySym with properties:\r\n\r\n        varnames: [0×0 string]\r\n       exponents: 1\r\n    coefficients: 3.1416\r\n\r\nAlso modify the method |mtimes| from the previous problem so it can multiply polynomials with different variable names.","description_html":"\u003cp\u003eIn \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44262-multivariate-polynomials-overload-multiplication\"\u003eProblem 44262\u003c/a\u003e I asked you to create a class \u003ctt\u003emPoly\u003c/tt\u003e with overloaded multiplication, so a product of two polynomials can be expressed in the form \u003ctt\u003ep = p1*p2\u003c/tt\u003e. However, the method of constructing these polynomials is still somewhat unintuitive. In the \u003ca href = \"https://www.mathworks.com/products/symbolic.html\"\u003eSymbolic Math Toolbox\u003c/a\u003e, one can simply define some variables,\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003esyms x y z\r\n\u003c/pre\u003e\u003cp\u003eand then create a polynomial:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003ep = 2*x*y + 3*x^5*z;\r\n\u003c/pre\u003e\u003cp\u003eWe would like to do something like that here. As a start, create a class \u003ctt\u003emPolySym\u003c/tt\u003e with properties \u003ctt\u003eexponents\u003c/tt\u003e and \u003ctt\u003ecoefficients\u003c/tt\u003e, and \u003ctt\u003evarnames\u003c/tt\u003e,  where the first two properties are the same as in previous problems and \u003ctt\u003evarnames\u003c/tt\u003e is a \u003ca href = \"https://www.mathworks.com/help/matlab/characters-and-strings.html\"\u003estring array\u003c/a\u003e. The constructor should accept a numeric, char or string input, e.g.,\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003ex = mPolySym('x')\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003ex = \r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003emPolySym with properties:\r\n\u003c/pre\u003e\u003cpre\u003e        varnames: \"x\"\r\n       exponents: 1\r\n    coefficients: 1\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003er = mPolySym(pi)\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003er = \r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003emPolySym with properties:\r\n\u003c/pre\u003e\u003cpre\u003e        varnames: [0×0 string]\r\n       exponents: 1\r\n    coefficients: 3.1416\u003c/pre\u003e\u003cp\u003eAlso modify the method \u003ctt\u003emtimes\u003c/tt\u003e from the previous problem so it can multiply polynomials with different variable names.\u003c/p\u003e","function_template":"classdef mPolySym\r\n    properties\r\n        varnames\r\n        exponents\r\n        coefficients\r\n    end\r\n    \r\n    methods\r\n        function p = mPolySym(s)\r\n        end\r\n        \r\n        function p = mtimes(p1,p2)\r\n        end            \r\n    end\r\n    \r\nend\r\n\r\n","test_suite":"%% Test mPolySym\r\nfiletext = fileread('mPolySym.m');\r\nassert(~contains(filetext,'regexp'))\r\n\r\n\r\n%%\r\nr = randi(1000);\r\nx = mPolySym(r);\r\nassert(isempty(x.varnames))\r\nassert(isequal(x.exponents,0))\r\nassert(isequal(x.coefficients,r))\r\n\r\n%%\r\nr = randi(1000);\r\nx = mPolySym('x');\r\ny = r*x;\r\nassert(isequal(y.varnames,\"x\"))\r\nassert(isequal(y.exponents,1))\r\nassert(isequal(y.coefficients,r))\r\nassert(isequal(r*x,x*r))\r\n\r\n%%\r\nx = mPolySym('x');\r\ny = mPolySym(\"y\");\r\nz = mPolySym('z');\r\nw = x*y*z;\r\nassert(isequal(w.varnames,[\"x\" \"y\" \"z\"]))\r\nassert(isequal(w.exponents,[1 1 1]))\r\nassert(isequal(w.coefficients,1))\r\n\r\n%%\r\nm = randi(5);\r\nn = randi(4);\r\nx = mPolySym(\"x\");\r\ny = mPolySym(\"y\");\r\np = [repmat(x,1,m) repmat(y,1,n)];\r\np = p(randperm(length(p)));\r\nr = randi(1000);\r\np_prod = r;\r\nfor ii=1:length(p)\r\n    p_prod = p_prod*p(ii);\r\nend\r\ns = randi(1000);\r\np_prod = p_prod*s;\r\nassert(isequal(p_prod.varnames,[\"x\" \"y\"]))\r\nassert(isequal(p_prod.exponents,[m n]))\r\nassert(isequal(p_prod.coefficients,r*s))","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":1011,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":9,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2017-07-14T23:13:17.000Z","updated_at":"2026-05-25T00:58:31.000Z","published_at":"2017-07-14T23:13:34.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44262-multivariate-polynomials-overload-multiplication\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 44262\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e I asked you to create a class\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003emPoly\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e with overloaded multiplication, so a product of two polynomials can be expressed in the form\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ep = p1*p2\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. However, the method of constructing these polynomials is still somewhat unintuitive. In the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/products/symbolic.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSymbolic Math Toolbox\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, one can simply define some variables,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[syms x y z]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eand then create a polynomial:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[p = 2*x*y + 3*x^5*z;]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWe would like to do something like that here. As a start, create a class\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003emPolySym\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e with properties\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eexponents\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecoefficients\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003evarnames\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, where the first two properties are the same as in previous problems and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003evarnames\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/help/matlab/characters-and-strings.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003estring array\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. The constructor should accept a numeric, char or string input, e.g.,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[x = mPolySym('x')\\n\\nx = \\n\\nmPolySym with properties:\\n\\n        varnames: \\\"x\\\"\\n       exponents: 1\\n    coefficients: 1\\n\\nr = mPolySym(pi)\\n\\nr = \\n\\nmPolySym with properties:\\n\\n        varnames: [0×0 string]\\n       exponents: 1\\n    coefficients: 3.1416]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAlso modify the method\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003emtimes\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e from the previous problem so it can multiply polynomials with different variable names.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2511,"title":" BLOCK x3 (Version 4) ","description":"Always in this series ( \u003chttp://www.mathworks.com/matlabcentral/cody/problems/2451-block-x3-version-1/ 2451\u003e, \u003chttp://www.mathworks.com/matlabcentral/cody/problems/2484-block-x3-version-3/ 2484\u003e, and \u003chttp://www.mathworks.com/matlabcentral/cody/problems/2478-block-x3-version-2 2478\u003e ).\r\n\r\nNow we are in color (1 for red  and 2 for white).\r\nYour task is always to count the  *minimum number* of movements required to align the 3 blocks in each colors (vertically or horizontally).\r\n\r\n\r\n\r\n\u003c\u003chttp://4.bp.blogspot.com/-iveYgRwOmLs/Udk9ZctxnUI/AAAAAAAA3Uw/Z9XNHw6f-f4/s400/pack+2+level+2-1.png\u003e\u003e\r\n\r\n* [0 0 0 0 0 0 0;\r\n*  0 0 1 2 0 0 0;\r\n*  0 0 0 0 0 0 0;\r\n*  0 0 1 2 0 0 0;\r\n*  0 0 1 2 0 0 0;\r\n*  0 0 0 0 0 0 0;\r\n*  0 0 0 0 0 0 0]\r\n\r\nIn this example you can move down the first two blocks ( *in two moves* ).\r\n\r\n\r\nNote that blocks can be *swapped* . \r\n\r\n\r\nIn this second example :\r\n\r\n\u003c\u003chttp://1.bp.blogspot.com/-XJP6Mn4X9Ws/Udk9bHEKx_I/AAAAAAAA3Vo/V_Ar3oF9gYw/s400/pack+2+level+2-7.png\u003e\u003e\r\n\r\n* [0 0 0 0 0 0 0;\r\n*  0 0 0 0 0 0 0;\r\n*  0 0 1 2 0 0 0;\r\n*  0 0 2 1 0 0 0;\r\n*  0 0 1 2 0 0 0;\r\n*  0 0 0 0 0 0 0;\r\n*  0 0 0 0 0 0 0]\r\n\r\nHere you win in only *one move* by swapping the two central blocks.\r\n\r\nGood luck !","description_html":"\u003cp\u003eAlways in this series ( \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/2451-block-x3-version-1/\"\u003e2451\u003c/a\u003e, \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/2484-block-x3-version-3/\"\u003e2484\u003c/a\u003e, and \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/2478-block-x3-version-2\"\u003e2478\u003c/a\u003e ).\u003c/p\u003e\u003cp\u003eNow we are in color (1 for red  and 2 for white).\r\nYour task is always to count the  \u003cb\u003eminimum number\u003c/b\u003e of movements required to align the 3 blocks in each colors (vertically or horizontally).\u003c/p\u003e\u003cimg src = \"http://4.bp.blogspot.com/-iveYgRwOmLs/Udk9ZctxnUI/AAAAAAAA3Uw/Z9XNHw6f-f4/s400/pack+2+level+2-1.png\"\u003e\u003cul\u003e\u003cli\u003e[0 0 0 0 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 1 2 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 0 0 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 1 2 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 1 2 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 0 0 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 0 0 0 0 0]\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eIn this example you can move down the first two blocks ( \u003cb\u003ein two moves\u003c/b\u003e ).\u003c/p\u003e\u003cp\u003eNote that blocks can be \u003cb\u003eswapped\u003c/b\u003e .\u003c/p\u003e\u003cp\u003eIn this second example :\u003c/p\u003e\u003cimg src = \"http://1.bp.blogspot.com/-XJP6Mn4X9Ws/Udk9bHEKx_I/AAAAAAAA3Vo/V_Ar3oF9gYw/s400/pack+2+level+2-7.png\"\u003e\u003cul\u003e\u003cli\u003e[0 0 0 0 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 0 0 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 1 2 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 2 1 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 1 2 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 0 0 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 0 0 0 0 0]\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eHere you win in only \u003cb\u003eone move\u003c/b\u003e by swapping the two central blocks.\u003c/p\u003e\u003cp\u003eGood luck !\u003c/p\u003e","function_template":"function y = block3_4(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = [0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 1 2 0 0 0;0 0 0 0 0 0 0;0 0 1 2 0 0 0;0 0 1 2 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3_4(x),y_correct))\r\n\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 0 2 1 0 0;0 0 0 1 2 0 0;0 0 0 2 1 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 1;\r\nassert(isequal(block3_4(x),y_correct))\r\n\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 2 1 2 0 0;0 0 1 2 1 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 1;\r\nassert(isequal(block3_4(x),y_correct))\r\n\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 1 0 1 1 0;0 0 2 2 0 2 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3_4(x),y_correct))\r\n\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 1 1 2 0 0;0 0 2 2 1 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 1;\r\nassert(isequal(block3_4(x),y_correct))\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 1 1 0 0 0;0 0 2 2 0 0 0;0 0 2 1 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3_4(x),y_correct))\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 1 0 0 0;0 0 2 0 1 0 0;0 0 1 0 2 0 0;0 0 0 2 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 3;\r\nassert(isequal(block3_4(x),y_correct))\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 1 0 0 0;0 0 1 2 1 0 0;0 0 2 0 2 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3_4(x),y_correct))\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 1 0 0 0;0 0 0 1 0 0 0;0 0 1 2 0 0 0;0 0 2 0 0 0 0;0 0 2 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 1;\r\nassert(isequal(block3_4(x),y_correct))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":5390,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2014-08-15T07:15:35.000Z","updated_at":"2026-05-25T00:45:42.000Z","published_at":"2014-08-15T07:17:45.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.png\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/media/image2.png\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAlways in this series (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/2451-block-x3-version-1/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e2451\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/2484-block-x3-version-3/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e2484\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/2478-block-x3-version-2\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e2478\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e ).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNow we are in color (1 for red and 2 for white). Your task is always to count the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eminimum number\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e of movements required to align the 3 blocks in each colors (vertically or horizontally).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[0 0 0 0 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 1 2 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 0 0 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 1 2 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 1 2 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 0 0 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 0 0 0 0 0]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this example you can move down the first two blocks (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ein two moves\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e ).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNote that blocks can be\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eswapped\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e .\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this second example :\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId2\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[0 0 0 0 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 0 0 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 1 2 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 2 1 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 1 2 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 0 0 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 0 0 0 0 0]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHere you win in only\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eone move\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e by swapping the two central blocks.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGood luck !\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray 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\"},{\"partUri\":\"/media/image2.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\"}]}"},{"id":2451,"title":"BLOCK x3 (Version 1)","description":"\r\n\u003chttps://play.google.com/store/apps/details?id=com.noodlecake.blockblock BLOCK x3\u003e is a simple, fun and relaxing puzzle game.\r\n\r\nThe basics are easy. Solve the puzzles by getting *3 blocks* in a row or column. \r\n\r\nThe puzzle is coded a 6x6 matrix and the 3 blocks are indicated by 1 (and 0 for free space).\r\n\r\nIn this example :\r\n\r\n\u003c\u003chttp://1.bp.blogspot.com/-GbZ-AMjylSs/Udk6kRyp4uI/AAAAAAAA3Pk/3jdWrVSqQpc/s400/pack+1+level+1-1.png\u003e\u003e\r\n \r\n \r\n\r\n* [0 0 0 0 0 0\r\n* \r\n* 0 0 1 0 0 0\r\n* \r\n* 0 0 1 0 0 0\r\n* \r\n* 0 0 0 0 0 0\r\n* \r\n* 0 0 1 0 0 0\r\n* \r\n* 0 0 0 0 0 0]\r\n\r\nYou line up 3 blocks in one move by dragging the last block to the top (you can move a block in the free space only horizontally or vertically.)\r\n\r\n\r\n\r\nThe goal in this first problem is to give the *smallest number* of moves to solve the puzzle.\r\n\r\n\r\n\r\n","description_html":"\u003cp\u003e\u003ca href = \"https://play.google.com/store/apps/details?id=com.noodlecake.blockblock\"\u003eBLOCK x3\u003c/a\u003e is a simple, fun and relaxing puzzle game.\u003c/p\u003e\u003cp\u003eThe basics are easy. Solve the puzzles by getting \u003cb\u003e3 blocks\u003c/b\u003e in a row or column.\u003c/p\u003e\u003cp\u003eThe puzzle is coded a 6x6 matrix and the 3 blocks are indicated by 1 (and 0 for free space).\u003c/p\u003e\u003cp\u003eIn this example :\u003c/p\u003e\u003cimg src = \"http://1.bp.blogspot.com/-GbZ-AMjylSs/Udk6kRyp4uI/AAAAAAAA3Pk/3jdWrVSqQpc/s400/pack+1+level+1-1.png\"\u003e\u003cul\u003e\u003cli\u003e[0 0 0 0 0 0\u003c/li\u003e\u003cli\u003e\u003c/li\u003e\u003cli\u003e0 0 1 0 0 0\u003c/li\u003e\u003cli\u003e\u003c/li\u003e\u003cli\u003e0 0 1 0 0 0\u003c/li\u003e\u003cli\u003e\u003c/li\u003e\u003cli\u003e0 0 0 0 0 0\u003c/li\u003e\u003cli\u003e\u003c/li\u003e\u003cli\u003e0 0 1 0 0 0\u003c/li\u003e\u003cli\u003e\u003c/li\u003e\u003cli\u003e0 0 0 0 0 0]\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eYou line up 3 blocks in one move by dragging the last block to the top (you can move a block in the free space only horizontally or vertically.)\u003c/p\u003e\u003cp\u003eThe goal in this first problem is to give the \u003cb\u003esmallest number\u003c/b\u003e of moves to solve the puzzle.\u003c/p\u003e","function_template":"function y = block3(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = [0 0 0 0 0 0;0 0 0 0 0 0;0 0 1 0 0 0;0 0 1 0 0 0;0 0 0 1 0 0;0 0 0 0 0 0];\r\ny_correct = 1;\r\nassert(isequal(block3(x),y_correct))\r\n%%\r\nx = [0 0 0 0 0 0;0 0 0 0 0 0;0 0 1 0 0 0;0 1 0 0 0 0;0 0 1 0 0 0;0 0 0 0 0 0];\r\ny_correct = 1;\r\nassert(isequal(block3(x),y_correct))\r\n%%\r\nx = [0 0 0 0 0 0;0 0 0 0 0 0;0 0 1 0 0 0;0 0 0 1 0 0;0 0 0 0 1 0;0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3(x),y_correct))\r\n%%\r\nx = [0 0 0 0 0 0;0 0 1 0 0 0;0 0 0 0 0 0;0 0 1 0 0 0;0 0 0 0 0 0;0 0 1 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3(x),y_correct))\r\n%%\r\nx = [0 0 0 0 0 0;0 0 0 0 0 0;0 1 0 1 1 0;0 0 0 0 0 0;0 0 0 0 0 0;0 0 0 0 0 0];\r\ny_correct = 1;\r\nassert(isequal(block3(x),y_correct))\r\n%%\r\nx = [0 0 0 0 0 0;0 0 0 0 0 0;0 0 0 1 1 0;0 0 0 0 0 0;0 0 1 0 0 0;0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3(x),y_correct))\r\n%%\r\nx = [0 0 0 0 0 0;0 0 0 0 0 0;0 0 0 1 1 0;0 0 0 1 0 0;0 0 0 0 0 0;0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3(x),y_correct))\r\n%%\r\nx = [0 0 0 0 0 0;0 0 0 0 0 0;0 0 0 1 0 0;0 0 0 1 1 0;0 0 0 0 0 0;0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3(x),y_correct))\r\n%%\r\nx = [0 0 0 0 0 0;0 0 1 0 1 0;0 0 0 0 0 0;0 0 0 0 0 0;0 0 0 1 0 0;0 0 0 0 0 0];\r\ny_correct = 3;\r\nassert(isequal(block3(x),y_correct))\r\n%%\r\nx = [0 0 0 0 0 0;0 0 1 0 1 0;0 0 1 0 0 0;0 0 0 0 0 0;0 0 0 0 0 0;0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3(x),y_correct))","published":true,"deleted":false,"likes_count":0,"comments_count":1,"created_by":5390,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":11,"test_suite_updated_at":"2014-07-20T00:15:18.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-07-19T23:58:55.000Z","updated_at":"2026-05-25T00:43:28.000Z","published_at":"2014-07-20T00:15:18.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.png\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://play.google.com/store/apps/details?id=com.noodlecake.blockblock\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eBLOCK x3\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a simple, fun and relaxing puzzle game.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe basics are easy. 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\"}]}"},{"id":2478,"title":"BLOCK x3 (Version 2)","description":"An extension to problem 2451 ( \u003chttps://www.mathworks.com/matlabcentral/cody/problems/2451-block-x3-version-1\u003e ).\r\n\r\nIt is based on an android game - BLOCK x3. The objective is to align the 1's in the matrix using minimum movements. The valid movements are  horizontal and vertical (by one step). A zero (0) indicates an empty space. Your task is to count minimum number of movements required to align the three 1's vertically or horizontally.\r\n\r\nNew challenge in this problem : There are some static elements which can not be moved and can not be included in the alignment. These elements are indicated by 2. This makes the problem a bit challenging.\r\n\r\nExample:\r\n\r\n\r\n  Input =[0 0 0 0 0 0 0 0;\r\n          0 0 1 0 1 0 0 0;\r\n          0 0 0 2 1 0 0 0;\r\n          0 0 0 0 0 0 0 0]\r\n  \r\n  Output = 2;\r\n","description_html":"\u003cp\u003eAn extension to problem 2451 ( \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/2451-block-x3-version-1\"\u003ehttps://www.mathworks.com/matlabcentral/cody/problems/2451-block-x3-version-1\u003c/a\u003e ).\u003c/p\u003e\u003cp\u003eIt is based on an android game - BLOCK x3. The objective is to align the 1's in the matrix using minimum movements. The valid movements are  horizontal and vertical (by one step). A zero (0) indicates an empty space. Your task is to count minimum number of movements required to align the three 1's vertically or horizontally.\u003c/p\u003e\u003cp\u003eNew challenge in this problem : There are some static elements which can not be moved and can not be included in the alignment. These elements are indicated by 2. This makes the problem a bit challenging.\u003c/p\u003e\u003cp\u003eExample:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eInput =[0 0 0 0 0 0 0 0;\r\n        0 0 1 0 1 0 0 0;\r\n        0 0 0 2 1 0 0 0;\r\n        0 0 0 0 0 0 0 0]\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eOutput = 2;\r\n\u003c/pre\u003e","function_template":"function y = blockx3_2(x)\r\n\r\nend","test_suite":"%%\r\n\r\nx =  [0 0 0 0 0 0 0 0;\r\n      0 0 1 0 1 0 0 0;\r\n      0 0 0 2 1 0 0 0;\r\n      0 0 0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(blockx3_2(x),y_correct));\r\n\r\n\r\n%%\r\nx =  [0 0 0 0 0 0 0 0;\r\n      0 0 1 0 1 0 0 0;\r\n      0 0 0 2 0 0 0 0;\r\n      0 0 1 0 0 0 0 0];\r\ny_correct = 3;\r\nassert(isequal(blockx3_2(x),y_correct))\r\n\r\n%%\r\nx =  [1 1 0 0 0 0 0 0;\r\n      1 2 0 0 0 0 0 0;\r\n      0 0 0 0 0 0 0 0;\r\n      0 0 0 0 0 0 0 0];\r\ny_correct = 3;\r\nassert(isequal(blockx3_2(x),y_correct))\r\n\r\n%%\r\nx =  [0 0 0 1 0 0 0 0;\r\n      0 0 1 2 1 0 0 0;\r\n      0 0 0 0 0 0 0 0;\r\n      0 0 0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(blockx3_2(x),y_correct))\r\n\r\n\r\n%%\r\nx =  [0 0 0 1 0 0 0 0;\r\n      0 0 1 2 0 0 0 0;\r\n      0 0 1 0 0 0 0 0;\r\n      0 0 0 0 0 0 0 0]\r\ny_correct = 1;\r\nassert(isequal(blockx3_2(x),y_correct))\r\n\r\n\r\n%%\r\nx =  [0 0 0 1 0 0 0 0;\r\n      0 0 1 2 0 0 0 0;\r\n      0 0 0 0 0 0 0 1;\r\n      0 0 0 0 0 0 0 0]\r\ny_correct = 6;\r\nassert(isequal(blockx3_2(x),y_correct))\r\n\r\n\r\n%%\r\nx =  [0 0 0 1 0 0 0 0;\r\n      0 0 0 2 0 0 0 0;\r\n      0 0 0 1 0 0 0 0;\r\n      0 0 0 1 0 0 0 0]\r\ny_correct = 4;\r\nassert(isequal(blockx3_2(x),y_correct))\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":17203,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":"2014-08-03T08:43:51.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-08-03T08:31:00.000Z","updated_at":"2026-05-25T00:44:08.000Z","published_at":"2014-08-03T08:31:00.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAn extension to problem 2451 (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/2451-block-x3-version-1\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://www.mathworks.com/matlabcentral/cody/problems/2451-block-x3-version-1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt; ).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIt is based on an android game - BLOCK x3. The objective is to align the 1's in the matrix using minimum movements. The valid movements are horizontal and vertical (by one step). A zero (0) indicates an empty space. Your task is to count minimum number of movements required to align the three 1's vertically or horizontally.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNew challenge in this problem : There are some static elements which can not be moved and can not be included in the alignment. These elements are indicated by 2. This makes the problem a bit challenging.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[Input =[0 0 0 0 0 0 0 0;\\n        0 0 1 0 1 0 0 0;\\n        0 0 0 2 1 0 0 0;\\n        0 0 0 0 0 0 0 0]\\n\\nOutput = 2;]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":56538,"title":"Cricket - Report the Result (Part II: Test Matches)","description":"Given two scalar strings representing the scores for a test match, return a string reporting the result in the appropriate form:\r\n\"Match drawn\"\r\n\"TeamName won by n runs\"\r\n\"TeamName won by n wickets\"\r\n\"TeamName won by an innings and n runs\"\r\nThe strings will be given in the order the teams batted and will have the form \"TeamName r/w \u0026 r/w\" (where r is runs and w is wickets). If a team is all out, their score will be given as just \"r\" (rather than \"r/10\"). The convention \"d\" is used for declared innings (eg \"432/1d\") and \"(f/o)\" for following on (eg \"England 123 \u0026 234 (f/o)\").\r\nGiven that tied Tests are very rare (and drawn Tests with the scores level equally rare), you do not need to worry about scores being level. You can also be grammatically incorrect for a win by 1 wicket or 1 run (ie you can report the result as \"... won by 1 wickets\", rather than making a special case for \"1 wicket\"). Other than that, your function will need to cope with all other possibilities, such as \"West Indies 123 \u0026 456/7d (f/o)\".\r\nThe strings will always use single spaces between the team name and the scores, but team names may be multiple words (\"Sri Lanka\", \"South Africa\", etc) which will also be separated by single spaces. The two innings scores will be separated by \"\u0026\".\r\nFor a match to have a result (not a draw), the losing team must have completed two innings with their total score still below the winning team’s score. If the winning team is batting last, their margin of victory will be in terms of wickets. Otherwise, it will be in terms of runs. If they batted only one innings, their victory will be given as an innings and runs. All possible permutations of how this can occur are summarized in the table below.\r\n\r\n\r\ns1 = \"India 250 \u0026 307\";\r\ns2 = \"Australia 235 \u0026 291\";\r\nres = reportresult(s1,s2)\r\nres = \r\n    \"India won by 31 runs\"\r\n\r\n\r\ns1 = \"South Africa 573/4d\";\r\ns2 = \"Bangladesh 147 \u0026 172 (f/o)\";\r\nres = reportresult(s1,s2)\r\nres = \r\n    \"South Africa won by an innings and 254 runs\"\r\n    \r\nTest matches are played over a fixed time period. If the match is not completed in that time, the result is a draw (regardless of the score).\r\nTo avoid running out of time (thus resulting in a draw), a team can choose to declare their innings completed before they are all out. This is effectively a gamble to forfeit the remainder of their innings for the sake of time.\r\nAfter both teams' first innings, if Team B is sufficiently far (200+ runs) behind Team A, A can choose to make B follow-on, meaning Team B has to bat again immediately, while Team A can keep their second innings in reserve if needed. Hence, the order of innings is A, B, B, A (if necessary), instead of the usual A, B, A, B.\r\nWith these rules, you can always figure out the order of play, and the result, given the team scores. For example:\r\nTeam A 543/6d \u0026 123/4\r\nTeam B 234 \u0026 456/7d (f/o)\r\nTeam A scores 543 and declares with 4 wickets still in hand\r\nTeam B scores 234 (all out)\r\nTeam B, forced to follow-on (309 runs behind), scores 456 and declares with 3 wickets in hand, giving A a target of 148 to win\r\nTeam A scores 123 for the loss of 4 wickets, leaving them 24 runs behind Team B's total\r\nResult: match drawn, because the team with the lower score (A) had not yet completed their second innings.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 1713.07px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 856.533px; transform-origin: 407px 856.533px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 373.5px 8px; transform-origin: 373.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven two scalar strings representing the scores for a test match, return a string reporting the result in the appropriate form:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 81.7333px; counter-reset: list-item 0; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 40.8667px; transform-origin: 391px 40.8667px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 45px 8px; transform-origin: 45px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\"Match drawn\"\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 2.5px 8px; transform-origin: 2.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 36px 8px; transform-origin: 36px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eTeamName\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 26px 8px; transform-origin: 26px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e won by \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 18.5px 8px; transform-origin: 18.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e runs\"\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 2.5px 8px; transform-origin: 2.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 36px 8px; transform-origin: 36px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eTeamName\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 26px 8px; transform-origin: 26px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e won by \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 27px 8px; transform-origin: 27px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e wickets\"\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 2.5px 8px; transform-origin: 2.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 36px 8px; transform-origin: 36px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eTeamName\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 74.5px 8px; transform-origin: 74.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e won by an innings and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 18.5px 8px; transform-origin: 18.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e runs\"\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 244px 8px; transform-origin: 244px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe strings will be given in the order the teams batted and will have the form \"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 36px 8px; transform-origin: 36px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eTeamName\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2.5px 8px; transform-origin: 2.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003er\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e/\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4.5px 8px; transform-origin: 4.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ew\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8.5px 8px; transform-origin: 8.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u0026amp; \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2.5px 8px; transform-origin: 2.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003er\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e/\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4.5px 8px; transform-origin: 4.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ew\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 28px 8px; transform-origin: 28px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\" (where \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2.5px 8px; transform-origin: 2.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003er\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 39px 8px; transform-origin: 39px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is runs and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4.5px 8px; transform-origin: 4.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ew\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.5px 8px; transform-origin: 1.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is wickets). If a team is all out, their score will be given as just \"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2.5px 8px; transform-origin: 2.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003er\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 46.5px 8px; transform-origin: 46.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\" (rather than \"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2.5px 8px; transform-origin: 2.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003er\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 116px 8px; transform-origin: 116px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e/10\"). The convention \"d\" is used for declared innings (eg \"432/1d\") and \"(f/o)\" for following on (eg \"England 123 \u0026amp; 234 (f/o)\").\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 375.5px 8px; transform-origin: 375.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven that tied Tests are very rare (and drawn Tests with the scores level equally rare), you do not need to worry about scores being level. You can also be grammatically incorrect for a win by 1 wicket or 1 run (ie you can report the result as \"... won by 1 wickets\", rather than making a special case for \"1 wicket\"). Other than that, your function will need to cope with all other possibilities, such as \"West Indies 123 \u0026amp; 456/7d (f/o)\".\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 382.5px 8px; transform-origin: 382.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe strings will always use single spaces between the team name and the scores, but team names may be multiple words (\"Sri Lanka\", \"South Africa\", etc) which will also be separated by single spaces. The two innings scores will be separated by \"\u0026amp;\".\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 384px 8px; transform-origin: 384px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor a match to have a result (not a draw), the losing team must have completed two innings with their total score still below the winning team’s score. If the winning team is batting last, their margin of victory will be in terms of wickets. Otherwise, it will be in terms of runs. If they batted only one innings, their victory will be given as an innings and runs. All possible permutations of how this can occur are summarized in the table below.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 516.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 258.25px; text-align: left; transform-origin: 384px 258.25px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 586px;height: 511px\" 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\" alt=\"Table shows 6 permutations of innings and results. (1) Team A bats first and third, Team B bats second and fourth. Team A wins. The margin is given as \u0026quot;n runs\u0026quot;. (2) Team A bats first and third, Team B bats second and fourth. Team B wins (with the fourth innings still in progress). The margin is given as \u0026quot;n wickets\u0026quot;. (3) Team A bats first and fourth, Team B bats second and third (following on). Team A wins (with the fourth innings still in progress). The margin is given as \u0026quot;n wickets\u0026quot;. (4) Team A bats first and fourth, Team B bats second and third (following on). Team B wins. The margin is given as \u0026quot;n runs\u0026quot;. (5) Team A bats first and third, Team B bats second (only one innings). Team B wins. The margin is given as \u0026quot;an innings and n runs\u0026quot;. (6) Team A bats first (only one innings), Team B bats second and third (following on). Team A wins. The margin is given as \u0026quot;an innings and n runs\u0026quot;.\" data-image-state=\"image-loaded\" width=\"586\" height=\"511\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 8px; transform-origin: 0px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 265.633px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 132.817px; transform-origin: 404px 132.817px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 92px 8.5px; tab-size: 4; transform-origin: 92px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 20px 8.5px; transform-origin: 20px 8.5px; \"\u003es1 = \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 68px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 68px 8.5px; \"\u003e\"India 250 \u0026amp; 307\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 108px 8.5px; tab-size: 4; transform-origin: 108px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 20px 8.5px; transform-origin: 20px 8.5px; \"\u003es2 = \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 84px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 84px 8.5px; \"\u003e\"Australia 235 \u0026amp; 291\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100px 8.5px; tab-size: 4; transform-origin: 100px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eres = reportresult(s1,s2)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 24px 8.5px; tab-size: 4; transform-origin: 24px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eres = \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 104px 8.5px; tab-size: 4; transform-origin: 104px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 16px 8.5px; transform-origin: 16px 8.5px; \"\u003e    \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 88px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 88px 8.5px; \"\u003e\"India won by 31 runs\"\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 108px 8.5px; tab-size: 4; transform-origin: 108px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 20px 8.5px; transform-origin: 20px 8.5px; \"\u003es1 = \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 84px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 84px 8.5px; \"\u003e\"South Africa 573/4d\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 136px 8.5px; tab-size: 4; transform-origin: 136px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 20px 8.5px; transform-origin: 20px 8.5px; \"\u003es2 = \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 112px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 112px 8.5px; \"\u003e\"Bangladesh 147 \u0026amp; 172 (f/o)\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100px 8.5px; tab-size: 4; transform-origin: 100px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eres = reportresult(s1,s2)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 24px 8.5px; tab-size: 4; transform-origin: 24px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eres = \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 196px 8.5px; tab-size: 4; transform-origin: 196px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 16px 8.5px; transform-origin: 16px 8.5px; \"\u003e    \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 180px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 180px 8.5px; \"\u003e\"South Africa won by an innings and 254 runs\"\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 16px 8.5px; tab-size: 4; transform-origin: 16px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e    \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cul style=\"block-size: 143.033px; counter-reset: list-item 0; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 71.5167px; transform-origin: 391px 71.5167px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 40.8667px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 20.4333px; text-align: left; transform-origin: 363px 20.4333px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 331.5px 8px; transform-origin: 331.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTest matches are played over a fixed time period. If the match is not completed in that time, the result is a \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 15px 8px; transform-origin: 15px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003edraw\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e (regardless of the score).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 40.8667px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 20.4333px; text-align: left; transform-origin: 363px 20.4333px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 241.5px 8px; transform-origin: 241.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTo avoid running out of time (thus resulting in a draw), a team can choose to \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 23.5px 8px; transform-origin: 23.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003edeclare\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 98px 8px; transform-origin: 98px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e their innings completed before they are all out. This is effectively a gamble to forfeit the remainder of their innings for the sake of time.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 61.3px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 30.65px; text-align: left; transform-origin: 363px 30.65px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 347.5px 8px; transform-origin: 347.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAfter both teams' first innings, if Team B is sufficiently far (200+ runs) behind Team A, A can choose to make B \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 28px 8px; transform-origin: 28px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003efollow-on\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 330.5px 8px; transform-origin: 330.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, meaning Team B has to bat again immediately, while Team A can keep their second innings in reserve if needed. Hence, the order of innings is A, B, B, A (if necessary), instead of the usual A, B, A, B.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 354.5px 8px; transform-origin: 354.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWith these rules, you can always figure out the order of play, and the result, given the team scores. For example:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 74px 8px; transform-origin: 74px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTeam A 543/6d \u0026amp; 123/4\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 83px 8px; transform-origin: 83px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTeam B 234 \u0026amp; 456/7d (f/o)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003col style=\"block-size: 102.167px; counter-reset: list-item 0; font-family: Helvetica, Arial, sans-serif; list-style-type: decimal; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 51.0833px; transform-origin: 391px 51.0833px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 185.5px 8px; transform-origin: 185.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTeam A scores 543 and declares with 4 wickets still in hand\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 86.5px 8px; transform-origin: 86.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTeam B scores 234 (all out)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 40.8667px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 20.4333px; text-align: left; transform-origin: 363px 20.4333px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 361.5px 8px; transform-origin: 361.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTeam B, forced to follow-on (309 runs behind), scores 456 and declares with 3 wickets in hand, giving A a target of 148 to win\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 275.5px 8px; transform-origin: 275.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTeam A scores 123 for the loss of 4 wickets, leaving them 24 runs behind Team B's total\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 339.5px 8px; transform-origin: 339.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eResult: match drawn, because the team with the lower score (A) had not yet completed their second innings.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function res = reportresult(s1,s2)\r\nres = s1+s2;\r\nend","test_suite":"%% Draw, 4th innings in progress\r\ns1 = \"Sri Lanka 253 \u0026 342\";\r\ns2 = \"West Indies 300 \u0026 147/5\";\r\nres = reportresult(s1,s2);\r\nassert( isequal(res,\"Match drawn\") )\r\n%% Draw, team 2 following on\r\ns1 = \"India 622/7d\";\r\ns2 = \"Australia 300 \u0026 6/0 (f/o)\";\r\nres = reportresult(s1,s2);\r\nassert( isequal(res,\"Match drawn\") )\r\n%% Draw, 3rd innings in progress\r\ns1 = \"Sri Lanka 282 \u0026 287/3\";\r\ns2 = \"New Zealand 578\";\r\nres = reportresult(s1,s2);\r\nassert( isequal(res,\"Match drawn\") )\r\n%% Draw, only 2 innings (yawn)\r\ns1 = \"India 537/8d\";\r\ns2 = \"Sri Lanka 952/6d\";\r\nres = reportresult(s1,s2);\r\nassert( isequal(res,\"Match drawn\") )\r\n%% Four completed innings, Team 1 wins (#1 in table)\r\ns1 = \"India 250 \u0026 307\";\r\ns2 = \"Australia 235 \u0026 291\";\r\nres = reportresult(s1,s2);\r\nassert( isequal(res,\"India won by 31 runs\") )\r\n%% Four completed innings, Team 1 wins by a single run (#1 in table)\r\ns1 = \"West Indies 252 \u0026 146\";\r\ns2 = \"Australia 213 \u0026 184\";\r\nres = reportresult(s1,s2);\r\nassert( startsWith(res,\"West Indies won by 1 run\") )\r\n%% Team 1 declared (#1 in table)\r\ns1 = \"New Zealand 178 \u0026 585/4d\";\r\ns2 = \"Sri Lanka 104 \u0026 236\";\r\nres = reportresult(s1,s2);\r\nassert( isequal(res,\"New Zealand won by 423 runs\") )\r\n%% Four innings in order, Team 2 wins (#2 in table)\r\ns1 = \"Pakistan 181 \u0026 190\";\r\ns2 = \"South Africa 223 \u0026 151/4\";\r\nres = reportresult(s1,s2);\r\nassert( isequal(res,\"South Africa won by 6 wickets\") )\r\n%% Four innings in order, Team 2 wins by a single wicket (#2 in table)\r\ns1 = \"Pakistan 269 \u0026 219\";\r\ns2 = \"West Indies 273 \u0026 216/9\";\r\nres = reportresult(s1,s2);\r\nassert( startsWith(res,\"West Indies won by 1 wicket\") )\r\n%% Team 1 wins after Team 2 follows on (#3 in table)\r\ns1 = \"Pakistan 310/9d \u0026 160/5\";\r\ns2 = \"Ireland 130 \u0026 339 (f/o)\";\r\nres = reportresult(s1,s2);\r\nassert( isequal(res,\"Pakistan won by 5 wickets\") )\r\n%% Team 2 wins after following on (#4 in table)\r\ns1 = \"Australia 401/9d \u0026 111\";\r\ns2 = \"England 174 \u0026 356 (f/o)\";\r\nres = reportresult(s1,s2);\r\nassert( isequal(res,\"England won by 18 runs\") )\r\n%% Team 2 wins after following on and declaring! (#4 in table)\r\ns1 = \"Australia 445 \u0026 212\";\r\ns2 = \"India 171 \u0026 657/7d (f/o)\";\r\nres = reportresult(s1,s2);\r\nassert( isequal(res,\"India won by 171 runs\") )\r\n%% Innings victory for Team 2 (#5 in table)\r\ns1 = \"Sri Lanka 144 \u0026 139\";\r\ns2 = \"Australia 323\";\r\nres = reportresult(s1,s2);\r\nassert( isequal(res,\"Australia won by an innings and 40 runs\") )\r\n%% Innings victory for Team 2 with declaration (#5 in table)\r\ns1 = \"Bangladesh 211 \u0026 209\";\r\ns2 = \"New Zealand 432/6d\";\r\nres = reportresult(s1,s2);\r\nassert( isequal(res,\"New Zealand won by an innings and 12 runs\") )\r\n%% Innings victory for Team 1 (#6 in table)\r\ns1 = \"India 474\";\r\ns2 = \"Afghanistan 109 \u0026 103 (f/o)\";\r\nres = reportresult(s1,s2);\r\nassert( isequal(res,\"India won by an innings and 262 runs\") )","published":true,"deleted":false,"likes_count":1,"comments_count":16,"created_by":287,"edited_by":287,"edited_at":"2022-11-13T04:10:24.000Z","deleted_by":null,"deleted_at":null,"solvers_count":14,"test_suite_updated_at":"2022-11-13T04:10:24.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2022-11-07T20:56:16.000Z","updated_at":"2026-05-24T22:31:43.000Z","published_at":"2022-11-08T15:17:22.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven two scalar strings representing the scores for a test match, return a string reporting the result in the appropriate form:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\"Match drawn\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eTeamName\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e won by \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e runs\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eTeamName\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e won by \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e wickets\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eTeamName\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e won by an innings and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e runs\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe strings will be given in the order the teams batted and will have the form \\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eTeamName\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003er\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e/\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ew\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u0026amp; \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003er\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e/\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ew\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e\\\" (where \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003er\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is runs and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ew\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is wickets). If a team is all out, their score will be given as just \\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003er\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e\\\" (rather than \\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003er\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e/10\\\"). The convention \\\"d\\\" is used for declared innings (eg \\\"432/1d\\\") and \\\"(f/o)\\\" for following on (eg \\\"England 123 \u0026amp; 234 (f/o)\\\").\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven that tied Tests are very rare (and drawn Tests with the scores level equally rare), you do not need to worry about scores being level. You can also be grammatically incorrect for a win by 1 wicket or 1 run (ie you can report the result as \\\"... won by 1 wickets\\\", rather than making a special case for \\\"1 wicket\\\"). Other than that, your function will need to cope with all other possibilities, such as \\\"West Indies 123 \u0026amp; 456/7d (f/o)\\\".\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe strings will always use single spaces between the team name and the scores, but team names may be multiple words (\\\"Sri Lanka\\\", \\\"South Africa\\\", etc) which will also be separated by single spaces. The two innings scores will be separated by \\\"\u0026amp;\\\".\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor a match to have a result (not a draw), the losing team must have completed two innings with their total score still below the winning team’s score. If the winning team is batting last, their margin of victory will be in terms of wickets. Otherwise, it will be in terms of runs. If they batted only one innings, their victory will be given as an innings and runs. All possible permutations of how this can occur are summarized in the table below.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"511\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"586\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"Table shows 6 permutations of innings and results. (1) Team A bats first and third, Team B bats second and fourth. Team A wins. The margin is given as \u0026quot;n runs\u0026quot;. (2) Team A bats first and third, Team B bats second and fourth. Team B wins (with the fourth innings still in progress). The margin is given as \u0026quot;n wickets\u0026quot;. (3) Team A bats first and fourth, Team B bats second and third (following on). Team A wins (with the fourth innings still in progress). The margin is given as \u0026quot;n wickets\u0026quot;. (4) Team A bats first and fourth, Team B bats second and third (following on). Team B wins. The margin is given as \u0026quot;n runs\u0026quot;. (5) Team A bats first and third, Team B bats second (only one innings). Team B wins. The margin is given as \u0026quot;an innings and n runs\u0026quot;. (6) Team A bats first (only one innings), Team B bats second and third (following on). Team A wins. The margin is given as \u0026quot;an innings and n runs\u0026quot;.\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[s1 = \\\"India 250 \u0026 307\\\";\\ns2 = \\\"Australia 235 \u0026 291\\\";\\nres = reportresult(s1,s2)\\nres = \\n    \\\"India won by 31 runs\\\"\\n\\n\\ns1 = \\\"South Africa 573/4d\\\";\\ns2 = \\\"Bangladesh 147 \u0026 172 (f/o)\\\";\\nres = reportresult(s1,s2)\\nres = \\n    \\\"South Africa won by an innings and 254 runs\\\"\\n    ]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTest matches are played over a fixed time period. If the match is not completed in that time, the result is a \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edraw\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (regardless of the score).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo avoid running out of time (thus resulting in a draw), a team can choose to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edeclare\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e their innings completed before they are all out. This is effectively a gamble to forfeit the remainder of their innings for the sake of time.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAfter both teams' first innings, if Team B is sufficiently far (200+ runs) behind Team A, A can choose to make B \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003efollow-on\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, meaning Team B has to bat again immediately, while Team A can keep their second innings in reserve if needed. Hence, the order of innings is A, B, B, A (if necessary), instead of the usual A, B, A, B.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWith these rules, you can always figure out the order of play, and the result, given the team scores. For example:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTeam A 543/6d \u0026amp; 123/4\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTeam B 234 \u0026amp; 456/7d (f/o)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTeam A scores 543 and declares with 4 wickets still in hand\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTeam B scores 234 (all out)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTeam B, forced to follow-on (309 runs behind), scores 456 and declares with 3 wickets in hand, giving A a target of 148 to win\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTeam A scores 123 for the loss of 4 wickets, leaving them 24 runs behind Team B's total\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eResult: match drawn, because the team with the lower score (A) had not yet completed their second innings.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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from bearing angles in 2D","description":"A robot moving on the plane has a sensor that measures the bearing angle to two mapped landmarks, that is, the world frame coordinates of the landmarks are known and represented by position vectors |P1| and |P2| .  The bearing angles to the landmarks, with respect to the x-axis of the robot's coordinate frame are |th1| and |th2| respectively.  The robot's forward direction is parallel to its x-axis and its heading angle, with respect to magnetic north is |thb| .\r\n\r\nDetermine the pose of the robot expressed as a homogeneous transformation matrix with respect to the world coordinate frame.  In surveying this is known as a resection problem.","description_html":"\u003cp\u003eA robot moving on the plane has a sensor that measures the bearing angle to two mapped landmarks, that is, the world frame coordinates of the landmarks are known and represented by position vectors \u003ctt\u003eP1\u003c/tt\u003e and \u003ctt\u003eP2\u003c/tt\u003e .  The bearing angles to the landmarks, with respect to the x-axis of the robot's coordinate frame are \u003ctt\u003eth1\u003c/tt\u003e and \u003ctt\u003eth2\u003c/tt\u003e respectively.  The robot's forward direction is parallel to its x-axis and its heading angle, with respect to magnetic north is \u003ctt\u003ethb\u003c/tt\u003e .\u003c/p\u003e\u003cp\u003eDetermine the pose of the robot expressed as a homogeneous transformation matrix with respect to the world coordinate frame.  In surveying this is known as a resection problem.\u003c/p\u003e","function_template":"function T = user_function(P1, P2, th1, th2, thb)\r\n% Input:  P1 a 2x1 vector representing the coordinate of a point\r\n%         P2 a 2x1 vector representing the coordinate of a point\r\n%         th1 bearing, a scalar angle\r\n%         th2 bearing, a scalar angle\r\n%         thb heading, a scalar angle\r\n% Output: T a 3x3 homogeneous transformation matrix\r\n  T = ;\r\nend","test_suite":"%\r\nP1 = [10 20]';\r\nP2 = [20 20]';\r\nP = rand(2,1)*5 + [5 5]';\r\nthb = rand*0.2;\r\nx = P1 - P;\r\nth1 = atan2(x(2), x(1)) - thb;\r\nx = P2 - P;\r\nth2 = atan2(x(2), x(1)) - thb;\r\n\r\nT = user_function(P1, P2, th1, th2, thb);\r\n\r\n%% test size and complexity\r\nassert(all(size(T)==3), 'The matrix must be 3x3');\r\nassert(isreal(T), 'The matrix must be real, not complex');\r\n\r\n%% bottom row\r\nassert(isequal(T(3,:), [0 0 1]), 'The bottom row of the homogeneous transformation matrix is not correct')\r\n\r\n%% x coordinate\r\nassert(abs(T(1,3)-P(1))\u003c1e-4, 'The representation of the x-coordinate is not correct')\r\n\r\n%% y coordinate\r\nassert(abs(T(2,3)-P(2))\u003c1e-4, 'The representation of the y-coordinate is not correct')\r\n\r\n%% valid rotation matrix\r\nR = T(1:2,1:2);\r\nassert( abs(det(R)-1) \u003c 1e-4, 'The determinant of the rotation submatrix is not correct')\r\n\r\n%% correct rotation matrix\r\nR = T(1:2,1:2);\r\nassert( abs(atan2(R(2,1), R(1,1)) - thb) \u003c 1e-4, 'The rotation matrix is not correct, check your calculation of the heading SSW and whether you are using radians or degrees')\r\n\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":13332,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":22,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":77,"created_at":"2019-01-21T00:32:56.000Z","updated_at":"2026-05-24T23:32:59.000Z","published_at":"2019-01-21T00:37:35.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA robot moving on the plane has a sensor that measures the bearing angle to two mapped landmarks, that is, the world frame coordinates of the landmarks are known and represented by position vectors\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eP1\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eP2\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e . The bearing angles to the landmarks, with respect to the x-axis of the robot's coordinate frame are\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eth1\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eth2\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e respectively. The robot's forward direction is parallel to its x-axis and its heading angle, with respect to magnetic north is\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ethb\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e .\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDetermine the pose of the robot expressed as a homogeneous transformation matrix with respect to the world coordinate frame. In surveying this is known as a resection problem.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":60461,"title":"Generate a point cloud on an equilateral triangle 2","description":"The input is the iteration parameter H.\r\nThe output is a point cloud W involving N points.\r\nW is N uniformly distributed points on an equilateral triangle.\r\nThe side length of an equilateral triangle is 2.\r\nThe relationship between H and N is as follows:\r\nH = [1 2 3 4 5 6 7 8 9];\r\nN = [4 10 19 31 46 64 85 109 136];\r\nThe results for cases where H is 1 to 6 are as follows.\r\n\r\n\r\nEx)\r\n[W,N] = lattice2(H=1) -\u003e W = [0 0; 1 sqrt(3)/3; 1 sqrt(3); 2 0]; N = 4;\r\n[W,N] = lattice2(H=2) -\u003e W = [0 0; 0.5 sqrt(3)/6; 0.5 sqrt(3)/2; 1 0; 1 sqrt(3)/3; 1 sqrt(4/3); 1 sqrt(3); 1.5 sqrt(3)/6; 1.5 sqrt(3)/2; 2 0]; N = 10;","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 749.188px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 374.594px; transform-origin: 407px 374.594px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe input is the iteration parameter\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eH\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe output is a point cloud\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eW\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003einvolving\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003epoints.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eW\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eis\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003euniformly distributed points on an equilateral triangle.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe side length of an equilateral triangle is 2.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe relationship between H and N is as follows:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 40.875px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 20.4375px; transform-origin: 391px 20.4375px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2188px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eH = [1 2 3 4 5 6 7 8 9];\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2188px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eN = [4 10 19 31 46 64 85 109 136];\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe results for cases where H is 1 to 6 are as follows.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 197px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 98.5px; text-align: left; transform-origin: 384px 98.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" width=\"193\" height=\"191\" style=\"vertical-align: baseline;width: 193px;height: 191px\" 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\" 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\" 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\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eEx)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 64.3125px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 32.1562px; transform-origin: 391px 32.1562px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 21.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.7188px; text-align: left; transform-origin: 363px 10.7188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e[W,N] = lattice2(H=1) -\u0026gt; W = [\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003e0 0; 1 sqrt(3)/3; 1 sqrt(3); 2 0]\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e; N = 4;\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 42.875px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 21.4375px; text-align: left; transform-origin: 363px 21.4375px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e[W,N] = lattice2(H=2) -\u0026gt; W = [\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003e0 0; 0.5 sqrt(3)/6; 0.5 \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003esqrt(3)/2; 1 0; 1 \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003esqrt(3)/3; 1 sqrt(4/3); 1 sqrt(3); 1.5 sqrt(3)/6; 1.5 \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003esqrt(3)/2; 2 0]; N = 10;\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [W,N] = lattice2(H)\r\n  W = [0 0; 1 sqrt(3); 2 0];\r\n  N = size(W,1);\r\nend","test_suite":"%%\r\nH = 1;\r\nA = [0 0; 1 sqrt(3)/3; 1 sqrt(3); 2 0];\r\n\r\n[W,N] = lattice2(H);\r\nassert(isequal(N,4))\r\nassert(sum(abs(sortrows(W)-sortrows(A)),'all') \u003c 1e-6)\r\n\r\n%%\r\nH = 2;\r\nA = [0 0; 0.5 sqrt(3)/6; 0.5 sqrt(3)/2; 1 0; 1 sqrt(3)/3;\r\n    1 sqrt(4/3); 1 sqrt(3); 1.5 sqrt(3)/6; 1.5 sqrt(3)/2; 2 0];\r\n\r\n[W,N] = lattice2(H);\r\nassert(isequal(N,10))\r\nassert(sum(abs(sortrows(W)-sortrows(A)),'all') \u003c 1e-6)\r\n\r\n%%\r\nH = 3;\r\n[~,N] = lattice2(H);\r\nassert(isequal(N,19))\r\n\r\n%%\r\nH = 4;\r\n[~,N] = lattice2(H);\r\nassert(isequal(N,31))\r\n\r\n%%\r\nH = 5;\r\n[~,N] = lattice2(H);\r\nassert(isequal(N,46))\r\n\r\n%%\r\nH = 10;\r\n[~,N] = lattice2(H);\r\nassert(isequal(N,166))\r\n\r\n%%\r\nH = 100;\r\n[~,N] = lattice2(H);\r\nassert(isequal(N,15151))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":2217275,"edited_by":2217275,"edited_at":"2024-06-09T21:16:00.000Z","deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":"2024-06-09T21:08:07.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2024-06-09T14:14:29.000Z","updated_at":"2026-05-24T21:53:50.000Z","published_at":"2024-06-09T14:24:00.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe input is the iteration parameter\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eH\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe output is a point cloud\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eW\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003einvolving\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003epoints.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eW\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eis\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003euniformly distributed points on an equilateral triangle.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe side length of an equilateral triangle is 2.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe relationship between H and N is as follows:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eH = [1 2 3 4 5 6 7 8 9];\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eN = [4 10 19 31 46 64 85 109 136];\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe results for cases where H is 1 to 6 are as follows.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"191\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"193\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"191\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"193\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId2\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"191\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"193\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId3\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"191\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"193\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId4\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"191\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"193\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId5\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"191\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"193\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId6\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eEx)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[W,N] = lattice2(H=1) -\u0026gt; W = [\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e0 0; 1 sqrt(3)/3; 1 sqrt(3); 2 0]\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e; N = 4;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[W,N] = lattice2(H=2) -\u0026gt; W = [\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e0 0; 0.5 sqrt(3)/6; 0.5 \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003esqrt(3)/2; 1 0; 1 \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esqrt(3)/3; 1 sqrt(4/3); 1 sqrt(3); 1.5 sqrt(3)/6; 1.5 \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003esqrt(3)/2; 2 0]; N = 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i0Y2DCMQCFg0OAAnIgkBsBdLaza5XM66wQE4EUkIgL1omlZbW2tFr3smk6mrq2N1DMBEJCEAttPY2JjJZEwfNp1Oh0Ih04cF4GgkIQC2EwgEamtrDcMwccxMJlNbW8smIQCTkIQA2FEoFBoYGDBxW086nW5sbDRrNAAVgyQEwI7q6uoaGhoGBgZMGS2VSp133nkUhABMRRICYFNqd3PxW6fVla6RSMSMSQGoNCQhADal4ks2my0mDKXT6Ww2SwwCMBOSEAD70jStubl5+fLl3d3dc90zlMvlUqnU2NjYrbfeSuc8gJksKvcEAGA2mqY1NjZ6vd7u7m6fz1fIXp9cLjc0NDQ4ONjY2Lhx48YSTBKAc1ETAmB3mqZtzGZburtVcWhgYGCmBvtcLpdOp7u7u8fGxppfeGHj0qUlnioAx6EmBMAJYjEtHo+Ew4Zh9Pb2Dg4OplIpj8dTXV3t8XhyuZzKRpqmhUKhG2+8UUTk//0/iUblwIEyzxyAvS0YGxsr9xxERILB4ODgYLlnAcCWYjFJJqWnZ+JrKvoYhmEYhnbc5G+sr5dwWFpbSzZTAE6RDx7UhADYXlvbpBgkxy9qPclW6Hhc6uulqUn8fssmB8DZ2CcEwN7q6yUSkXB4Pt/r90s4LLGYyVMCUEGoCQGwsWRSkkkpZhG/tVXq6yWZnGeWAlDpqAkBsLFYTOLxokbw+6W1VaJRkyYEoNKQhADYVSIhIlL88dDhsPj946MBwIlIQgDsKhYzp+1LlYXYLQRgOiQhALYUi0k4bNrmHlUWYo0MwBTsmAZgP7oubW0mH4qoOup1nY56ABNREwJgP9GotLWZHFn8folEKAsBmIQkBMBmVOe8FQdDNzWJrksyaf7IAByLJATAZorvnJ8JHfUApiAJAbATszrnZxKJ0FEPYCJ2TAOwk2h06hVjJlNbp60LWwAchZoQANuIRud/xVjh1GVkrJEBEBFqQgDsQtclkTC5c34mXEYG4DhqQgDswYrO+Zlw6jSA40hCAGwgmRRdt6RzfibhMB31AIQkBMAWolGrOudn4vdLPM5uIQAzJqHBwcEihx4dHf3LX/6yd+/ekZGRIocCUMkSifFdzCWmLiNjjQxwt2l2TD///PM7duzYv3//Cy+8ML9Be3p67rvvvmw229DQsGbNmuJmCKDSlaBzfiaqo76picvIANc6IQn19/ffeeedfX19IyMjixcvnsdwhw4d+sEPfvDcc8/dfPPNGzduNGmSACpXaTrnZ6JqUdadag3A9k5YHTv77LPj8XhLS8v8xnr99dcvvfTSF1988YEHHiAGATi5ZFISiTKnkNbW8ZvOALjSCUnI6/WKyMqVK+cx0KFDh7761a++8cYbO3fuXL16tTmzA1DZ7FCMoaMecLdpdkxXVVXNY6Bvfetbb7zxxpYtW84///yiZwXABRIJ0XVb3Hqh1ua4jAxwJXO66B966KFnnnmmurp6y5YtpgwIoPLZoSCkUBYCXMycJLRz504R+cpXvrJ06dLDhw/v3bu3v79/dHTUlMEBVKBYrDyd8zOhox5wKxPuHevr63vttddEZOXKlZs3b/7Tn/505MiRo0ePaprW0tJy8cUXFzhOMBic+mLxxxoBsB1dl7a2snXOz4SOeqByTZsxFBOS0B//+Ef1h1dfffUnP/nJihUrjh079tOf/vTee+/9zne+s2jRoi984QuFjEPoAdyivJ3zM/H7JRKx0ZodAPNMzRj5bGTC6tjQ0JCIrF69+uabb16xYoWIVFVVbdu27dxzzxWRWCzGMhmA96mWdXumjaYmOuoBtzEhCR06dEhEPvKRj0x6PRqNikg2m927d2/xPwVAhbBz0UVtneYyMsBNTEhC6jTqD37wg5Ner6+vV394++23i/8pACqB6lS3Q+f8TNTWaTrqAdcwIQktW7ZMRKZes1pdXV1dXV38+AAqRywmra3lnsSs6KgHXMaEJPSxj31MRF5++eVpRl+4UEROO+204n8KAMeLxSQctt1G6anUJFkjA9zBhCSk+uT/9re/HTx4cNJbx44dO+2000KhUPE/BYCzqc55mxeE8riMDHCN+SSh/fv3b9++/c0331T/6Pf7L7vsMhF55JFHJn7ZCy+8cPTo0S1btqjKEABXi0alrc0xR/XkO+oBVLppMkoulxORkZGRY8eOTX13dHR006ZNu3fv3rp1a/7FrVu3rlmzpqOj45VXXlGvvPvuu7fccstnP/vZzZs3WzNzAM6RTIquO6YgpDQ1ia5TFgIq3gknK/b19T322GN79uwRkZGRkauuuuqCCy6IRCJnnHHGxC+rqak5cuTI8uXL8694PJ54PN7S0nLFFVd87WtfO/XUUx999NG6urobb7yxNH8NALZm5875meQ76g8cKPdUAFhowdjYWLnnICISDAY5YxqoTImE3HOP7e7WKJC6f8PObf8A5iUfPEy4bQMAZhONOjUGyfHLyEhCQOViLzMAK9nzirHC+f101AOVjZoQAMvouiQSjt9n09oq9fWSTDo4zwGYGTUhAJZxVuf8TDh1GqhoJCEA1nBi5/xMVDWIjnqgEpGEAFgjGnVe5/xMuKMeqFwkIQAWiMXG9xpXDHVHPWtkQMUhCQGwgIOuGCtcPC6JhOh6uecBwEwkIQBmc3rn/ExUlYuyEFBZ6KIHYKpkUhIJscfh9eZrbZVVq6SpqQJzHuBW1IQAmMqJV4wVzu+XeJyyEFBJSEIAzKO20VT23RSqGpRIlHcWAMxCEgJgnsouCCkctAhUFpIQAJNUXuf8TFRHPccLARWBHdMAzKDr0tbm+CvGCqfuqNd1x98lArgeNSEAZqiMK8YK5/dLJMIaGVABSEIAipZMSjJZgUcpzq6pafwvDsDJSEIAiuaGjdJTcRkZUBFIQgCKo/rJK7tzfiZq6zQd9YCTkYQAFCcWc926WB4d9YDzkYQAFCEWk3DYFZ3zM1F/fdbIAMeiix7AfLmtc34mra1SXy/JpKsTIeBY1IQAzJfbOudnwhoZ4GQkIQDzkkyKrrt3h9Ak4bDoOh31gBORhADMizs752dCRz3gWCQhAHOn+sbZFjNRJEJHPeBE7JgGMHfRqPT0lHsS9qMuI3Pn0UqAY1ETAjBH0ahEIhSEpuH301EPOA41IQBzkUxKIkHn/IzoqAechpoQgLmIxeicnw0d9YDTkIQAFCyRoHP+5FQ1iI56wCFIQgAKRud8IeioBxyFJASgMLHY+I5gnJS6o541MsAJSEIACtPWxrrYHMTj44uJAOyNJASgAHTOz5Wqn1EWAmyPLnoAJ6M658fGyj0Pp2ltlVWrpKmJBAnYGTUhACfDRun58fslHmfrNGBzJCEAs1IXaXGDxPyordNcRgbYGEkIwKxiMTZKzx8HLQK2RxICMDM654unykKskQF2xY5pADPQdWlr44oxE6g76nWdW0oAG6ImBGAG0ShXjJnD75dIhDUywJ5IQgCmk0xKMskOIdM0NY3/TwrAZkhCAKZD57y5uIwMsCuSEIAp6Jy3QiRCRz1gQyQhAFNEo6yLWYKOesB+ZkxCg4ODxYybzWb37Nnz9ttvFzMIgDLgijHrhMMSDrNGBtjKNEno+eefv+aaay699NJixv3GN75x7bXXptPpYgYBUGq6LokEBSELtbaydRqwlRPOE+rv77/zzjv7+vpGRkYWL14870F37do1MDBQ9NwAlByd81bLnzpN1Q2whxNqQmeffXY8Hm9paSlmxBdffPHee+8tblYAyiGZFF2nIGS5cFh0nbIQYBMnJCGv1ysiK1eunPdww8PDzc3N27dvL3ZeAEqPzvnSoKMesJNp9glVVVXNe7jt27dfeOGFoVCoiCkBKAfV3c2STWmojnr6yAAbMPPesT179vT39z/88MMmjgmgRKJR6ekp9yTcRF1G1tTEriygvEw7T+jQoUMtLS3t7e1Lliwxa0wAJULnfOn5/RIOUxYCys60mlBLS0skEgkGg/MeYdrvLfJYIwAnl0xKIsGd82XQ2ir19ZJMkkEBq82ST8xJQg899NA777yzefPmYgYh9ADloTZKs0ZTenTUA6UyNWPks5EJq2Ovvvrqzp07//u//7v4oQCUWiIhus4VY2WjMhAd9UD5FJuERkdHv//973/ve98744wzTJkQgJKic7686KgHyq3Y1bF4PP7yyy+nUqlUKjX13bvuuuvRRx/95Cc/eckllxT5gwCYLxYb37eLMgqHxzvqOdMSKIdik9CBAwfeeeedBx98cNp3k8dLviQhwI7a2uictwU66oHyKTYJNTU1XXTRRVNfv/baa0Xku9/9biAQOPPMM4v8KQDMV19P57xd+P0SibBSCZRFsUlo9erVq1evnund888//4ILLijyRwAwn7oOfWys3PPAcU1NdNQDZTGfHdP79+/fvn37m2++afpsAJQI5Qe7Yes0UCbTJKFcLiciIyMjx44dm/ru6Ojopk2bdu/evXXrVstnB8AK6ooxOuftRm2dVv92AJTKCatjfX19jz322J49e0RkZGTkqquuuuCCCyKRyKQO+ZqamiNHjixfvrykMwVgFgpC9pQvCxFSgRJaMGaPjQLBYJAzpoFSiMVE10lC9lVfL34//4IAq+WDh5l30QOwO12XtjauGLM11VHP1mmgVEy7ix6AA0Sj0tbGoTW2lu+oB1ASJCHANVTnPAcZ219Tk+g6l5EBpUESAlwjFuNEaWegox4oIZIQ4A6qN5utJ04RidBRD5QGO6YBd4hGKQg5jNo6TUc9YDFqQoALqCNqKAg5i98v4TBrZIDVqAkBlU7XJZGgc96RWlvpqAesRk0IqHR0zjuX2jpNRz1gJZIQUNGSSdF1OucdLBymox6wFEkIqGjRKPc2OJu6eYPdQoBlSEJA5UokxnfdwtHUHfWskQHWIAkBlSsaZV2sQsTjkkiIrpd7HkAFIgkBFYrO+UqianuUhQAL0EUPVKJkUhIJGRsr9zxgHjrqAWtQEwIqUSzGRulKQ0c9YA2SEFBx1IYSbmmoPKoaxGVkgKlIQkDFoSBUqSgLARYgCQGVJRajc76S0VEPmI0kBFQQXZe2NgpCFY6OesBUJCGggqjOea4Yq2x+v0QilIUAs9BFD1SKZFKSSTrnXaGpiY56wCzUhIBKwUZp91Bbp7mMDDADSQioCKqzms5591Bbp+moB4pGEgIqQizGFWPuQkc9YBKSEOB8sZiEw2wZcR31L501MqA47JgGHE51zh84UO55oBy4jAwoGjUhwOGiUWlro3PepeioB4pGEgKcLJkUXWeHkKs1NYmuSzJZ7nkATkUSApyMznnQUQ8UhyQEOJbqoGaDCNTB4nTUA/PCjmnAsaJR6ekp9yRgD/G41NdzoBQwD9SEAGdSV4xREILi99NRD8wPNSHAgZJJSSTonMcJ6KgH5oWaEOBAsRid85iMU6eBeSEJAU5D5zxmoqpBdNQDc0ESApwmGqVzHtOjox6YO5IQ4Cix2PjeWGBa6o561siAgpGEAEdpa2NdDCcRj0siIbpe7nkAzkASApyDznkUQlUNKQsBhaGLHnAI1Tk/NlbuecAJWltl1SppaiI3AydFTQhwCK4YQ+H8fonHKQsBhSAJAU6gtn1wlwIKp6pBXEYGnAxJCHACCkKYKw5aBApDEgJsj855zI/qqOd4IWBW7JgG7E3Xpa2NK8YwT+qOel3nbhZgJjPWhAYHB+c61ujo6L59+5566qm33367uFkBOC4a5YoxzJ/fL5EIa2TALKapCT3//PM7duzYv3//Cy+8UPhAHR0dHR0d//73v9U/rl+//sc//rGfj2+gGMmkJJPS01PuecDJmpq4ox6YxQk1of7+/mg0es011zzzzDNzGuXmm2/esWPHsmXLwuGw1+tVQ1122WXzKCwBeB8bpVE8LiMDZnVCEjr77LPj8XhLS8uchti3b98f/vCH3bt39/T0dHR0PP30062trSLy9ttv33TTTWZOFnAV1f9M5zyKF4mI309HPTCtE5KQKuesXLlyTkM8/PDDd95554UXXph/5aqrrrr++utF5M9//vMrr7xixjwB94nFuGIMpqGjHpjBNDumq6qq5jTE2rVrzzvvvEkvXn311eoPmUxmfjMDXC0Wk3CYjR0wjfq/E2tkwBQmdNFfccUVU1/0er2LFi1677335lphAkDnPCzR2srWaWAqq84TGhkZee+99z784Q+fc845Fv0IoGLROX8yxgTZbNbr9WoTlHt2dpU/dZokBExgVRLq6+sTkU2bNhX+LcFgcOqLdJ/BdZJJ0XV2CM2kt7d3cHBw//79IqJpmsfjEZGhoSERyeVyhmFomtbY2FhXV1fmidpTOCyxGGUhuNC0GUOxKgk98sgjK1euvOaaawr/FkIPIELn/Iy6uro6Ozs9Ho/P52toaJj2a1QY6uzs7OzsDIVCGzduLPEk7S7fUc/aK1xmasbIZyNLktBLL73U2dn5y1/+csmSJVaMD1Qs1efM8/qJDMNIJBKGYTQ0NKgi0Ew8Ho+KSrlcLpVKpVKp5uZm1stOEInIPfdIIsEBDYBi/g2so6OjP/zhD2+44Yb169ebPjhQ4aJR1sUmSafTW7duXbBgQSgUmj0GTeTxeNatW+f1etvb29PptKUzdJ54XGIx0fVyzwOwBfOT0G233bZ69errrrvO9JGBCheNSiRCQWiidDrd3t4eCoUCgcBcv9fj8QQCgWAwmEgkCEMn8PvHNwwBMD0JPfzww7qu33rrreYOC1S+ZFISCQpCExmGoWJQMctbmqb5fD61uGbi3ByvtXX8VjvA9cxMQnv27Pntb3/7P//zPyaOCbhFLEbn/CSJRCIYDBa/y8fn83m93gR3TUyU76gHXM+0JPTUU0/t2rXrf//3fyftks5ms2+++aZZPwWoTIkEnfOTdHV1GYYxj0WxadXW1hqG0dXVZcpoFUKtw1IWguvNJwnt379/+/btE/PN3r17b7/99o6OjqVLl076ymuvvfZDH/pQsdMEKhud81OkUqlZzv+YK7WBOpVKmTVgJeCOekBEpu2iz+VyIjIyMnLs2LGpd5CNjo5u2rTpyJEjf/3rX++++24RefLJJ7/5zW+KyMRLWEXk6NGjItLY2Fh4uwfgRrHY+A5WHNfb2ysi5na/qw+idDptVp2pEoTD4vdz1y9c7oSaUF9f37Zt23784x+LyMjIyFVXXXXbbbdNXduqqakRkeXLl4tIf3//dddd995777333ntHT6S++JJLLinF3wNwrrY2/js0SSqV8vl8pg/r8/k6OztNH9bZ4vHxxVnArRaMjY2Vew4iIsFgkDOm4UZqbYKlsRP953/+Z2Njo+nD5nK57u7uW2+9lbMWT8D/CeFK+eBh1W0bAE5Odc7b42nEPtLptEVJxePxaJqm7iazYnynam2VVaukqYklWriT+ScrAigUG6WnYxiGpZsLOVhoMr9f4nG2TsO1SEJAmajjbbj7aQpLk5DX6yUJTUNtnebIJbgSSQgoExp2ZpDNZqurqy0avLq6OpvNWjS4g3HQIlyMJASUYv5gzwAAIABJREFUA53zM/N6vcPDwxYNPjw87PV6LRrc2VRZiDUyuA9JCCg5XZe2NnYIzUTTNOvKNrlcju3SM4rHJZmkox5uQxICSi4a5YqxWViaVGgcm43fL5EIa2RwG5IQUFrqAnB2CM1M0zTrVsfE4qTleE1N3FEPtyEJAaVF5/zJaJqmLkw1feRMJlNbW0sSmg2XkcF9SEJACdE5X5jGxkYrDp3PZDKhUMj0YStNJEJHPVyFJASUUDTKulghVNnG3LKQYRiGYdTV1Zk4ZsWKx9ktBPcgCQGlEo1KJELnfCE0TTO9LDQ4OGjFXWaVSR3xwBoZ3IEkBJSErksiQUGocIFAQNO0dDptymjqLrONGzeaMportLaydRouQRICSoLO+TnSNC0SiWSz2eLXyAzDyGazEbZnzQmnTsM1SEKA9dRpdRSE5kiFoYGBgWIqQ4ZhpFKpSCRCy9ichcOi65SFUPFIQoD16Jyfr0Ag0NLSks1m5xeG0un04OBgc3NzIBAwfW6Vj456uANJCLCY6kZmo/R8aZrW3Nw8NjbW3d1d+EpZLpdLpVJjY2O33norMWj+VEc9a2SoaAvGxsbKPQcRkWAwaMXxIUD5LVggPT0koSIZhtHb29vZ2enxeAKBgM/nm+kr0+m0urYsFAqxRdoEui719dLTwy43VJh88CAJAVZSKwssjZnEMIx0Op1KpYaGhqqrqz0ejxy/nSOXy+VyOXWtGBnIZPzfGJWIJARYL5mU+nqxx69YhTGOU58bXq9XRAKBAAthllBloXic0iYqST54LCr3TIDKxUZpy2iapnrBODO6FPId9SQhVCJ2TAPWSCRE17liDBVCZSAuI0MlIgkB1qAghErCQYuoXCQhwAKx2PjNTUDFCIfpqEdFIgkBZtN1aWvjRGlUoHh8fNkXqCAkIcBs3DmPSuX3SyRCWQgVht4xwFTq+m4651Gpmpqkvl6SSbI+KgY1IcBUbJRGZeMyMlQckhBgHtVjTOc8KpvaOk1HPSoFSQgwTyzGRmlUPjrqUVlIQoBJ1Am8bJ6AG6iyEGtkqAjsmAbMoDrnDxwo9zyAUonH2TqNykBNCDBDNCptbeL3l3seQKnQUY9KQRICiqY659khBLdpahJdl2Sy3PMAikISAooWi0lPT7knAZQcHfWoCCQhoDiql5itEnCnSISOejgdO6aB4kSjFITgamrrNMdowbGoCQFF4IoxwO+XcJg1MjgXNSFgvnRdEgk65wFpbaWjHs5FTQiYLzrnAYVTp+FkJCFgXpJJ0XU654FxqhpERz0ciCQEzEs0yp3zwPvoqIdjkYSAuUskxneJAshTl5GxRganIQkBcxeNsi4GTCMel0RCdL3c8wDmgCQEzBGd88BMVK2UshAcxZIkNDg4aMWwQPklk5JIsEMImFFr6/hNfIBDmJyEnn/++WuuuebSSy81d1jALmIxYhAwGzrq4TSmJaH+/v5oNHrNNdc888wzZo0J2IvaAMGtAsDs1Noxl5HBIUxLQmeffXY8Hm9paTFrQMB2KAgBhaAsBEcxLQl5vV4RWblypVkDAvYSi9E5DxRKddRzvBCcwOR7x6qqqswdELAFXZe2Nq4YA+ZA3VGv69xIA5ujix4ogOqc5wMdKJzfL5EIa2SwP+6iB05GtQSPjZV7HoDTNDVxRz3sj5oQcDJslAbmh8vI4AQ2qgkFg8GpL3JII8pMdQLTOQ/MTzgs99wjiQS/RCivaTOGYqMkROiBHVEQAoqRLwuRhFBWUzNGPhuxOgbMLBaTcJgtDkBR1C8Ra2SwKxvVhAB7oXMeMEtrK1unYVvUhIAZRKPS1kbnPGACOuphYyQhYDrJpOi6tLaWex5ApWhqEl3njnrYEEkImA4bpQFz0VEPuyIJAVOoznk2NADmUge1c0c9bMbkHdO5XE5ERkZGjh07xh1kcKpoVHp6yj0JoBKpy8joqIedmJaE+vr6HnvssT179ojIyMjIVVdddcEFF0QikTPOOMOsHwGUgjr4hIIQYAW/f7yjntVn2IZpSehTn/rUpz71KbNGA8ojmZREgs55wEJ01MNm2CcETBCL0TkPWEttnaajHrZBEgKOo3MeKA1VDaKjHvZAEgKOY+8CUBp01MNOSEKAiIjEYuN7OQGUQDgsfj9rZLADkhAgIiJtbayLASUVj0siIbpe7nnA7UhCAJ3zQDmoKixlIZQbd9HD9VTn/NhYuecBuE9rq6xaJU1NPIegjKgJwfW4YgwoF79f4nHKQigvkhDcTV2BxNn/QLmoahCXkaF8SEJwt1iMjdJAOXHQIsqNJAQXo3MesAPVUc/xQigTdkzDrXRd2tq4YgywBXVHva5z1w1Kj5oQ3Coa5YoxwC78folEWCNDWZCE4ErJpCST7BACbKSpafwXEygtkhBcic55wG64jAxlQhKC+9A5D9hTJCJ+Px31KDGSENyHznnAtuioR8nRO4bKYRiGiKTTacMwtAlO+KJoVMJhOucBm1K/ntHopPVr4zj1j5qmBQKBcswPFYgkBMczDKO3t7ezs1NEPB5PdXW11+vN5XK5XG54eLi6ujoQCIRCoUAgILouiQSd84CttbZKfb0kkxIOG4bR2dnZ29vr8XhERD3Y5HI5EVEPPKFQqK6ubvIDDzAXC8bscfFkMBgcHBws9yzgMPlPyWAwWFtbqz4rJ8nlcoZhZDIZEQml0xvXrmVpDLC7RKKrqysVCAwPD/t8vpnKP7lcbmhoKJvNvv+0AxQsHzyoCcGpurq6Ojs7g8FgY2PjLF/m8Xg8Ho/P58vlcimR1LvvNhsGT5CAbRmGkTAMIxAIBoOz/6p6PJ5AIFBbW5vJZBKJRCgU2rhxY8nmiYpBTQiO1N7ePjQ0VFdXN20daCb5J8jGxsa6ujrrpgdgftLpdHt7ezAYnGuBJ5fL9fb21tbWNjc3WzQ3VJh88KB3DM7T3t4+PDzc0NAwpxgkx58gg8Fgd3d3b2+vRdMDMD/pdPrOO++c3zqXx+Opq6tbsGDB1q1brZgbKhhJCA7T3t5uGMa6devmPYLqOuns7Eyn0yZODEAxDMNob29ft27dvBev1aOO1+tNcCIR5oIkBCfp6uoaGhoKhUJFjqN2DvFxCdiH2uhT/B6+2tradDrd1dVlyqzgBiQhOEY6ne7s7CymGjSRz+fzeDyEIcAOEomEYVIrg8fjWbduXSqVouiLApGE4BipVKqYyvlUgUAgnU7zcQmUXW9vr1kPOXK86JtKpcwaEJWNJATH6O3tNbf7XTXY83EJlFdXV5eq0Zo4pqZpPOSgQCQhOENvb6/pn5VyvCxk7pgA5iSVSvl8PnPHVJ8V7BZCIUhCcIbOzk7TPyvl+MclYQgoF3WehRWHnQaDQSq+KARJCM5g1m7KqbxeL0kIKCPTa71KdXV1/sZWYBYkITiA1R9n2WzW0vEBzGRwcNCihxy1EZAwhJMiCcEB0um0dTeFaZrGZyVQkSgLoRAkITiDRfVz4bMSKKt0Om3dbzc1IRSCJAQH0DQtl8tZNLh1O5AAnJSlv33WfW6gkpCE4ACapg0PD1s6vnWDA5iFpc85w8PD87jMFW5DEoIzWFoT8nq9Fg0OYHZer9e655xcLsdzDk6KJAQH0DTN0n3NfFYC5RIIBCxq3mRpDAUiCcEZAoFAJpOxYuRMJkP9HCgXtfZtRWpJp9N1dXWmD4vKQxKCMzQ2NlpRE1KfldSEgHLRNO28884bGhoyfWTDMBobG00fFpWHJARn0DSttrbW9DCUyWRCoZC5YwKYk1AoZHrFN5PJ1NbW8pCDQpCE4BiNjY0DAwMmDphOp2tra1kaA8orEAjU1taae+lNJpOhIIQCkYTgGIFA4LzzzjMrDBmGMTg4yGclYAeRSCSTyZhV9E2lUpqm8ZCDApGE4CSNjY25XM6UZ8eBgYHm5mY+KwE70DTt8ssvHxgYKH7rtFpoa25uNmNecAWSEJxE07Tm5uZMJlNMGMrlcqlUqqGhgRgE2EddXV1DQ0Nvb28xYcgwjIGBgUgkYt68UPlIQnAYTdNaWlqy2ez8wpBhGN3d3aFQaOPGjabPDUAxigxD6XR6cHCQWi/masHY2Nikl0ZGRp599llN084555y5Dnfw4MHBwcFTTjnlvPPOW7hwDjErGAwODg7O9cfBtQzD6Ozs3L9/f11dXeHXN/JBCdhfb2/vb37zG5/PV/jvaS6XGxgYUDVjS+eGSpIPHpOTUEdHRzweD4VCBw8efOutt2699da1a9cWMmJ/f/8tt9yyfPnys8466+jRo729vRs2bLjhhhuWLFkypwkBBTIMo/c//7NT0zRN8/l8Pp9vpq/M5XJDQ0ODg4Pqg5LGWsDmDMNIJBJDQ0M+n6+2tnaWpx21cTCXy9U98UTjs8+WcpJwuumT0LZt237/+98/+OCDq1evFpHbb7/95z//eSKROP/882cfrr+/PxqNtrS0XHnlleqVt95669JLL/X5fPF4fE4TAgqVSMg99xgPPZROp1Op1NDQkIo4mqZ5PJ5cLqcuM1IH+YdCIU5QBJzFMIze3t7u7u7q6mqPx6N+fz0ej2oxy+VyhmFUV1ePL3bX10s4LK2t5Z41HGOaJNTV1dXc3Hz99dd/+9vfVq+Mjo5eeOGFixcvfvzxx6urq2cZbsOGDVVVVZ2dnRNfvO+++2Kx2M6dOz//+c8XPiGgUAsWSE+PhMPqnwzDSKfThmFks1n1QRkMBkUkEAiwFgY4l3Gc+m+EYRiapnm9XtUn//7jja5Lfb309IjfX8bZwkHywWOR+ufR0dH29nYRufjii/NftHDhwi9+8Yv33nvvr371qy1btsw01uHDhw8cOLB+/fpJr59++uki0tfXV0gSAuYmGpVIJB+DRETTNO4YAiqPuoBZRE7yC+73SzgssZgUthAB5I1vat6zZ89rr722ePFitS6Wp/LNr3/965MO9Nxzz7355psTX3njjTdE5OMf/7hpkwWUZFISCT7vAJygtVWSSUkmyz0POMx4EnriiSdEZGqzmKrr/P3vf3/llVdmGmLp0qVnnXXWyMhIc3Pzu+++q14cHR194IEHfD7fhg0bLJk43IzHPgBT+f3S2iqxWLnnAYcZT0JqqWxq981HP/pR9YfZz2656aabROSZZ5658sorDx48KCItLS2HDx+Ox+MF9o4BhUokRNeFk9MATKVWzBOJ8s4CzjKehF5++WURmbotetGi8Y1EqgFnJp/73Oe2bt0qIi+++OIll1zyX//1X4cPH/7d7343S2MzME8UhADMhLIQ5m486Bw5ckREqqqqZvq6WVbHlKamplNOOWXbtm2GYTz55JO33HJLTU3NnKaiOn0moaEMJ4jFxvdFAsC0wmHx+yUWo6MeE02bMZRFBQ7xgQ984KRf884773ziE5945ZVXDMO46aabXnrppe9973uFzpHQg5PSdWlrk56ecs8DgL3F41JfL01NdNQjb2rGyGej8dWx/CrYJKOjo+oPa9asmf1nbNu2rbOzMx6PP/zww6oBbffu3T/96U/nPWlgsimd8wAwDb9fIhHWyFCg8SS0YsUKEcl3fuWpE+pEZNmyZbOM0tHR8cADD/zsZz+rqqpasWLFfffdt27dOhFJJBJ79+41f9ZwIdUcyw4hAIVoaqKjHgUaT0LnnnuuiBw+fHjS22ontYhMOmdoorfeeuuOO+5Ys2ZNvgm/pqbmrrvuWrVqlYj84he/MH3ScCM2SgMonNo6HY2Wex5wgPEkVF9fLyL79u2b9Pa//vUvEfH5fGedddZMQzz77LNHjx71n7gcW1NTs3379mnHBOZM9cTSOQ+gcGrrNB31OJnxJLRhw4aampp//vOfr7/++sS3U6mUiFx22WUnHWjSnfYisnbt2sWLF5966qkmTRUuRhsIgLmiox6FGU9CVVVVX//610Xk8ccfz783Ojr69NNPe73eq6++euL37N+/f/v27fm7NT796U+fcsopzz77bH57tTIyMjIyMnLRRRdZ+zdAxYvFJBxmozSAOVNlIdbIMKuF+T9t3rw5FAolEolDhw6pV3bt2pXNZnfs2LF06dL8l42Ojm7atGn37t3qKEURqa6u/tGPfmQYxo4dOyYOfccdd5x55pnXXXed9X8LVC7VOU9BCMD8xONsncbsTmie37lzZ2tr6+WXX/6Zz3wmk8n84x//uP/++9euXTvpe2pqao4cObJ8+fL8K1/+8peXLFly2223vfjii1/60pdEpKura9myZb/5zW/mer4icIJoVNraOBQEwDzlO+qpK2MGC6bu7ymLYDDIyYqYLJmUaFQOHCj3PAA4ma5Lfb3E44QhTJQPHgtP+qVA2dA5D6B4dNRjViQh2JXqfeUZDkDxIhE66jGTQu8dA0otGuWKMQCmUZeRcSwZpqAmBFviijEA5vL7JRxmjQxTUROC/ei6JBJslAZgstZWqa+XZJKnLExETQj2Q+c8ACtw6jSmQxKCzSSTouscpQjAEqoaxEGLmIAkBJuJRumcB2AVOuoxBUkIdhKLje9qBACLqMvIWCPDcSQh2AlXjAEogXhcEgnR9XLPA7ZAEoJt0DkPoDRU7ZmyEESELnrYRTIpiYTY4xY8AJWPjnocR00I9sAVYwBKiY56HEcSgg2oBXtOwQdQSqoaxGVkrkcSgg1QEAJQepSFICIkIZQfnfMAykV11HO8kLuxYxplpevS1sYVYwDKRt1Rr+vc8ONa1IRQVqpzng8gAOXi90skwhqZm1ETQvkkk5JM0jkPoMyamuiodzNqQigfNkoDsAMuI3M3khDKRHWu0jkPwA7U1mk66l2JJIQyicW4YgyAXdBR72IkIZRDLCbhMEvyAGxEfSixRuY+7JhGydE5D8CeuIzMlagJoeSiUWlro3MegO2wRuZKJCGUVjIpus4OIQA2FQ6LrksyWe55oHRIQigtOucB2Bkd9e5DEkIJqQ5VFuAB2Jk6+J6OetdgxzRKKBqVnp5yTwIATkZdRsaBZ+5ATQiloq4YoyAEwP78fjrq3YOaEEoimZREgs55AI5BR71rUBNCScRidM4DcBI66l2DJATr0TkPwIlUNYiO+kpHEoL1olE65wE4Dx317kASgsVisfG9hwDgOOqOetbIKhpJCBZra2NdDICDxeOSSIiul3sesApJCFaicx6A06mqNmWhykUXPSyjOufHxso9DwAoTmurrFolTU0811UkakKwDFeMAagMfr/E42ydrlQkIVhDXdnDWfUAKoPaOs1lZJWIJARrxGJslAZQOThosXKRhGABOucBVB5VFmKNrOKwYxpm03Vpa+OKMQAVSN1Rr+vcHVRJqAnBbNEoV4wBqEx+v0QirJFVGJIQTJVMSjLJDiEAFaupafyDDpWCJART0TkPoLJxGVnFmSYJjYyM9PX1vfTSS0UOPTo6+pe//GXv3r0jIyNFDgVnoHMepWUYRjqd7u3tTafThmGUezpwjUiEjvpKMnnHdEdHRzweD4VCBw8efOutt2699da1a9fOddCenp777rsvm802NDSsWbPGpKnC9qJR6ekp9yRQ4QzD6O3tHRwcTKfTHo9HRKqrq9XrmqaJSCgUqqurU38GrKLKQjz4VYQFYxMuQ9i2bdvvf//7Bx98cPXq1SJy++23//znP08kEueff36Bwx06dOgHP/jBc889d/PNN2/cuLHweQSDwcHBwTlNHfaiasUsjcEyKgN1dnYGg0FN06ZmnVwuJyLpdDqXywUCgVAoFAgEyjFTuAMfeg6XDx7vJ6Gurq7m5ubrr7/+29/+tnpldHT0wgsvXLx48eOPP66eumb3+uuvX3311UeOHLnnnntUlprHhOBIui6rVsmBA7SMwSJdXV3d3d0+n6+QcJPL5YaGhnK53Lp16+b0SAbMga5Lfb3E45yd5lD54DG+T2h0dLS9vV1ELr744vwXLVy48Itf/OJrr732q1/96qQjHjp06Ktf/eobb7yxc+fOucYgOB6d87CMYRjt7e3d3d11dXUF1ng8Hk8gEAgEAt3d3e3t7WwhgiU4dbpSjCehPXv2vPbaa4sXL54UYtavXy8iv/71r0860Le+9a033nhjy5YthS+loUIkk6LrdM7DCioGDQ8PNzQ0qF1BhfN4PHV1dQsWLCAMwSrhsOg6HfVON56EnnjiCRE555xzJr19+umni8jf//73V155ZZZRHnrooWeeeaa6unrLli3WzBM2Ruc8LJNIJDRNW7du3fy+XRWHvF6vqnkDJqOjviKMJyG1VObz+Sa9/dGPflT9IZ1OzzLKzp07ReQrX/nK0qVLDx8+vHfv3v7+/tHRUfPnC7tRfaQsk8MCXV1dhmEEg8Eix6mtrRWRBD3PsILqqGeNzMnGu+hffvllOd6MesLbi8a/IJvNzjREX1/fa6+9JiIrV67cvHnzn/70pyNHjhw9elTTtJaWlokbj1CB6JyHNdLpdHd3d0NDQ/FDeTyedevW9fb29vb21tXVFT8gcAJ1GVlTE3slHWo86Bw5ckREqqqqZvq6WVbH/vjHP6o/vPrqqz/5yU9WrFhx7Nixn/70p/fee+93vvOdRYsWfeELXyhkKtM++dFQZmvqOA0KQrBAZ2fnvBfFplJhqLOzkyQE8/n9Eg6zT8DmZqkuF3oX/Qc+8IGZ3hoaGhKR1atX33zzzeqVqqqqbdu2vfDCC3/6059isdjnP//5hQtPfq0HocdhkklJJLhzHlZIp9PpdLr4dbGJVM07nU5zyBDM19oq9fWSTPJkaFtTM0b+E2Y8oORXwSbJ7/WZ5ajoQ4cOichHPvKRSa9Ho1ERyWaze/funfOUYX/qAYhqMCyQSqVMLAgpHo/H5/OlUilzhwVE6Kh3tvEktGLFChF59913J72dbz1dtmzZTEMsXrxYRD74wQ9Oer2+vl794e233zZjqrCTREJ0nZPmYZHe3l4rrsvQNG325g9g/lQ1iI56BxpPQueee66IHD58eNLbaie1iMxyWKIKSVOvWa2uri7kZGo4EivisIy6UGyupwcVQo1JGIIl6Kh3rPEkpOo3+/btm/T2v/71LxHx+XxnnXXWTEN87GMfkwmZ6YTRFy4UkdNOO82k2cIeYrHxHYKABfJ3qQIOEw7TUe9E40low4YNNTU1//znP19//fWJb6s19csuu2yWIVSf/N/+9reDBw9OeuvYsWOnnXZaKBQyc8oou7Y2TpSGdSw9D9rr9VITgoXi8fHNA3CO8SRUVVX19a9/XUQef/zx/Hujo6NPP/201+u9+uqrJ37P/v37t2/f/uabb6p/9Pv9Kio98sgjE7/shRdeOHr06JYtWwppHINj1NfTOQ9LZbNZS2tCs5yOBhQr31EP53g/o2zevDkUCiUSCdULJiK7du3KZrM7duxYunRp/stGR0c3bdq0e/furVu35l/cunXrmjVrOjo68scOvfvuu7fccstnP/vZzZs3l+QvgpJIJiWZZIcQLOX1eoeHh8s9C2C+WlvHPyrhECc0z+/cubO1tfXyyy//zGc+k8lk/vGPf9x///1r166d9D01NTVHjhxZvnx5/hWPxxOPx1taWq644oqvfe1rp5566qOPPlpXV3fjjTeW4i+BkmGjNEoil8tZNzKHK8Ja+a3THLfmECckIY/Hs3379tm/YeHChdOeD7R8+fJdu3bl/3HTpk2mzA82oq5tonMeFgsEAtad+mNdxgLeFw7LPfdIIsEHpiOwgwcFi8XYKI0S0DTNutWx4eFhzpiG5Tho0VFIQihMLCbhMBulUQKaptXW1lrRQZbJZHK5HC36KAXVUc/xQk5AEkIBdJ3OeZRSKBSy4iJCwzAirFagZOJxSSbpqLc/khAKEI1KWxtXjKFkAoHA8PCw6Xt6MpkMS2MoHb9fIhHKQvZHEsLJqHZQCkIoIU3TzjvvPHOPQEyn03V1dSyNoaSamkTX6ai3OZIQTobOeZRDY2NjLpfLZDKmjGYYRjabZWkMpcZlZE5AEsKs6JxHmWia1tzcnE6ni18jy+VyqVSKGITyiETE7x//LIUtkYQwq2iUdTGUi6ZpDQ0Nvb29xYShXC43MDDQ2NjIDiGUTTxOR72dkYQws2iUK8ZQXhs3blRhaH7LZIZhdHd3h0KhjRs3mj43oFDqMjLWyOxq0cm/BO6k65JIcFo8ym7jxo2BQODOO++c66GI6XQ6m802NzdTDUL5tbZKfb0kkzxb2hA1IcyAznnYRiAQaGlpGRsb6+7uPunOoVwul06nOzs7iUGwEU6dtrEFY2Nj5Z6DiEgwGLTiIDXMUzLJ9YGwIcMwOjs79+/fr5rhNU3zeDzV1dXq8KHh4eFsNmsYRmNjIw3zsB1dl/p6iccpC9lEPniQhDCdVav4dYVtGYaRTqfVJ4ZhGOpejkAg4PV6RYQtQbAvHjLtJB882CeEKRKJ8f19gC1pmlZXV1dXV1fuiQBzpC4j4zZrm2GfEKagcx4ALBKPSyLBZWS2QhLCieicBwDrqIo7W6fthNUxTJBMSiIh9tg6BgCViY56m6EmhAm4YgwArEZHvc2QhHCcWrrmbiYAsJqqBnEZmT2QhHAcBSEAKA3KQnZCEoKIiMRidM4DQOnkO+pRbiQhiOi6tLXROQ8AJUVHvT2QhEDnPACUg98vkQhlobKji971kklJJumcB4AyaGqio77sqAm5HhulAaBc1NbpaLTc83A1kpC7qR5OOucBoFzU1mk66suHJORuXAQIAOVFR325kYRcLBaTcJjFaQAoM/VRzBpZmbBj2q1U5/yBA+WeBwCAy8jKiZqQW0Wj0tYmfn+55wEAoKO+nEhCrpRMiq6zQwgAbKSpSXRdkslyz8N1SEKuROc8ANgNHfVlQhJyH9WryVI0ANhNJEJHfemxY9p9olHp6Sn3JAAA04nHpb6eY95KiZqQy3DFGADYmd9PR32JURNyE12XRILOeQCwNTrqS4uakJvQOQ8A9sep06VFEnINOucBwClUNYiO+pIgCbkd/8r3AAAgAElEQVRGNErnPAA4Ax31JUQScodYbHwXHgDAEdQd9ayRWY8k5A5tbayLAYDDxOOSSIiul3seFY4k5AJ0zgOAE6laPmUhi9FFX+mSSUkkZGys3PMAAMxda6usWiVNTTzNWoeaUKXjijEAcC6/X+JxykKWIglVNLXAzKntAOBcqhrEZWSWIQlVNApCAOB0HLRosWmS0MjISF9f30svvVTMuNlsds+ePW+//XYxg6AodM4DQGVQHfUcL2SNyTumOzo64vF4KBQ6ePDgW2+9deutt65du3Ye437jG98YGBi49957L7jgAjPmiTnSdWlr44oxAKgQ6o56XefGJNOdkIS2bdv2+9///sEHH1y9erWI3H777ddcc00ikTj//PPnNOiuXbsGBgbMnCbmiivGAKCS+P0SibDnwQrvr451dXU98MAD0WhUxSAR+eY3v7l06dIbb7xxeHi48BFffPHFe++91+RpYk6SSUkmOUoRACpKU9P4xztMNZ6ERkdH29vbReTiiy9+/72FC7/4xS++9tprv/rVrwocbnh4uLm5efv27aZPFHPAQwMAVB4uI7PGeBLas2fPa6+9tnjx4nxBSFm/fr2I/PrXvy5wuO3bt1944YWhUMjcWWIOVKclnfMAUHnU1mk66k01noSeeOIJETnnnHMmvX366aeLyN///vdXXnnlpGPt2bOnv7//u9/9rtmTxFzEYqyLAUBloqPeAuNJaHBwUER8Pt+ktz/60Y+qP6TT6dkHOnToUEtLS3t7+5IlS8yeJAoWi0k4TOc8AFQs9SHPGpl5xnvHXn75ZRGprq6e/Pai8S/IZrOzD9TS0hKJRILBoNkzRMHonAcAN2htlfp6SSZ57jXFeNA5cuSIiFRVVc30dbOvjj300EPvvPPO5s2bi5nKtClKFatQEDrnAcAN8mtkJKGCzVKpKfQu+g984AMzvfXqq6/u3Lnz/vvvn/O8TkToKUoyKbrODiEAcIVwWGIxykKFm5ox8tlofJ9QfhVsktHRUfWHNWvWzPQF3//+97/3ve+dccYZJswU80bnPAC4Bx315hkPQCtWrMhkMu++++6ktw3DUH9YtmzZtN8fj8dffvnlVCqVSqWmvnvXXXc9+uijn/zkJy+55BLz5owpVEclTwYA4B6RiNxzjyQSHJtSpPEkdO6552YymcOHD096W+2kFpFJ5wzlHThw4J133nnwwQenfTd5/ChMkpC1olHp6Sn3JAAApaUuI1OHDGG+xpNQfX39Y489tm/fvklv/+tf/xIRn8931llnTfv9TU1NF1100dTXr732WhH57ne/GwgEzjzzTDOnjEmiUYlEKAgBgOv4/eMbhtgdUYTxJLRhw4abb775n//85+uvv75ixYr822rN67LLLpvp+1evXj1TuUhEzj//fO6it1YyKYkEnfMA4FJ01BdtfMd0VVXV17/+dRF5/PHH8++Njo4+/fTTXq/36quvnvg9+/fv3759+5tvvlnKiWJ6sRid8wDgXpw6XbT376LfvHlzKBRKJBKHDh1Sr+zatSubze7YsWPp0qX5LxsdHd20adPu3bu3bt1a6slikkSCznkAcDtVDeKO+vk6oXl+586dra2tl19++Wc+85lMJvOPf/zj/vvvX7t27aTvqampOXLkyPLly0s4T0yHtWEAQL6jnp0S87JgbGys3HMQEQkGg5ysODfqTC1axgAAIuNNZKwSFCwfPBae9EthU21t/D8eADAuHh/fMoE5Igk5E53zAICJ8h31mKNC7x2DjajOeXssawIA7KK1VVatkqYmnpPnhJqQA7FRGgAwld8v8TiXkc0VSchp1BVj3DIDAJhK3byh/kuBwpCEnCYWY6M0AGB6HLQ4dyQhR4nFxvfEAQAwLVUWYo2sYOyYdg5dl7Y2Ds4CAJyEuqNe17mLqRDUhJwjGuWKMQDAyfn9EomwRlYgkpBDJJOSTLJDqBDGceWeCACTGYaRTqfLPQuHaGoa/w8HTobVMYegc35WhmH09vYODg6qT0mPxyMiuVxO0zRN04LB4MaNG8s9RwBzlv/VVo83E3+1RSQQCIRCoUAgUO5p2hKXkRWMe8ecIJGQe+7hirGp1KdkKpUaHh72+Xwq9+TfzeVycrxElMvl+NAEHET9dnd2dqpfbY/HM+m3e3h42DCMTCZTXV0dCoV42plefb00NXHwyrTywYMk5AQLFkhPDy1jkxiG0d7ePjw8HAgEfD7f7F+cy+WGhoYymUxDQwOfmIDNdXV1dXd3+3y+Qh5dVB7K5XLNzc0T0xJERJJJykIzIQk5h+qEZGnsRL29vYlEIhQKzemDL5fLpdNpPjEB2zIMI5FIGIYRCoXm9I3pdDqbzVIcmgb/EZkBScghdF1WrZIDB2gZm6i9vX1oaGjdunXzSDOqOJTNZiORCCtlgK2k0+n29vZgMDi/381cLtfb20vddzJdl/p6icdZWJgkHzzoHbM3Ouen6OrqMgyjoaFhfkUdj8ejVtMSnEYP2ExnZ2cxm/k8Hk9dXV0qlerq6jJ3Ys7GqdMnQxKysWRSdJ3O+YnS6XRnZ2cwGCxyHJ/P5/V6CUOAfbS3txuGUeSytcfjWbduXSqVotn+BOGw6Dod9TMhCdkYnfNTtLe3z3Vv0Exqa2vT6TTPjoAdqFrvXPcGTcvj8aiiL4eKvS/fUY/pkITsSpUrWNadIJFIqH5aU0bLPzuaMhqAYphS683z+Xwej6ezs9OsAStBJCJ+P2tk0yIJ2VU0yrrYJOl02tw9zuqUtt7eXhPHBDBXvb29Jj7kKIFAgAWyyeJxSSRE18s9D9shCdlSNCqRCAWhiVReUdnFRMFgkAdHoLxSqdRJjwSbK/VZQRg6gd8v4TBloalIQvaTTEoiQUFoEis+K0Wkurpa+LgEyiedTqfTaSvO9+I5ZxqtrVxGNhVJyH7URmk6509k0Welx+PxeDwkIaBcDMOw4iFHRKqrq9k0PRkd9dMhCdmMWsTljpjpmL40ZumwAAoxODho3a+2unbQisEdTO27oCw0AUnIZuicn45FBSFF0zTONwfKSC1SW0HTNJLQZHTUT0ESspNYbHxHG05kGIallRs+K4FysfQ5R/jtnlY4TEf9RCQh29B1aWtjo/S0NE3L5XKWjm/d4ABmoWna8PCwRYOz9j0jOuonIAnZBp3zs7LuszKXy5GEgDKy7jnHMAwuWp6e3y+RCGUhhSRkD6qtkR1CM7A0qQwPD3u9XuvGBzCLYDBo6XOORSNXgqYmOuoVkpA9sFF6Vmp1zKIPNWpCQBlpmpbNZi0d37rBnY2t08eRhGxAXTFG5/ysAoGARTsfM5kM9XOgXAKBgEU1oUwmU1dXZ8XIlUNtnVb/DXIxkpANxGJslD6pxsZGK84/VJ+VPDUC5aJpWm1tbSaTMX3kdDptyuX2lYyDFkWEJFR+sZiEw2yUPqlAIFBbW2t6WSiTyZh4AzaAebDiOSeTyeRyOcq9J6fKQu5eIyMJlRWd83MRCoXMPQJRnT9L/RwoL03TTL8ZI5PJRNhyUKB43OVbp0lCZRWNSlsbV4wVKBAIaJpm1rNjLpdLpVLNzc2mjAZg3jRNa2xsHBgYMKsrQn1K8JBTKNd31JOEykdlcApCBdM0LRKJZDIZU54dBwYGGhsbKZ4DdlBXV9fQ0DAwMFD8UIZhDA4OUhCam6Ym0XXXloVIQuVD5/zcaZp2+eWXF//sODAwoGnaxo0bzZoYgCKp3oUii765XG5gYKC5uZmHnLlxd0c9SahM6JyfL/Xs2NvbO79PTLUolsvlWBcDbEUVfbPZbCqVmt8IhmF0d3dffvnlxKD5iERc21G/YGxsrNxzEBEJBoPuug98wQLp6aFlbN4Mw2hvb/d6vXP6yMvl/n979x/b1HU2cPwpkFS2+CFhl0GJRTqBDa0C2bSxJStbIiqBWNOJTcBUGDhC66B70Sp4/1grjSTtxop4w0QnEqLRxhN0gCaKRDLE0FISNThKpi1EbBW+ApbJIQzwHQWBEzpi3j9O6mb5Hd97Y/v6+/nLvUnOPfj23PucH8+50ZaWlmXLljFyDqQmXddbWloaGhoKCgom9NYwTdMikYjf7ycMSlxnpxQXyz/+kex6TJJ44EEklAxqBJKpMWN0XQ8EAl1dXR6PZ8x7XzQa1TRNpZOwjhJIcfX19Q0NDS6Xy+PxjL7dVzQa7erqCofDOTk5DPSaIJMeT0RCydPZKc88I//4ByljptB1va6urqOjw+VyuVwup9PpcDicTqdaSKTrek9PTzgcdjgchYWFLAwC0oUaHAqFQqq3o9q1ioqi0WhPT4/KnAiFQir1gaEgc6hhodraTJiyIBJKnuJiKSoiZcxcuq5rmhYKhfTPqDumy+Xy+Xwul4txICBNqd6OfNbMRcT1GbfbTffGfIGA/Pa3cv58suthOSKhJGlslNLSzJmFBQCkmYwZFooHHuSOTa7S0gyZfwUApKXcXKmtzaiMeiKhSRQISG6u7aNsAEB6Uy8jy5hdp4mEJlFpKcuDAABpoLZWAgHp7Ex2PSbDMJFQX19fa2vrlStXJlpWLBZrb29vbm6+d++eGXWzl9JS8fsZEAIApAE1g5EZw0LTBv13TU1NbW1tYWHh7du3P/nkkz179uTl5Y2noJqampqamgcPHqj/XL58+VtvvZVLorjS2CiBgKTG4nQAAMZWVibFxdLYaPs+/H+NCe3evbu6uvrIkSP79+8/cuTICy+8sGnTpvb29jFLefPNN/fv3z9r1qyioiK32y0ibW1t69aty4h0sPHgFWMAgPSiXkaWAcNCn0dC9fX1J06cKC0tXbRokTqyY8eO6dOn79y5s6enZ5Qi2tvbz549e/jw4fPnz9fU1Fy4cKGsrExE7t2799Of/tTS2qcHNdXK6x0AAOlFjQbZ/WVk/ZFQLBarrKwUkTVr1nz+sylTVq1a1d3dffTo0VGKOHnyZHV19YoVK+JHXn755VdffVVEPv7442vXrllS8TTCgBAAIB1lxrBQfyTU1NTU3d2dnZ0dHxBSli9fLiLHjh0bpYi8vLxly5YNOrhx40b1IRwOm1bZdFRRQeY8ACBdqYx6W28v1B8JnTt3TkQWLlw46Mdz5swRkevXr48ytLNhw4ahB91u97Rp00Rk/vz5ZtU1/XR2Snk5A0IAgDRWWyuNjTbOqO+PhNTSZo/HM+jHS5YsUR/U217Gr6+v79GjR3Pnzh0aXWUQlTlPAh0AIH3l5orfb+M5sv4s+qtXr4qIw+EY/ONp/b8QiUQmVG5ra6uIbN682WgF01djozQ2kjkPAEh7W7bYOKO+P9Dp7e0VkaysrJF+b6ILnz/44IP58+dv2rRp/H/i8/mGHkzjPHwWSgMA7EEtnU7nN4gPG2Mog3dWHMnUqVPHf74rV67U1dUdOXLkySefHP9fpXHQM5TKOSRzHgBgD0VF8tvfSiCQpo+2oTFGPDbqXycUnwUbJBaLqQ+LFy8e58lisdjrr7/+2muvqbyzDFVRwSvGAAD2Yd+M+v5IaN68eSLy8OHDQT/WdV19mDVr1jhL3Lt376JFi7Zv325SDdNQRYUUFdlyMhUAkLnUo812GfX9Q0FLly4Nh8P3798f9GO1klpEBu0zNJKTJ092dnbW1NSYWMU0ozLn03YmFQCAEdnxZWT9Y0LFxcUiMvQVY3fv3hURj8ezYMGCMctqamo6derUO++8Y3Yl00ppqZSXkzkPALAhO2bU90dCq1evnjlz5p07d27cuDHwx8FgUETWrVs3ZkHNzc1VVVWHDh0atEo6EoncvHnTvAqnNrX3FCuEAAB2tWWLdHZKY2Oy62Ga/kgoKytr27ZtInLmzJn4z2Kx2IULF9xud/zVGUpHR8e+ffsGxjcfffTRgQMHampqpk+fPug3X3nllRkzZlj4L0gpZM4DAOwtnlFvF5+njG3durW5uTkQCKxdu3b27NkiUlVVFYlE3nvvvYHxTSwW27x5c29v7+XLl999910R+fDDD3fs2CEiA1/CKiKffvqpiJSUlDidzsn5xySZypy30dQpAADD8PvTOqN+kP9Knj948GBZWdn69euff/75cDh869at48eP5+XlDfqbmTNn9vb2qmipra1t9DSx73znO6ZXOkWVlsr588muBAAA1qutleJie0RCTzxOjddB+Hy+9N5ZUY0TMjUGAMgQaf7giwce491jGqNpbJRAgMx5AEAGsUtG/ZRkV8AWKirInAcAZBa77DpNJGQYmfMAgMykRoPSPKOeSMiw0tL0nSUFACBxtsioJxIypqJCcnPTfYoUAIAEFRVJbm5az5ERCRlTXs68GAAgo9XWSiAgnZ3JrkeCiIQMKC0Vv58BIQBARlNzI2k7LEQWfaJU5nxq7MYEAEAylZXJM8/Ili3pODrAmFCieMUYAABKbq7U1qbpsBCRUELUhKgtdhkHAMAEajRIvYIzrRAJJYQBIQAABkrbjRaJhCaOzHkAAIZSGfXptr0QK6YnqLNTyst5xRgAAMNQ76jv7EyjN1AxJjRBpaW8YgwAgOHl5orfn15zZERCE9HYKI2NbKUIAMCItmzpf1ymCSKhiWChNAAAo0u3l5ERCY2bygwkcx4AgNGppdNpklFPJDRuFRXMiwEAMLa0yqgnEhqf0lIpKiJzHgCAcVEPzXSYIyOLfhw6OyUQIHMeAIAJKCuT4mJpbEzxcQTGhMaBzHkAACYqTebIiITG0tgonZ2sEAIAYMKKiqSzM8Uz6omExkLmPAAAiUmHjHoioVGpDMDUnuAEACB1+f0pnlHPiulRlZbK+fPJrgQAAOlMvYxMbTKUehgTGllpqfj9DAgBAGBIbq4UFaXs0mnGhEbQ2EjmPAAA5kjhjHrGhEZQUUHmPAAA5kjhjHoioeEEAmTOAwBgJjUalHoZ9URCwyFzHgAAc6VqRj2R0BAVFf1ruwAAgIlU+liKzZERCQ1RXs68GAAAlqit7V+CkjKIhP4bmfMAAFgn9TLqyaIfQGXOP36c7HoAAGBfZWXyzDOyZUuKjDswJjQAC6UBALBabq7U1qbO0mkioc+oV6L4/cmtBQAA9qeWTqfGy8iIhD5TUcFCaQAAJkMqbbRIJCQiZM4DADC51LBQCsyRZUokpOv6iD/r7JTyclYIAWlqtNYNIJXV1kpjY9Iz6m2bO6brektLSyQS0XVd0zR10PUZn89XUFDQ/6ulpbxiDEgjqnWHQiFd11UY5HK55LMGXlhY6PV6k11HAOOQmyt+v5SWyvnz6kB9ff1ID2632/3iiy9aUYsnHqdG0rjP5wuFQqYUpe6SdXV16otzuVwOh8PpdEajURHp6emJRqPhcFhEvF5v4dNPe1etInMeSAv19fXBYLCnp8fj8ah2rWIg1bp1Xe/p6QmHww6Ho7Cw0KKbJgAzdXZKcbH+f//X8uSTdXV1TqfT4/HEH9wiEo1G1YNb1/VoNOr1es3q7cQDD7tFQpWVlV1dXR6PZ8yvKRqNdnV1hUKhkry8F//nf4yfGoB1NE2rrKx0Op1er9fj8Yzym+qOqXo7JSUln4/+AkhJ9f/7vw19fR6PJycnR0U/I1EP7nA4vHLlSuNdHRtGQrquBwIBXdcLCwvH/1fqa41EIrt27VKdSwCppr6+vqGhIT8/f0KNVNf1UCjE4BCQspL74LZbJKT6iz6fL7ERM03TIpGI3+9neQGQatRAb0FBweidxWFFo9GWlpacnJxdu3ZZUTcACTPlwW2kqxMPPOyQO6bremVlpZGJQ6/X6/P5VGRqbt0AGFFZWanr+sqVKxMIg0TE6XQWFBSofqfZVQOQOFMe3Pn5+cFgML62OmF2iIQCgUBhYaHBuS21vLqystKsWgEwSNO0rq6uCQ2bD+V0OvPz8zs6Ourr682qGACDAoGAz+cz+OBWy6uN93NMjoT6+vpaW1uvXLlibrGjUF1GU5b4qMiUviOQClSXMT8/33hRamTIlL4jAOPUDIwpy1E8Ho/b7Tb44DYzEqqpqfnGN75x4sSJioqKkpKSS5cumVj4sFpaWjRNM9hlHCg/P1+VaVaBABITCAQmukR6FKrvWFdXZ0ppABKmdrox8cGdk5OjaZqRB7dpkdDu3burq6uPHDmyf//+I0eOvPDCC5s2bWpvbzer/GGFQiFTuoxxTqfT5/MFg0ETywSQAE3TRs+WnyiXyzVwuzYASVFXV2du0zbezzEnEqqvrz9x4kRpaemiRYvUkR07dkyfPn3nzp09PT2mnGJYLS0tpqe+q+jS3DIBTEggEDD3XikiTqfT6XTSzwGSq6WlxfQ0bdXPSTjnyYRIKBaLqYXGa9as+bzcKVNWrVrV3d199OhR46cYVn19vcfjSSyjZBSqwJaWFnOLBTB+mqZZsaWF1+ulnwMkUUtLi0UPbqfTmfCD24RIqKmpqbu7Ozs7Oz4gpCxfvlxEjh07ZvwUw4pEIhbthejxeOg4Asmi+nam3ytFxOl09vT0EAwByRIKhax7cCe8K6EJkdC5c+dEZOHChYOOz5kzR0SuX79+7do142cZStM0i75QK27BAMbJ9BVCAzkcDotKBjAm6zbtczgcyZwdU1HY0DvXkiVL1AeLemAW9RrF2BcKIJU5nU7GhIBkMWvXG3NNM17E1atXZbie1rRp/YVHIhHjZ5lkuq5LRUWyawFkopCIc8YMiwp3Op3S2Ch/+YtF5QMYhXVDGGruO7FIy4RIqLe3V0SysrJG+oVxzo75fL6hB0ea9rPu25T47Fhnp0XlAxiF2+X6t2WRkMPhiOi6MOgLTDp9+vQknn3YGEMxIRIa09SpU8fzaxNa6+RyuaLRaKI1GkN/ybW1FpUPYDT19WLZBJau6ytfeUUKCiwqH8BIXCKuN96IRqMWDWREo9FRBoSGxhjx2MiEdULxWbBBYrGY+rB48WLjZxlK7R9gRclm7QIOIAFerzcdp9QBjMnlclm0y+DoYdDoTIiE5s2bJyIPHz4cdDwepsyaNcv4WYaydNVVCi7pAjKEdfdKoZ8DJJV18zlGmrYJkdDSpUtF5P79+4OOq5XUIjJonyGz+Hy+cDhsRcnhcHiUCUUAVotGo1aM+EajUSMdRwAGWffgNnLHMCESKi4uFpGhrxi7e/euiHg8ngULFhg/y1AFBQXWzY4VsIwASBKXy1VQUGDF7VLTNJo2kERer1dleJlecjgcLikpSexvTYiEVq9ePXPmzDt37ty4cWPgcbVN87p164yfYlgulysnJ8f02yX3SiDpSkpKUu1eCcA4l8u1bNky0x/c4XC4oKAgmeuEsrKytm3bJiJnzpyJH4zFYhcuXHC73Rs3bjR+ipGUlJSYvklaOBwuLCw0t0wAE2JFP8fgvRKAKazo52iaZuTBbc676Ldu3VpYWBgIBP7973+rI1VVVZFIZP/+/dOt3D/A6/Xm5ORcvHjRrAKDwWBOTg4LKoGk8/v9mqaZdceMRqMXL16kkwMkncvlWrlypYkPbk3TDD64zYmEROTgwYNf//rX169fX15evnXr1j/+8Y/Hjx//2te+Zlb5I/H7/dFo1JS+oxpe2rVrl/GiABjkcrnWr19v1u3y4sWLfr+fTg6QCgoKChwOhylTOrquRyIRgw9u03ZWdDqd+/btM6u08XO5XLt27fr5z3/ucrmMbNak63ooFCIMAlJHQUFBKBS6ePFifn6+kXKCwaBahW1WxQAY4XK5/H6/enAbmbCORqPBYND4g9u0MaEkUn3HlpaWhAPMcDisvk26jEBKKSkpycnJaWhoSGwPEnWjVP0l0+sGIGHxQd+EH9y6rjc0NJgy1vvE48ePDRZhCp/PN6G3bQyl63plZaXb7Z7ol3Lx4sVoNMrIOZCadF1vaWlpaGjIz8+fUPdR1/VgMFhSUvLiiy9aVz0ACUv4wa1pWiQSMfjgjgce9omEZMAd0+PxjPntRKPRrq6ucDick5NDfxFIcZqmVVdXu1wuj8czZjykWrea76aHA6SyJD647RkJKZqmBYPBjo4OddOUAa/OUAPsalunUChUUlLi9Xq5UQJpQd0xg8FgT0+PioccDkd8dWA0GlVNOxwOOxyOwsJChoKAdKHrel1dnXpwq1W/Iz24XS5XSUmJKcv+7BwJKeqmGQqFdF3Xdd3pdKpvU33LPp+PuySQjnRd1zRNNW1N01QkFH+Hhtfr9fl8LI4G0tGYD25ztwSzfyQ0iK7r7KgG2I/acIjWDdiP1a07HniYlkWf4rhRArZE0wbsatJatx2y6AEAABJDJAQAADIXkRAAAMhcREIAACBzEQkBAIDMRSQEAAAyF5EQAADIXJkSCfl8vmRXAZOHy51RuNwZhcudUSbncmdKJAQAADAUkRAAAMhcREIAACBzEQkBAIDMRSQEAAAyF5EQAADIXERCAAAgcz3x+PHjZNdBhC0iAADA5AqFQpI6kRAAAMDkY3YMAABkLiIhAACQuYiEAABA5iISAgAAmYtICAAAZC4iIQAAkLmIhAAAQOYiEgIAAJmLSAgAAGQuIiEAAJC5iIQAAEDmIhICAACZi0gIAABkLiIhAACQuTIiEurr62ttbb1y5UqyKwJzcEExSCgUSnYVYBquJmKxWHt7e3Nz87179ybhdNMm4RzJVVNTU1tbW1hYePv27U8++WTPnj15eXnJrhQSZ/CCfve73+3u7h54ZOvWrT/84Q/NriYmyV//+tf9+/d3dHRcunQp2XWBUUauJk3bNmpqampqah48eKD+c/ny5W+99VZubq51Z7R5JLR79+7Tp0///ve/X7RokYgcOHBg06ZNgUDgS1/6UrKrhkQYvKD19fV///vfBx6ZNm3a2rVrLakrLNbW1lZdXd3a2trX15ednZ3s6sAQg1eTpm0bb7755vvvv//0009/9atf/dvf/haJRNra2tatW3f06FGfz2fRSZ94/PixRUUnXX19/a5du1599dWf/OQn6kgsFluxYkV2dvaZM2ccDkdyq4eJMn5B16xZ8/jZuXoAAAVISURBVP3vf9/tdsePuN3u5cuXW1VjWCkSibjd7t/97ncVFRXZ2dmMCaU1g1eTpm0P7e3tP/7xj/fu3btixQp1RP0vISLPPvvsqVOnLDqvbceEYrFYZWWliKxZsyZ+cMqUKatWrXr//fePHj3KqGl6MX5Bz549O3v27M2bN1tbUUwW9dibP39+sisCExi5mjRt2zh58mR1dfWyZcviR15++eXbt29XVVV9/PHH165d++IXv2jFeW27Yrqpqam7uzs7O1tNo8SpXsKxY8eSVC8kyPgFPXjwIKPl9pOVlZXsKsA0iV1NmrZt5OXlDQyDlI0bN6oP4XDYovPaNhI6d+6ciCxcuHDQ8Tlz5ojI9evXr127loRqIVEGL+if/vQnTdPeeOONL3/5y2+88QbJKYA90LTtZMOGDUMPut3uadOmiZUDwLaNhFR78Hg8g44vWbJEfdA0bbLrBAMMXtBf//rX6sODBw9Onjz50ksvlZeXP3z40IKaApg8NG3b6+vre/To0dy5c4f2hM1i23VCV69eFZGhq2hVaCkikUhksusEAwxe0OPHj2uaduPGjaamptOnTz969OjYsWP//Oc/Dx8+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qlUqtViuXy5ZlhUIhy7Jc19XZSEqZTCbf//73CyHE//t/IpMRp08PuecARtues2fPDrsPQggRi8VqtdqwewFgJC0siFJJHD++8TUdfZRSSil5zuYvnJoSk5Nifn5gPQUwLtrBgzkhACPvyJFNMUicu6j1AluhczkxNSVmZ8XEhGedAzDe2CcEYLRNTYl0WkxO9vK1ExNiclIsLBjuEgAfYU4IwAgrlUSpJPpZxJ+fF1NTolTqMUsB8DvmhACMsIUFkcv11cLEhJifF5mMoQ4B8BuSEIBRlc8LIUT/x0NPToqJifXWAOB8JCEAo2phwUzZl54WYrcQgO2QhACMpIUFMTlpbHOPnhZijQzAFuyYBjB66nVx5IjhQxF1RX29TkU9gI2YEwIwejIZceSI4cgyMSHSaaaFAGxCEgIwYnTlvBcHQ8/OinpdlErmWwYwtkhCAEZM/5XzO6GiHsAWJCEAo8RU5fxO0mkq6gFsxI5pAKMkk9l6xZhheuu0d2ELwFhhTgjAyMhker9irHP6MjLWyAAIIZgTAjAq6nWRzxuunN8Jl5EBOIc5IQCjwYvK+Z1w6jSAc0hCAEZAqSTqdU8q53cyOUlFPQBBEgIwEjIZryrndzIxIXI5dgsB2DEJ1Wq1PpteW1v793//9xMnTqyurvbZFAA/y+fXdzEPmL6MjDUyINi22TH9+OOP33fffcvLy08++WRvjR4/fvyzn/1ss9mcnp6+7rrr+ushAL8bQOX8TnRF/ewsl5EBgXVeEjp16tTRo0dPnjy5urq6b9++Hpp77rnn/viP//jrX//6hz70oZmZGUOdBOBfg6mc34mei/LuVGsAI++81bFrrrkml8vNzc311tZ///d/33LLLU899dRDDz1EDAJwYaWSyOeHnELm59dvOgMQSOcloXA4LIQ4cOBADw0999xzv/Ebv/Hd73734x//+LXXXmumdwD8bRQmY6ioB4Jtmx3Te/fu7aGh3//93//ud7/77ne/+/Wvf33fvQIQAPm8qNdH4tYLvTbHZWRAIJmpov/7v//7r33ta6FQ6N3vfreRBgH43yhMCGlMCwEBZiYJffzjHxdC/Pqv//qll176/PPPnzhx4tSpU2tra0YaB+BDCwvDqZzfCRX1QFAZuHfs5MmT3/nOd4QQBw4ceNe73vWv//qvZ86ceeGFF6SUc3Nzv/qrv9phO7FYbOuL/R9rBGDk1OviyJGhVc7vhIp6wL+2zRiagST0pS99Sf/Df/7nf/7pn/7pVVdd9eKLL/7Zn/3ZZz7zmT/4gz+4+OKLf/mXf7mTdgg9QFAMt3J+JxMTIp0eoTU7AOZszRjtbGRgdWxlZUUIce21137oQx+66qqrhBB79+49fPjw6173OiHEwsICy2QAXqZL1kczbczOUlEPBI2BJPTcc88JIX76p3960+uZTEYI0Ww2T5w40f93AeATozzpordOcxkZECQGkpA+jfrHf/zHN70+NTWl/+H73/9+/98FgB/oSvVRqJzfid46TUU9EBgGktBll10mhNh6zWooFAqFQv23D8A/FhbE/PywO7ErKuqBgDGQhH72Z39WCPHtb397m9YvukgI8RM/8RP9fxcAY29hQUxOjtxG6a10J1kjA4LBQBLSdfL/8R//8eyzz276oxdffPEnfuInkslk/98FwHjTlfMjPiHUxmVkQGD0koSWl5fvvffeZ555Rv/fiYmJt73tbUKIz33ucxv/2pNPPvnCCy+8+93v1jNDAAItkxFHjozNUT3tinoAfrdNRnFdVwixurr64osvbv3TtbW1O+644/777z906FD7xUOHDl133XXHjh17+umn9Ss/+tGPPvKRj/zCL/zCu971Lm96DmB8lEqiXh+bCSFtdlbU60wLAb533smKJ0+e/MIXvvDoo48KIVZXV2+77bYbbrghnU5feeWVG//a/v37z5w5c8UVV7RfsSwrl8vNzc29/e1v/83f/M3LL7/8H/7hHxKJxPvf//7B/GsAGGmjXDm/k3ZF/enTw+4KAA/tOXv27LD7IIQQsViMM6YBf8rnxYMPjtzdGh3S92+Mctk/gJ60g4eB2zYAYDeZzLjGIHHuMjKSEOBf7GUG4KXRvGKscxMTVNQD/sacEADP1Osinx/7fTbz82JqSpRKY5znAOyMOSEAnhmvyvmdcOo04GskIQDeGMfK+Z3o2SAq6gE/YnUMgDcyGe8q55VSjuMopZrNplJKSimEiMViUkrbts1/PyrqAf8iCQHwwMLC+l5jo5RSlUqlUChYliWltCxLCBEOh13XVUqtrKzog2GTyWQikdDxyBh9R/3o3yALoEucJwTAA3v2iOPHzSahYrFYKBRisVgkEtEZaFuu6zqO47qubdtps9Xv9bqYmhLHj4/9zicAG4IHSQiAabrm3NzSmFJqcXHRsqx4PN7hl7iuu7Ky0mw2s9msyckh0/9qAIaFJATAG6WSmJoS5t5YKpVKPp+Px+PRaLTbr3Ucp9lsJpPJmZkZM72p18XVVxuf7gIweO3gQe0YAKOMXjHmOM7DDz+cTCZ7iEFCCNu24/H40tJSpVIx06GJCZHLUVEP+AlJCIA5+byo103dTaEXxeLxeD/LW5ZlJRKJQqHgOI6RXq3PBuXzZloDMGwkIQDmGJ0Q0oti/e/ysSwrGo3mTWUXDloE/IUkBMAQo5Xz+XxeKdXbothW0WjUsixjYUhX1HMZGeALJCEAJtTr4sgRgxNClUql80qxTti2bWyBTAiRy60fog1gzJGEAJhg9IqxSqWiZ3GMtKbp1kxunU6nWSMDfIAkBKBvpZIolQwevlwul02ti20Ui8UKhYKx5mZn1//FAYwzkhCAvhndKC2EcBzH8F0ZQgghQqFQq9VSSplprn0ZGYBxRhIC0B+9DdncvRaVSsWLGCSEsCwrFAoZS0Li3NZpKuqBcUYSAtAfDy4lNbtDaFPLJpMQFfXA+CMJAejDwoKYnDR79YRSamySkBDr//qskQFj6+JhdwDA2NKV86dPm2212WyabXCjUChkvv35eTE1JUolLiMDxhFzQgB6ZbRyvi0cDpttcCOllPn2WSMDxhlJCEBP9LmCpncICSFs2/Z0WsiT7diTk6Jep6IeGEckIQA9MV05v1Gr1fKoZaWUJ0mIinpgbJGEAHRP1417sy3GoxJ6zXVdr9pPp6moB8YRSQhA9zIZL9bFNCllJBJpNBrGW240GolEwsOklcuxWwgYOyQhAF3KZEQ67WmdVCqVMnlb6jmO4ySTSePNvmxigop6YOxQRQ+gG6WSyOeNV85vYtt2JBIxu6en0Wi4rmvbtqkGt0dFPTBumBMC0I2FBS8q57dKJpPVatVgg41GI23uSpAdUVEPjBuSEICO5fMeVc5vlUgkDh482EMYcl1364vlcllKmUgkTHTtQvRsEBX1wJggCQHomJeV81ulUinXdbvdMLT1pg69+TqbzRrr2e6oqAfGCkkIQGcWFtZ3BA+KlDKbzTYajX52TzuOU61WB7EutpG+o541MmAc7Dl79uyw+yCEELFYrFarDbsXAHa2Z484fnzwG4GVUouLi5ZlxePxbr+2XC4LIbLZrKdnFG2vXhdTU+L48QHsqQLQg3bwIAkB6IBe6xng0thGSqlKpbK0tGTbdjQa7eRL9EzSwYMHBz0btNFQf2gAdkcSAtCxUklMTYlhv1dUKpVyubyyshKNRiORyNb9QEII13VXVlZqtZqUMplMzszMDL6fL6vXxdVXD2UiDcAFtYMH5wkBuJDBbpTeSSKRSCQSen6oUCjoJNRe9nJdVyklhEilUrOzs0NYDttqYkLkciKT8fr4JQD9IAkB2JW+SGuIa0znk1LOzMzMzMyoDaSUUkrPT03sweSkePBBkc+Pzg8QwCasjgHY1dVXi1yO9Z3elUpMCwEjqB08qKIHsLOBV877kK6o53ghYFSxOgZgB/W6OHKEyQwDcjkxNSXqdSrqgRHEnBCAHWQyg7lizP8mJkQ6zUGLwGgiCQHYTqkkSqXBXDEWCLOz6z9SACOG1TEgWJRSjuPognN97k44HN6m9mo0Kuf9o30Z2YbVxq1jEYvFhBAjWgcH+BRJCAiKYrFYLpdbrVYoFAqHw0KIcDjsuq6+711f4b5+GuGIVc77RDqtK+pVKqVPidw4FpZltVqtpaUl13X168M/GRIIBqroAf8rFov6KMLdb6vQF7+7rmtXKun5eUrGjFOPPFL5m78pSBmLxXY6JlucOyWy0WiIdjYFYNqFb9uo1Wp6nrY3zWbzqaeeev3rX79///6uOgTAIKVUPp9XSsXj8Z2eu5voCyuazeZwLi71rx6ukm1nU8YCMG6384Qef/zx22+//ZZbbunnG/zu7/7ue97zHsdx+mkEQD8cxzl06NCePXuSyWSHMUgIoaeOwuHw4uJisVj0tIfBUalUDh06FI1GO49B4vyxqFQq3nUPCLLz9gmdOnXq6NGjJ0+eXF1d3bdvX8+NfuITn9A7DwAMi+M4i4uLyWSyt7kE27YjkcjS0pIQgtWZPjmO8/DDD/c2FjoMSSkLhQI7qQEvnDcndM011+Ryubm5uX5afOqppz7zmc/01ysAfdGLYj3HIM2yrEQiUS6Xmdzth46k8Xi827HQe9g1KWU0GtULnaY7CATdeUlIlzAcOHCg5+ZarVY2m7333nv77ReAPuTzeV0b32c7lmXpB7CJTgVUz5F004JmNBoNh8OMBWDcNvuE9u7d23Nz995775ve9KZkMtlHlwD0pVKpKKVMLaNEo1EpJQ/g3uifm6nNzpFIRCnFhiHALJNnTD/66KOnTp36wAc+YLBNAN0qFAr9FH5uFY1GWSDrTaVS6WqL9O4sy4rFYoVCwVSDAITBJPTcc8/Nzc0tLi5ecsklptoE0C09YWC24lov01BH1q1KpRKNRjuv2uuEHlmCKWCQsTOm5+bm0ul0P59Et/1aDhkCulIul3c5O7FnsVisXC5TRNYVI5Nzrutu3TBUKBSy2WyfLQOBsssvo5kk9Pd///c/+MEP3vWud/XTCKEH6J/jOKlUyniz7akIqrg7pC8UM7JpfdMrUkp9/DSAzm3NGO1sZGB17D//8z8//vGP/8Vf/EX/TQHoh1LK7FrM1va9a9xnlFJeTM6JczeUsUAGmNJvElpbW/ujP/qjP/zDP7zyyiuNdAhAzxzH8e5OhnA4TBLqnKc/q1Ao5F3jQND0uzqWy+W+/e1vl8vlcrm89U8/+clP/sM//MPP/dzP3XzzzX1+IwBD12w2h92FsdFsNr1LpZZlsVIJmNJvEjp9+vQPfvCDv/u7v9v2T0ulkv4HkhAwAJ6ujjEP0RVPx8LTNVAgaPpNQrOzs29+85u3vv6e97xHCPGBD3zAtu1XvepVfX4XAJ2QUjabTaYKRoGU8rnnnvOocdd1uZoeMKXfJHTttddee+21O/3p61//+htuuKHPbwGgQ54+HZVSBg8J9L1wOOxpEvKoZSCAetkxvby8fO+99z7zzDPGewOgH1LKVqvlafveNe4ztm17t62q1Wox8weYss2ckP60sbq6+uKLL269g2xtbe2OO+44c+bMN7/5zQceeGAQfQTQMdd1t57FZ4TBu8wCwrtUyuoYYNB5SejkyZNf+MIXHn30USHE6urqbbfddsMNN6TT6U0V8vv37z9z5swVV1wx0J4CuBApZSKRWFlZMR5ZGo1GJBLh6ds527b1hanGf2iO4yQSCbNtAkF2XhK66aabbrrppt2/4KKLLjpx4sQF2+XAaGAoksnk0aNHvUhCXhxd7W/JZLJQKCSTSbPNNhoNrj0BDDJ5Fz2AoWtPRRhs03VdpRTzEN2ybVspZXYs9OQcy5SAQSQhwG+SyWS1WjXYYLVaZUKoB1LKVCpldoKcyTnAOJIQ4DeJRGJ6etpUGNI3eLAc05tEIiGlNHVHWLlcllIyIQSYRRICfCiRSIRCof4fwEqpWq2WTqdNdCqIpJTpdLrZbPa/RqYjaTabNdIxAG0kIcCH9AO40Wj0E4aUUuVyOZvNUjLWDz0W1Wq1n7FwHKfZbLIuBniBJAT4k5Rybm6u2WxuezvyBTmOU6vVstksazH9s21bj0VvYahcLjebTcYC8Mies2fPDrsPQggRi8UovAeMU0pVKpWlpSXbtqPRaCdf4rputVplIcY4pVShUFheXo7H4x1OsymlqtXqwYMHWaAEjGsHj37vHQMwyqSUM699rcxmy5HI0tJSNBqNRCLbnkDtuu7Kykqj0QiFQslkki3SxulSsnA4XC6XW61WNBrdaY5Hj0WtVpNS3vqNbyRuvnnAXQUChTkhwO+mpsTkpJif13MSlUrFsqxQKGRZlo5EzWaz1WrpCxxSqRTnBg1ApVKp1WqbxkLflKL3VuuBkFKKfF48+KA4fnzYXQb8ph08SEKAr5VKIpMRp0+3X9APWnWOEELPTLAHZfD0z19vHmqPhZTyvLWzel1MTYlcTkxODqeXgE+RhIBguPpqHqJjb0ucBdC/dvCgdgzwr3xeTEwQg8be5KSYmBALC8PuB+BPJCHAvzIZMT8/7E7AhFxO5POiXh92PwAfIgkBPpXJiHSaCSGf0HN7TAsBHqCKHvCjUknk82I0dgHCjPl5MTUlSiXSLWAWSQjwo4UFkcsNuxMe0oVvuuqq2WwKIWKxmC658m0R3MSEmJ8XCwskIcAskhDgO3pDiU9PJS4Wi/pkwvYxPKFQSAixtLSkD+Px86lIk5PiwQdFPu/XwQWGgip6wHd8WjlfLBYLhYJlWbvcHKLDUKPREEL486RsKuoBQzhPCPCphQVRKvnsSGKlVD6fV0rF4/FtrwrZSl9YoS8u7fCSr7Fx7tDwYfcDGG8kIcCP6nVx9dXi9GkxMTHsrhjjOM7i4mIsFut2A1A7DPltckifOn38uJ9GGRg8TlYE/EhXzvvoAaljUDKZ7GEftF5Hi8fj1WpV7632iYkJkU5TUQ+YQhIC/KJUEqWSn0rGlFI6BvWzvGVZVjQa1YtrBvs2ZLOz68MNoG8kIcAvfFc5n8/n27Xx/ZBShsPhxcVFI70aCbqiPpMZdj8A8go/nAAAIABJREFUPyAJAb6Qzwsh/FRcXSwWlVKmDgeKRCJCiLz+KfmDvozMT/9GwJCQhABfWFjwWTFRuVyOx+OmWrMsKx6P+223kD5oEUB/SELA+NPnDvvoAKFKpSKE6LBgvkO6NV+FIT3orJEB/SEJAWOuXhdHjvhvQminsxP7EYvFCoWC8WaHaX6erdNAn0hCwJjLZMSRI36qnBdCOI7jRRIKhUKO4/iqiIyKeqBvJCFgnJVKol732YSQ4zgenQptWZaU0ldJSAgxOyvqdaaFgJ6RhIBx5rvKeSGEUsrsDqGt7XvX+BBQUQ/0hyQEjC1dQe2jjdKap0koHA77LQkJsX6wOBX1QE8uHnYHAPQqk/HZTatas9kMhUIeNR4KhZrNpkeND1MuJ6am/HSgFDAwzAkB40lfMea7CSEhRDgcbrVaHjXearXC4bBHjQ/TxAQV9UBvSELAGCqVRD7vs43SbVJK76ZtXNf1aDv28FFRD/SEJASMoYUF/1XOt3maVJRSvk1CnDoN9IQkBIwbP1bObySl9G51THictIZMr5YyLQR0gyQEjJtMxn+V8xtJKSORSKPRMN5yo9E4ePCgn5MQFfVA90hCwFhZWFjfG+trqVTKiwvCGo1GLBYz3uxo0XfUs0YGdIwkBIwV310xti0pZSgUMnvwj1JKKZVIJAy2OaJyOZHPi3p92P0AxgNJCBgf/q2c30RKmUqlqtWqwTZrtVoqlTLY4OjSs4ZMCwGdIQkBY0JXzvt6h9BGtm1HIhFTYUjfZTYzM2OktTEwPy/yebZOA50gCQFjwo9XjO1CSplOp13X7X/rtFKq2Wxms1kjHRsPExMil2NaCOgESQgYB3rbR8DuUpBSZrNZx3H62T2tlCqXy+mA/eiEOFdRz2VkwIWQhIBxELAJoTYp5dzcXLPZ7C0MOY5Tq9Wy2axt28b7Nuo4aBHoDEkIGHnBqJzfiZ4ZuuKKK5aWljqvJnNdt1wunz179u677w5iDNJ0RT3HCwG74i56YLTV6+LIEXH69LD7MUy6lCwcDpfL5VarFY1Gdwk3juM0Go1QKJRMJgO0RXon+o76et2vd7MA/dtz9uzZbf+gVqt1ewTZ2tra8vLyD3/4w9e97nX79+/v6mtjsVitVuvqS4BAmJoSk5NBOEOoE0opx3HK5fLKyoo4d2+GZVlCCH1pq75WLJVKBeLcoA4tLIh6PZirq8Au2sFjmyT0+OOP33fffcvLy08++WTnLR47duzYsWM//OEP9f+98cYbP/zhD090/CmEJARso1QSU1Nih48rQabOEULUarVwOCyllFIGdyFsF/W6mJoSuVxgF1iBbW2fhE6dOnX06NGTJ0+urq7u27ev8yT0oQ996DOf+cyrXvUq27b/7d/+TX84279//6c//ekOJ5ZIQsA2pqbE7GzQSsZgXj4vFhYCvsYKbNIOHuftmL7mmmtyudzc3FxXbT3xxBP//M//fP/99x8/fvzYsWNf/epX5+fnhRDf//7377rrLoOdBoJF1z8Tg9C/dFpMTFBRD2zrvCQUDoeFEAcOHOiqiUceeeTo0aNvetOb2q/cdtttv/M7vyOE+MY3vvH000+b6CcQPAsLbA+CMVTUAzvYpop+7969XTVx/fXXHzx4cNOL73znO/U/9H8+LBBECwticpKNHTBG/+dERT2whYEq+re//e1bXwyHwxdffPFLL73U7QwTACrn4Yn5eTE1JUolEjawkVfnCa2urr700ks/9VM/9ZrXvMajbwH4ViYjjhzx6AAYXW/lOI7+h2azGYvF5DlefEfsRG3QbDY9r4BrnzpNEgI28CoJnTx5Ughxxx13dP4l21aZUVCGwCmVRL3uxQ4hpVShUFheXhZChEIhvS/Qdd2VlRXXdTmJZ5CKxaI+JVIIIaXURyLpQ5Jc1xVC2LadTCbNR6LJSbGwwLQQAmiXSnavktDnPve5AwcO3H777Z1/CaEHEMKTK8baz91oNDo9Pb3t39FhqFAoFAoFTmf2TrFYLBQKlmXtclK2Hot8Pi+EMDwWelook2HtFUGzNWO0s5EnSehb3/pWoVD41Kc+dckll3jRPuBbus7Z6Of1xcXFlZWVeDzeXvxyXVdPQmxkWZZ+PLuu6zjOoUOHstks62UG6XCjlJqent76899o41gsLS3pS2SN9SOdFg8+KPJ5DmgANPM3sK6trX3wgx983/ved+ONNxpvHPC5TMbguphS6tChQ61Wa3p6emOmueBj2LbtcDi8uLhYLBZNdSbgdLjcs2dPMpnc/ee/kWVZiURiz549hw4d6vz22QvL5dav4ADgRRK65557rr322jvvvNN4y4DPZTIinTY1IaRjUDQajcfjPXy5bdvxeHxpaalSqRjpT5A5jnP06NHe9v1sDKaO45jp0MTE+oYhAMaT0COPPFKv1++++26zzQL+VyqJfN7ghFA+n08mk9FotOcW9IREoVAwORsRPEqpxcXFjauTPbBtOxqN5g0eEj0/L0olUSoZaxAYWyaT0KOPPvr5z3/+L//yLw22CQTFwoLByvnFxUVdC9ZnO5Zl6dkII70Kpnw+r88p6LOdaDQaDoeNhaF2RT0QeMaS0Fe+8pVPfOITf/VXf7Vpl3Sz2XzmmWdMfRfAn/J5g5XzlUplZWUlmUwaac22bcuyTM5GBIneIm2qGD4SiTiOY2y9Uq/DMi2EwOslCS0vL997770b882JEyc++tGPHjt27NJLL930N9/znve88pWv7LebgL8ZrZwvFAq97Q3aiW3bxnaoBEylUjE4FpZlxWKxQqFgprl2RT0QbNtU0etzvVZXV1988cWtd5Ctra3dcccdZ86c+eY3v/nAAw8IIb785S+/973vFUJsvIRVCPHCCy8IIVKpVOeFEkAQLSys72A1QR8ebbb6Xf8KVyoVDl3sSqVSiUajZt8A9cg6jmNmnmlyUkxMcNcvAu68OaGTJ08ePnz4wx/+sBBidXX1tttuu+eee7aube3fv18IccUVVwghTp06deedd7700ksvvfTSC+fTf/nmm28exL8HML6OHDH4HCqXy2YnhDSTUxGBUSgU+tmxvpNoNGpyLHK59cVZIKjOmxO66aabbrrppt2/4KKLLjpx4kT7/954442cDQ30zmjlvBCiUqnsdIp0PwxPRQSAF5NzmpSy0WgYa65dUW/6ZHNgXHh12waAC9OV82fPmmpPl7uzHj0KlFJeTAgJISzLarVaJlPp/Ly4+moxO8tlZAgm8ycrAuiU6Q/ijuN4dz9GOBxm33TnPD2EKRQKmWxuYkLkcmydRmCRhIAh0XXppu9+8nRCqNlsete4zzSbTe9SqWVZhlOp3jrNWQkIJJIQMCQeFOwopbxLQobnIfzO0zkh86PMQYsIMJIQMAxGK+c30qdgYOiklK1Wy6PGXdc1P+Gkp4VYI0PwkISAgavXxZEjXpTq2LbtXRJSSoXDYY8a9x9Pf1ZejXIuJ0olKuoRNCQhYOAyGYNXjG3k6TyEOFdLj07Ytu3dtqpWq+XJcQYTEyKdZo0MQUMSAgZLXwDu2ZG+ns4JcZhQV8ZsdUybneWOegQNSQgYLC+PsJNSenRHWKPRCIVCzAl1zrbtSCTixb7pRqPh4bUnXEaG4CEJAQPkTeX8RqlUyuQBxOcopVKplPFm/S2ZTHpxBL/jOMlk0nizL0unqahHoJCEgAHKZLy+6tKjqQhv5yF8yrbtVqtldiz05Jzny5S5HLuFEBwkIWBQTF8xtpNkMlmtVg02WC6XiUE9kFJOT0+bnaJrNBqDmJzTRzywRoZgIAkBA1Gvi3ze6wkhLZFIHDx40FQY0ruO0l6u6PlYIpEIhUKmdm6Vy2Up5YBS6fw8W6cRECQhYCA8q5zfViqVcl23/wewUqpWqxGDeialTKfTjUaj/zUy3UI2mzXRrw5w6jQCgyQEeE+fVjeQCSFNSpnNZhuNRj9hSClVLpez2SzF8/2QUt56663VarWfMKTHYtCRdHJS1OtMC8H3SEKA97ysnN+JlHJubq7ZbPYWhhzHIQaZkkgkdBjqeSxqtdoQxoKKegQDSQjwmK5G9n6j9FZ6Zsi27aWlpc4nJFzXLZfLzWbz7rvvJgaZkkgkdDCtVqudn36px+Ls2bNDGwtdUc8aGXxtz9mzZ4fdByGEiMViXhy8AQzfnj3i+PGhJKG2YrFYLpf1FQ3RaHSnv6a3Frmum0wmZ2ZmBtnDgFBKVSqVQqEgpYxGo7uMheM4umB++GNRr4upKXH8+MB2uQGD0Q4eJCHAS3plYeBLY1sppfSC18rKSigUsixLnLunzHVd13WVUlLK4T93A2DrWFiWFQqFNo1FKpUalcMLRuY/Y8AgkhDgvVJJTE2J0fgVa1MbNJtNfWW6bdsshA1eeyD0u9/ojoWeFsrlhju1CZhFEgK8NzUlZmc9vVsDGJB8Xjz4oDh+fNj9AIxpBw92TAPeyOdFvU4Mgk/o2SAuI4MfkYQAbwyjch7wCgctwr9IQoAHFhbWb24CfGNykop6+BJJCDCtXhdHjgzyRGlgQHK59WVfwEcuHnYHgHGiK330ScFSSv2/myt9BnXnfJDpgyIvPBYwa2JCpNObVn71WDiOo/9BSqmHg7HAuCAJARfWPhBPn/uiD4DRR7/oP3359Bd9ffdolGT6klKqUChUKhU9FrryXI9Fq9UKhUK2bSeTSR7DXpmdFVNTolQSk5P6xE6lFGOBsUYVPbAbx3Hy+Xyr1YpGozu9oeuj8BqNhhAi6TgzMzOUjHmhfVL27mOxsrLSbDaFEJwS6ZV8vlgsFqS0LOuCY6FPyh6hUyKBczhPCLiwYrG4tLS0+w0VG7Ufw9lsVi8QwAilVD6fX1lZ6WosKpVKJBLJZrNedy9Q9FgopeLxuD6pfHf6c4LjONPT0wRTjBSSELCb9tt9Mpns9msdx2k2m0xImKKUWlxctCwrHo939YUEU+Mcx1lcXIzFYt0ueLWvtGMsMDo4WRHYkVLq0KFDe/bs6SEGCSFs247H40tLS8Vi0XjfgsZxnEOHDkWj0W5jkBDCsizbtsPh8OLiot5YjX44jnP06NHe9v1sHAu9sRoYHSQhYLN8Pt/Dp96NLMtKJBLlcpkHcJ/y+XwymexwRWxbekFNz/AZ7FjQ6Jm5eDze84zOxjBktm9An0hCwHmKxaJSqv9qF72ZNM/tBH3Qi2L9L6ZEo9FwOMxY9ENH0v7HIhKJCCGYLsVIIQkBL3McZ2lpqbdFsa14APejUqnobblGWotEIvooBCOtBY2eUTOyv0fv91paWmK6FKODJAS8rFAomD37JBKJtE+cQ1cKhUIsFjPVmmVZsVisUCiYajBQKpWKqUgqzi2TMRYYHSQh4GWO4/SzJWUrfQYjUxHd0j8xs0VGujWmIrpVLBaj0WgnBfOdk1I6jsNYYESQhIB1+XzebAzSbNsul8vGm/W3crnsxVhEo1GmIrrlxVjoXXT8XmBEkISAdY7jeHEtgP4wzcffrhifnNP0VASLlZ2rVCqtVsuLE4Bs2+aXAiOCJASs09cnedGyZVm86XfOcRyPDt/TlWgkoa54NxYMBEYESQgQQgjHcTyKQeLctBA65F0kbbfvXeM+U6vVvBsLPUXnUeNA50hCgBDn7pP3qHEpJZfJdM7TJBQOh0lCXQmFQsPuAuAtkhAwCDx9O9dsNr17+oZCIX1TPTrhaSpl1RgjgiQECCGElNJ1XY8ad13Xi73YfhUOh1utlkeNt1qtcDjsUePoFrexYhSQhIB1nj59PWrZl6SU3k3buK7L07dzsVjMu+lMI9faAP0jCQFCePzZ1HVdg8cl+56nY+HphjBf8nSu1KOWga54koTYHIqxo1fHPPr4yyahrkgpPZ1FIwl1zrZtT/MKY4FRYDgJPf7447fffvstt9xitllgABKJRKPRMN6s67qu6yYSCeMt+5WUUl+YarzlRqMRiUR4+nZOH7/U/1hsjVPVapVfCowIY0no1KlTmUzm9ttv/9rXvmaqTWCQUqmUF09fx3F4x+9WMpn0Ymq50Wgkk0njzfqYlDKVSvX/CWFrAZpSirHAiDCWhK655ppcLjc3N2eqQWDAPJqKaDQaqVTKbJu+Z9u2kamIjfTqJ6m0W4lEwotfikgkwnZpjAhjSUgXph44cMBUg8DgJZPJarVqsEE9IcRyTLeklOl02uxiJcsxvdGfEMwe/MPHA4wUw/uE9u7da7ZBYJASicT09LSpMKSUajab6XTaSGtBY9t2KBQy9QDWd5kxFr1Jp9PNZtPUzFC5XJZSMiGE0UEVPXCeRCJh5AHsum65XObR27P2tFD/D2ClVK1WYyx6pseiWq32X0emf7Oy2ayJfgFmkISA87QfwP2EIdd1q9VqKpXig28/pJR33nlntVrtJwwppcrlcjabZY2yH7ZtT09PVyqVfsJQo9EgkmIEkYSAzaSUc3NzzWaztzCklFpaWkomkzMzM8b7FjS2besw1NtYOI5Tq9Wy2SyRtH8zMzM6DPU8Fo1Gg7HACNpz9uxZg82Vy+VMJrNv374nn3yyqy/c6QReDmnEsCilKpXK0tJSPB7vcDrBdd2VlRW9N4i3e4OUUouLi0KIeDze4YWgelpOCMFskFl6LMLhcOf/heuxkFKyKIYh2jZm6IwxQkmI0IMRVLzllrJtt1ot27aj0ehOf01noFqtlkqlmArygg6mhUIhGo1KKXcfC8dxXNdlWs4j7Q8JeiB2CZp6HigUCiW++MXUY48NspPABbWDB0kI2NnCgqjX1V/8heM45XJ5ZWUlFApZlmVZVigU0jdCNJvNVqsVCoV47g6AUmrTWOjHcHss9LVijMUA6DxULpf1f/87jUUqlUokEiKTEUKIXG64fQY2IgkBF1Kvi6uvFqdPi4kJ/YI6p/3fajgcllJSEjx4OhIJIWq1mlJKz3vrgWAtbJDavxTi/LHQA/HyWNTrYmpK5HJicnJ4nQXOQxICLmRqSkxOivn5YfcD8IWFBVEqiePHh90PYF07eFA7BmynVBL1OjEIMGZ2VtTrolQadj+AzUhCwHYWFtjTAJg0MSHm59c3DAGjhCQEbJHPCyHY0AAYlk6LiYn13y9gZFxstjl9/Ojq6uqLL77IHWQYV5kMuxkAT+RyYmpKcMw0RomxJHTy5MkvfOELjz76qBBidXX1tttuu+GGG9Lp9JVXXmnqWwCDkMmIdNrfE0JqA7nBsPsVRHoUdB1cIMZiYkJMTopMhtVnjA5jSeimm2666aabTLUGDEepJPJ5cfr0sPvhifbhhPqYZimlZVmu67quq4+EsW07mUxyIsAAKKUKhUKlUmmPhX7ddV0dT/08FvPzYmpKlEr+/ryBMWK4ir5nVNFjJPi0ct5xnHw+32q1otFoJBLZ9sIKfUy2PhGYkwm9UywW9WmE0Wh0p6CzcSzWTyb0mXxePPgga9AYLq/OE+oZSQjDVyqJTMZ/E0LFYnFpaWmX5+4mSqlqtRqJRNLptJ+XaQZOKZXP51dWVna/uaVNzw85jjM9Pe23YFqvi0xGzM8zLYQhIgkBW1x9tc/OwNWPXqVUMpns6gvbV8lyfakp+uJSy7Li8XhXX+jbsfDpBw+MEU5WBM63sLC+l9MvlFKHDh3as2dPtzFICGFZlm3b4XB4cXFRb+ZFPxzHOXToUCwW6zYGifPHQl9q4ROTk2JiQiwsDLsfAEkI0I4c8dn2oHw+H4vF+tlyqxdx9KySwY4FjZ4NSiaT/czotMOQwY4NXy4n8nlRrw+7Hwg6khDgw8p5PX/Qf+VRNBr14QN4sHQk7X9hS49m3k/HEupZWKaFMGwkIQSerpz30ekmjuOsrKz0sCi2rUgkIoQoFotGWguaYrFoJJJq8Xh8eXm5UqkYaW0kzM+LfJ7LyDBcJCEEnu+uGCsUCgbPodGbfMvlsqkGA6VQKPSwN2gnPhyLiQmRyzEthOEiCSHY9FqDj87+dxzHcZxOirQ7p88f8tVUxEBUKpVoNLrt6U09C4VCKysrvtrGrlel/bTqh3FDEkKwLSz4bKN0uVyOxWLGm43FYoVCwXiz/lYul3vYHqRvb9yJZVnRaNRv00Lz80wLYYhIQggw31XOCyEqlYre2WNWKBQSQvhqKsJj+lDEHibnLjiHFIlE/DYQuqI+kxl2PxBQhu+iB8ZGvS6OHPHZwW663N3scozmRZv+ZnyNss2yrFarpa8n86L94dB31NfrYmJi2F1B4DAnhKDKZMSRIz5723Ucx7unYzgc9ttUxNgKhUJ+O+RpYkKk06yRYShIQgikUkmUSj7bIaR5OnnTbDa9a9xnarWad6nUsiy/JSEhxOzs+i8mMFgkIQSS7yrnNaWUd0nIh/MQY8ufSUhvnWa3EAaOJITg8V3l/Ea7Vx71yVcbU7zXarU8atl1XX+ORTotJiaoqMeAkYQQPL6rnG+zbdu7JKSUCofDHjXuP57+rDzNu0NGRT0GjiSEgMlkxOSkzyrn26SUm+YhzD4y/TkP4Q3btr3bVtVqtQweIz5a9K8na2QYIKroEST1usjnfVY5v8mm6GNw25DB+7MCgtWxHs3Pi6kpUSr59RMLRg1zQggSP1bObySltG270WgYb1kp5fOnr2lSykgk4sW+5kajkUgkjDc7Qjh1GoNFEkJglEqiXvfrDqG2VCrlxak/jUYjlUoZb9bHpJTJZNKLVNpoNJLJpPFmR8vkpKjXqajHYJCEEBg+rZzfxKOpCP/PQ3jAtm3jA6GUCsQyJRX1GCCSEIJB1+UGYNuBnoqo1WoG26xWq4lEgqWxbulUWq1WDbZZq9XSPj0AYjNdUc8aGbxHEkIwZDK+Xxdrs21bSmlqjUzvEArK09e0dDrtuq6pNTJ9m0qAJudyOZHPi3p92P2Az5GEEACZjEingzAhpEkp0+l0s9nsf2nGdd1yuUwM6pmUMpvNOo7T/1gopZrNZjabNdKx8TAxISYnmRaC10hC8LtSSeTzwZkQ0nQYqlar/cxGuK5brVbT6bT/d6V4SUp56623VqvVfs52UkoFNJLOz3MZGbxGEoLfLSz4u3J+J7Zt33nnnY7j9LZMppRaWlpKJpMBWovxTCKRmJ6erlQqvY2F4zi1Wi2bzQYxklJRD++RhOBrepNBwCaE2mzbnpubazab5XK58wkJ13Xbj96ZmRlPexgcMzMzeiy6CkN6dVIvigUxBml6XZtpIXhmz9mzZ4fdByGEiMViZqtdACGEuPpqkcsFZ4fQtpRSlUplaWlJShmNRncpAXNdd2VlpVarJRKJIC7EeK+rsXAcx3XdZDJJHhWlkshk/H06PAavHTxIQvCvhQVRKonjx4fdj5GglHIcp1wur6ys6AewlNKyLNd1W62W67pKqVAoxHN3AHQeKpfLrVZr41joXdWMxfampsTkZGDnd+EFkhACYM8ecfx4wCeEttKRSAhRq9WUUlJKfWu6bdvBXX8ZBrWBLvTTYyGl1LemDLuDI6ZeF1NT4vjxAO75g0dIQvA7fTptAA6VBoKCX2oY1Q4e3EUPP9KV86OR8gGYwR318Aa1Y/CjYFwxBgQLl5HBGyQh+I6+YozSJ8B/JifFxMT67zhgCEkIvrOwQIEJ4E8ctAgPsE8I/rKwICYnvdtGoOucdfmV3MCjb4dd6LFoH1RIvdUQtYvg9P/1diz0tFAmwwo4TCEJwUfqdXHkiBfHr+kDYAqFghDCsqxQKGRZljh39It+308mkzyJB2DbsdAnaDMWA9YeC/3r0B4LfUKVlFLf1mL+o0IuJ6amRL1ORT2MoIoePuLB2WtKqUKhUKlUYrHYTtM/+mjmRqPBaXiechynUCisrKxEo9FIJKKfvpu0j8mWUqZSKS5N80ixWNQnQ0aj0Z1Cpx6LZrPpSTbl3FT0jfOE4DulkpiaMls5XywWl5aWdnmv36TRaDiOE4lE0uk0S2ZmdTUWeq7OcZzp6WmCqVlKqXw+v7KyYtt2NBq94N93XbfRaCilDH9I0ActBv4uHfSDJATfmZoSs7MGS8YWFxdXVlYSicS2cw87aX8OzmazhCEj9KNXP0q7+kLGwjil1KFDh2KxWLcTPK7rViqVSCSSzWaN9SafFwsLXEaGnrWDB7Vj8AXTlfOLi4utVmt6erqrGCSEsCzLtu1wOLy4uNjVrePYln707tmzp9sYJM4fi/ZmXvTMcZxDhw71ts5lWVYikdizZ8+hQ4eMdSidpqIeRpCE4AuZjMHtQfrBGY/He27Btu1YLKZnMkz1Kpjy+XyfF6LpRZzFxUWDvQogpdTi4mIymex5dq0dTPMGs0suR0U9+kcSwvjLZEQ6bWq7QLFY7GEhZit9myYP4H7oSBqLxfpsJxqNWpZl8gEcPPl8XhcN9NlOJBJxHKdYLBrplZiYEJOTnDqNPpGEMObqdZHPm5oQ0tVJ/T96NT2TwQO4N5VKZWVlpf9Iqtm2vby8XKlUjLQWNHp200jxl2VZ8Xi8XC4bWzuenxelkiiVzLSGQCIJYcxlMuLIEVPHipTL5Xg8bnB3bTweZ7dQbwqFQj8LlJvoB7A+hQjd0qdImGrNsqxoNGpsLDh1Gn3bJgmtrq6ePHnyW9/6Vg/NPfvss1/5yleeeOKJtbW1vvsGXEipJOp1gzuEKpVKJ4XBndMbro2tBQRGpVJptVpmC750awTTbulfCuNjsfFM6n5NTop6nWkh9GxzEjp27Ngb3/jGhx56aGFhIZVKPfnkkx02dOrUqZtvvvmP/uiPvvSlL/3d3/3d9PT0Pffc86Mf/ch0h4ENjJ64XywWzcYgLRaLlctl4836W61W8+KQaJNTEYFRKBSM/15YlmVZlrHFSu6oR3/OS0KHDx8+evTopz71qfvuu+9Tn/rUL/3SL91+++1PPPHEBVs5depUJpP5jd/4jVwud+TIkbvvvvvzn//8v/zLv/z2b/+2Zz1H4OXz6/slDSmXy14kIaYiemB8ck4zPBURAJVKRd9hYrxl27ZNfkLCAuaxAAAgAElEQVTQFfWskaEnLyehYrH40EMPZTKZa6+9Vr/y3ve+99JLL33/+9/farV2b+Xw4cPXXHPNO97xjvYrl19++W/91m+Vy+UvfelLXvQbMFs5L87dWmWwwTbLsnj6dk7fbutFy3qxkrHoiheRVHjxS5HLiXxe1Osm20QwrCehtbU1Xe77q7/6qy//2UUX/cqv/Mp3vvOdT3/607s08fzzz58+ffryyy/f9PpP/uRPCiFOnjxpuMuAMFw5L4RQSnV7iGLnSEJd8XQsBEmoG7VazdPfC5NzpXqGmGkhdG89CT366KPf+c539u3b154Q0m688UYhxN/8zd9csKGvf/3rzzzzzMZXvvvd7wohXvva1xrrLKCVSiKfN7hDSHg5DyGEkFJymUznPE0q4XCYJNSVUCg0Ni1TUY+erCehL37xi0KI17zmNZv+WM/r/Nd//dfTTz+9UxOXXnrpq1/96tXV1Ww2294ivba29tBDD0Wj0be85S2edBxBtrBgNgYNAE/fzjWbTU+vCWs2m9417jNez5Ua3j9HRT16sp6E9AfWrevBP/MzP6P/Yff/Xu+66y4hxNe+9rV3vOMdzz77rBBibm7u+eefz+Vyl1xyifFOI9D0VgBzV4xpUkrXdc222ea6rheVUH4VDocvuDexz/a9axxdMR959Yo5x5miG+tJ6Nvf/rbYbq7y4osv1v+w+6eoX/zFX9T36j311FM333zz7/3e7z3//PP/+I//6NFWOwSaZxNCnj590TkppXfTNq7rci9953S1nUeNmzq3+jxMC6F760HnzJkzQoi9e/fu9Pd2WR3TZmdnX/GKVxw+fFgp9eUvf/kjH/nI/v37u+rKtmeYsrsC51lYMFs53+bp01EpNT097V37PuPpWHg38+dL4XB4ZWXFo8a9GovJyfWKeqO1pRh3u5yTfnGHTfzYj/3YBf/OD37wgze84Q1PP/20Uuquu+761re+9Yd/+Ied9pHQgwuq18WRI+L4cS/a1qtjrut6sSuCp29XpJTezc+1Wi1WKjtn+NSfLbxKvbmcmJoSs7Om7uGBD2zNGO1stL461l4F26R9acZ11123+/c4fPhwoVDI5XKPPPKILkC7//77/+zP/qznTgObma6c38S2bY8+/iqlEomEFy37kpQyEol4sSjTaDQikQirY53TqdSjsfDwl2JiQqTTrJGhQ+tJ6KqrrhJCbL0co/0LcNlll+3SyrFjxx566KE///M/37t371VXXfXZz35WX52Yz+dPnDhhvtcIIF0c62XJWCqVajQaxputVqvEoG4lk0kvJokbjYapy+0DQkp58OBBL34vHMfxdixmZ6moR4fWk9DrXvc6IcTzzz+/6Y/1TmohxKZzhjb63ve+97GPfey6665rF+Hv37//k5/85NVXXy2E+Ou//mvjnUYQeV85b9u2F1MRSqlUKmW2Td+zbduLqQgm53qQSqWMD4SenPN2mZLLyNCx9SQ0NTUlhNh6xdj//d//CSGi0eirX/3qnZp47LHHXnjhhYnzl2P3799/7733btsm0DVdE2u6cn6rZDJZrVYNNug4DssxPfBiKqJcLhODeqAXK80e/DOgyTm9dZqKelzIehJ6y1vesn///v/93//97//+741/rPfKve1tb7tgQ2fPnt30yvXXX79v376tt3AAXRtUGUgikYhEIqbCkFKqVqsxIdSbVCrluq6pB7Ce1Uh7H6Z9KZ1ONxoNUzND1WpVSjmIVEpFPTqznoT27t2r743/p3/6p/afra2tffWrXw2Hw+985zs3fs3y8vK9997bvlvjjW984yte8YrHHnusvb1aW11dXV1dffOb3+ztvwF8b2FBTE56t1F6k3Q67bpu/7MRrutWq9VsNkulUm+klNls1sgDWClVLpeJQT2TUt56663VarX/KkillOu62WzWSMcuTE8LsUaGXb18F/273vWuZDKZz+efe+45/conPvGJZrN53333XXrppe2/tra2dscdd9x///36KEUhRCgU+pM/+ROl1H333bex6Y997GOvetWr7rzzTu//LeBfunJ+gOeC6Aew4zj9hCEdg6anp4lB/TDyACaSGpFIJKanpyuVSj9jMZxImsuxdRq7O694/uMf//j8/Pytt9768z//841G43/+53/+9m//9vrrr9/0Nfv37z9z5swVV1zRfuXXfu3XLrnkknvuueepp55661vfKoQoFouXXXbZww8/3O35isB5Mhlx5MiADwWRUt55550PP/xwb2fP6Lf7VCo1MzPjRfcCJZFIKKWWlpai0Wg/Y0EM6p9ez1paWorH4z1sfXMcp9lsDiGStivqBzWvjLGzZ+v+nqGIxWKcrIjNSiWRyYjTp4fyzZVSi4uLQoh4PN75cYuO49RqNWYgzNJjEQ6HO/+puq67srLSbDbT6TRjYZDjOEePHu0qmOppOSHE3Xff7WXXdlavi6kpkcsRhrBRO3iQhDDCpqbE/PwQ37yUUpVKZWlpSUoZjUZ3+Rysn7u1Wk0vrlEsZlwPY2Hb9uD2owSJUiqfz6+srESj0UgkssvnBL3nvdFoDH+KNJ8XCwvD+liF0UQSwsjL58WDD3p0t0ZX9DO4VqutrKzoB7CU0rIs13X1pRD6utBkMplIJMhAntJjUS6XW62WHoVQKLRxLBqNRigUSiaTLE16rZ1N9RDo//LbY+G6rlJqtMZC37/BxnmcQxLCyNuzRxw/PlKz2Y7j6MJ4ca4qW19bM6CSYJyjztk6FrZtsxY2SEopfdJBrVbTAyGlDIfDYgTHQq+RMS2Ec0hCGG266tXjQ6UBBAtvLNigHTw6vYseGJx6XeTzfHQDYNj8vJiaEqXSSE02Y+guuvBfAQZsGJXzAPyPU6exHZIQRkypJOr1QR6lCCBA9GwQBy1iA5IQRkwmwyo+AK9wRz22YJ8QBqdd8iOEkFJuU1eysCAmJljCH4D2WOji59Gq8QmYjWOhDbtHfqcvI9tyr7N+a9KlcIxFoJCE4K32YTyO4+gT2KSU+uoi/dZv23YymVx/Eh85MgoHCPlV+zAepdSmk3hc15VS6oEgFQ2AHotCoSCE2GksOJ7KQ7nc+vFCExM7jUX7c8IIHYkEb1BFD6+0318sy7JtOxqNbv07+jjg9dPwHGfmla9kacwL7bGIxWLbftLV2bR9THYqleKEJI84jlMul5eXl/VJ2TuNheM4ruue9zkBZmUyRSnL+/a1Wq2dLg/ReUgppccilUqRTf2E84TgLcdxFhcXY7HY7ofxa/rtRt/9zlUVxhWLxc5vMNVj4TjO9PQ0n4ONy+fzOgN1OBb66jTmJIxrXxiy04e0TVzXbTQaSinGwk9IQvCQfvR2e2E1V2Ya1367TyQSnV8iKzaMBcHUFD0W+lHa1Re6rlupVCKRCHeomdL+nNbt+4weCz4k+AZJCF5ZXFzs4e2+TV+hwOqMEYuLi61WKx6P9/bljuMQTI3o+dGrEUwN0mORTCZ7+0nqO2Vd17377ruN9w0D1g4eVNHDpGKx2E8MEkJIKePxeKFQ0HUc6Nni4qIQoucYJITQCwf5fN5Yn4KqUCj0s91H77QLh8OMRZ+UUv3EIHFuLIQQjIWfkIRgjOM4S0tL/cQgzbKsaDSqH+TojY6k+lLSfkSjUR7AfdKzpP3P5UQiEaVUsVg00qtgyufzumign0Ysy4rH48vLy5VKxVTHMFwkIZihP2z1MwOxUTQatSyLB3BvHMcpFAqmxiISiTiOwwO4N/3PkrbpB3C1WtUH3qBbOpIaWeq1LCuRSDB17RskIZhRKBR0VbCpBm3bdhyHN/0e6BjU1RbpXegHcLlcNtJa0JTL5U5KkzpkWZaUUp98g245jmMkkmqWZYXDYcbCH0hCMKNSqZjdV2tZlmVZPIC7pWvgDT59hRA6VJFKu6VXT8yOhZSSTwg9yOfzZgdCnJsuNdsmhoIkBAMqlYpezzLbrJ4WMtum7+nJOePNRqNRPv52y+yEkGZZViwW4xNCt7Z+PNAnWPZDv+OxW8gHSEIwwIt3fMFURE+MT85pTEV0y4vJOY2piG7psLJp7d7IJ7dYLMYnBB8gCcEAx3FCoZAXLYfDYd70O6f3bxqfnBPndqgYb9bHPIpBQgjLslqtFnt1O6cv2vOiZY/e9zBgJCGY4dEbjRCi2Wx61LL/OI7jXV6xLItUOiJCoRBJqHPNZtOj3wvLsvTFZF40joEhCaFf7UvmvSCl5F2mK96NhXct+1KtVvP0J8bvRec8/VnxHuUDJCH0Synl3RQxn3274t0qgBAiFApxJU5XvPu9CIfD/F50zsjJlvAxkhD65elbTKvV4i2sK/1XxOyCsRgRrusyFp2TUrZaLe/aJ5WOO5IQ+uXpuwzv+F2xbdu7JKSUCofDHjXuP+Fw2NOnL7ri3e9Fq9XiiuJxRxKCAZ6+y3jUsi95/dmXVNo5KaV3m/1N3RoRELFYjE9r2AVJCP2SUnq3Z9B13f6vEYURbLboiqc/K0/XQH3Jo1RKDPIHkhAMsG270Wh40XKj0eCzb+eklPrG8j7b2fqgVUq5rstYdE5/PPDiE4L+peAB3LlEIuHRnJDjOPxS+ABJCAakUimP3vETiQTv+F1JpVL9V3htLUDTY9Fns4EipUylUl58Qmg0GgZvEg0C/QnBi7FQSqVSKePNYsBIQjDAozcas3dHB4QOjsaDaaPR4B2/W4lEwvhA6HkmUmm3UqmU8XNBG43GwYMH+ajmAyQhmGH8jUYfU8TMc7e8mIpwHIfJuR7oTwhmD2Gq1WrEoB7Ytm3801qj0WAXoz+QhGCGbdsHDx6sVqtGWnNdt1wup9NpI60FjW3boVDIVDBVStVqNSaEepNOpw3eEaZvU+H3ojfpdNpxHFObzcvlspSSVOoPJCEYk0qlXNc18gCuVqupVIoJod7oh2Wj0TCydbpcLmezWSaEeiOlvPXWW6vVav8PYCJpn6SU09PT+l76PunfrGw2239TGAUkIRgjpcxms41Go88wpD9szczMmOpYABl5ALv/v73zD3KjuvL9sbHNqsuBhe6QGKRlvIW7IXnGylZCIi1kZ1apQLFWUmQfZisQ0JSXH2EfS5Zhf8TLMh6ycZLyE1tsCjt+AaYpIBAISQopLuJCsVxAd+xs1tKyyUYdwEq1MAF0CSHQsgHL748z7gjNeEbdfa+k6T6fP3aNZubOzZzuc7733HvOdRySpMFRVRUDcEBboCQlWwRh/fr1wVPXjDHKWIcMUkIET2RZvuWWW5rNpj8xhO4eFRX3uUWNVCqFAdifLRhjpVIpnU6TJA2Oawt/51Rs2y6VSrlcjmRQcLLZbDKZLJVK/oSpZVm1Wo0kachYcvTo0UHPAQBA0zS63DE0MMZ0XW80GqlUqvcLQXGllc1mKfRyhDGWz+cVRfHkuC3LajabFHr5YlnW9u3bE4lE739V3G52HIdswZdisVgqlbzaolKp0DotTLjCg5QQIQTGmHnttaVEQpblRCIxzykTx3EajYZt27FYjNy9CBhjpmmWSqUebVGr1dDd09kg7riLhEQiEY/H51knuLZQVZVCrwg6bTG/23Ftsf6nP83+x3/0bYaEaEgJEYLRdbj3Xvad75imaRgGXimPkRW9v+M4rVbLcRxs2adpGlVhCAX1UKFQkCSpyxZ4/LPZbGKbOCqYF02nNgUAWZZdSeTaAgDS6TTZQjSWZRmGUa1WO20Ri8UYY9iWGhdp6XR6/f/6XzA+DlddBXRCKCyQEiIEs3o1TE/D6Cj+l2VZWPmCjh7vj8SLzWkvrJ8wxvDYENoCrxKTZVlRFCoJ7jNz2gL705At+gzagjGG64FOW6iq+vuMUbkM4+Nw4MAg50rwg5QQIZKpKSiXYffuQc+DIAiCK2NjMDIC09ODngfBAVd4LBv0TIjQUa/D5s20bCIIIoRMT8PYGNTrMDIy6KkQ3KAqeoI34+OweTO5CYIgQsjICORyMDU16HkQPCElRHClXIZyGSYnBz0PgiAIMVx11YyjI8ICKSGCK1NTtINOEESYGRmByUkYHx/0PAhuzKGEjhw5snfv3meffTbg0O12+3/+53+efPLJI0eOBByKWBzoOgCEvsQUS0uwdzOvmzUJf2DJD9liGOi0xaDnIp5cDkZGZjwesfjpPjG9Y8eO6enpdDr9yiuvvPbaa1u2bFm7dq3XQXfv3v2tb32r2WxmMpmzzz6b01SJoWd8PMT1YsVisVaroZfHjiMAgNW2QK1f+gjKULQFtuEhWwwKtIVhGIyx49kitG0yMC0U9oVfRHhXFf2tt9762GOPPfLII2vWrAGAO+6445577tF1/UMf+lCPw7366qv/+I//+NOf/vS2227z9AJQFf2iB3PFodsa62xIqKpqIpHo+ga8vchtB4xheBAzDT+uLTRNc5tDdoK2wOsp0BbUslwQjLFCoVCtVrFr+Zy2YIzhPWuqqmaz2RBq05A6vegwRz+hYrE4MTFx/fXX33jjjfhJu92+4IILVqxYsXPnTlT68/Piiy9efvnlhw4duvfee1FL+ZgQsSip12H1ajhwIGQlY5Zl5fN5TdPmvxgBQddvWVYmkwntOnhweLooCq9HcBwnmUySLbij6zpqoN5t0Ww2Q5gfqtdhbKyzhSyxuOhWQu12O5PJHDx4sFgsdoqY22677YEHHrj55puvvvrq+Ud89dVXL7nkkldeeeWBBx7oPYc0e0LEomRsDEZHQ1YyhqE3mUx6Wsu6fp/u7eKFvwt9AcBxHNM04/F4LpcjW3ABbcEYS6fTnn4QbRHCRYKuw733hvhUQLhxhcfMiek9e/YcPHhwxYoVXbmc8847DwAefPDBBUf827/921//+tdXX321DxlELG7KZajXQyaD8vm8YRiZTMZrBMVNNEVR8vl8JI6OCoYxls/nW61WJpPxJIMAQJKkVCq1ZMmSfD5P56mDY1nWpk2blixZ4lUGwTFb4Agi5jYwRkehXqeK+sXOjBLatWsXAJx11lldXz7ttNMA4IUXXnj++efnGeU73/nOT37yk1gstmDqiAghoaucx8Dpw9274IkiXD1znFgE0XVdluVkMunvxzuFKd+JRRBd14McvUJbSJKkh6nkiirqQ8GMEsIE0ezToOeccw7+Y/7V7Z133gkAn/nMZ1auXPnGG288+eST+/bta7fb/OdLDBvo1EK0TW5ZVqPRCCKDkEQioShKqJx+3ykWi4wxvAgzCPF4HADIFkHI5/N4TW/AcVRVrVarxWKRy6yGAqyop67Ti5kZJfTcc8/BsQLITpYtmymzbzabxxti7969Bw8eBIAzzjhj48aNY2Nj119//ec+97nzzz9/586dQmZNDA/j42HaF8O9GN8ZiC7i8ThjLFROv49YllUqlYJLUgCQJCmZTFarVdM0g48WQVCScinEw20ywzBCtXc8PQ26DvX6oOdB+GRG6Bw6dAgAli9ffrzvm2d37IknnsB//OpXv/rXf/3XVatWvf3221/5ylceeOCBv/u7v1u2bNknP/nJXqYy58qPjlEPNdhOI0QJoUKhoKoqr9O1GIANwwjbKdG+UCgUeElSOGaLQqFAPQ58UCgUuEhSRJKkRCJhGEZ4ehyMjMDoaPjOCYSMebLLvd5Ff8IJJxzvS41GAwDWrFlz22234SfLly+/9dZbn3nmmf/6r/+ampr6xCc+sXTpwtd6kOhZZJTLoOshu3Mey1s4DoiHfE3TpADsCcuyLMsKvi/WCea8LcsKTwDuC6ZpYtMgjmPKslypVDgOOHgmJ2FsDMrlMK0MQ8ZsjeF6mBmB4u6CdeGe9ZmnVfSrr74KAH/0R3/U9fn4+DgANJvNJ5980vOUieEHF0AhaiCEHt9rgdKCaJpWKBT4jhl6DMPgmBBC3FQE32FDT6FQmH2ENCD4loVq4xiPTtNpocXJjBJatWoVABw+fLjry27ly8knn3y8IVasWAEAf/AHf9D1+djYGP7j9ddf5zFVYpjATfFwdZoX4fGhIxXBfeQQY5qmiA5AsiyTITyBF7qJsIWmaWFTpZgNoor6RciMEjr33HMB4I033uj6Mp6kBoB5ekajSJp9zWosFuulMzWxKAnjjrggj4/LXyqn7x28UIx7cg6O2YLEUO8wxkQsDwAgFouF7aWgivpFy4wSwvzN/v37u77829/+FgASicSZZ555vCE+8IEPQIdmetfoS5cCwCmnnMJptsRwMDU1c0IwRLhXSIpAUZSwOX2RCJKkhA/EvRcodsOmSkdHqaJ+MTKjhC666KKTTjrpN7/5zYsvvtj5ZcxeXnrppfMMcfHFFwPAL3/5y1deeaXrS2+//fYpp5zCseiAGAo2bw5T5TxiWZa4FGYsFqOCgN4RqhoVRQlb9BVJs9kU+l4IGnmQUEX9ImRGCS1fvvy6664DgM4OQO12++mnn1YU5fLLL+/8mWq1unXr1pdeegn/c2RkBKXSd7/73c5ve+aZZ956662rr766l8IxYtEwNhayynkXcTkhcSOHkmazKTQnNE93NKIL3KkUNHgIc0LQUVFPLB5+r1E2btyYTqd1XcdaMADYtm1bs9m8/fbbV65c6X5bu92+8sor77rrrs7rYzZt2nT22Wfv2LHDbTt0+PDhL3/5yx//+Mc3btzYl/8hRF8ol6FcDt8JIQCQZdlxHEGDO45D2z29oyhKq9Ua9CwIAADRz20434vJyRlXSSwS3lU8f+edd05OTm7YsOH888+3bfvll19+6KGH1q5d2/UzJ5100qFDh0499VT3E0mSpqenb7nllssuu+xzn/vcH/7hH37/+99PpVI33XRTP/5HEH0jjAelXSj6Dg9CVSn1duodWZbFHdsK7YEw9+h0uNqthZh3KSFJkrZu3Tr/DyxdunTO/kCnnnrqtm3b3P+88sorucyPGCLw2qZwVc67CPXIjDHu3XFCjKqq4uqrxWmsUKIoirtLIIJwKiEAGB2Fe+8FXQ+rwwwZdIKH6JmpqfAdlHYRujsGIfb4ApBlWVx+rtVqUY/p3lFVVdyxqjDvGlOjxUUFKSGiN6amYHQ0lAelXVRVtW074CBzyinbtin69o4sy3h5LfeRbdsOc/QVAKpSEYsE27ZDvk2JFfXUXmgxQEqI6IF6PZSV811ks9nglSyzC23Q41P09UQ6nRbRd4AxlqPdCi/Isrxu3Tq8XJIvlmWFv8HK9DSUy1RRP/yQEiJ6YHwcNm8O0xVjc6KqqohURCQ8Pm9UVRWRiqDknA/S6XTwXGkXtm3HYrHw22JkBHI5SgsNP6SEiIXActCwJ4QQ7qkI27bj8Xj4PT5vMBXBt9mMZVmUnPMBrhC4vxfZbJbjgMPLVVdBvU4V9UMOKSFiIUJdOd+FqqocL+l0HKdSqUTF4/Mmm806jsMrG8EYazabtDXmj1wu12g0eKVLLcuSZTnkh4Rc6DKyxQApIWJeQl05PxtZlnO5nOM4XJx+pVKZmJighJA/ZFmemJiwLCv4HpnjOIZhkAzyjSzLGzZsqFQqwYdijNVqtWjZIpeDkZEZX0oMJaSEiHkZH4/IvpiL6/QDBmDDMGRZJhkUBFmWM5mMaZpBbOFm5sgWQUilUplMJqAYQkk6MTERuT3K6WmqqB9mSAkRx2d8PKxXjM2PqqoYgP1tk6G7x5QG97lFjfXr16Mt/G2TMcZKpVI6nV6/fj33uUWNVCqVTCZLpZI/YWrbdqlUyuVyUZSkeBkZ7ZENK0uOHj066DkAAGiaRpd1Dxf1OqxeDQcOhL5k7HgwxvL5vKIonhw3Y8wwjGw2S6GXI5Zlbd++PZFIeLKFZVl4NiiKoVcYxWKxVCp5tQUmWSNti3odxsZgejqCa8uhxRUepISI4zA2BqOjUdsa64IxZppmqVSSZTmRSMyT0nccp9FoYG1wpN29MBhjuq43Go1EIhGPx+e5IB1tUavVMC0XuY0Y8eAiodVqLaiHXFuoqkopUtB1uPde2L170PMgZiAlRMxLuUzXB7qgHioUCpIkybIsy7IkSbFYDL+E90Jg3M1ms1GpiBkQjLFCoVCtVlHfuLbA5kOtVqvZbDLG0BCkgYRiWZZhGGgLtIJrCyw4wGs60uk02WIGSgsNGaSEiHlZvZpe1y4YY5ZlYTE2Ywx9vSzLmqYBAPn6foK2QI/h2kJVVUVRAID2JfvJPLaIUKl879Aic5ggJUQcH0rhEgRBCIIOHgwNrvCg2jFiFtGrnCcIgugT09Og63QZ2VBBSoh4N1GtnCcIgugHWFFP7YWGiWWDngAxTJTLoOswHBumBEEQ4WRyEsbGoFymNeeQQDkhooMoXTFGEAQxGPAyMkoLDQ2khIhj4Na14PuAsNKE7x3jhD/IFkMCllyRLYYB1xa8rps9LpgNosvIhgPaHSOOISwhhH1HTNMEgM5OPFh2Tu1G+kmxWKzVahhx57QFlaD3B2xS1WUL7IqEPas0TSNb9IdOW2DHzi5boI/i/FvdO+ojdRntsEJV9AQAAExNQbnMt3K+syHhnH2BsRUe3k2dSqU0TaPuI4LotIWqqtgKr/Mb0Ba2bTuOo6pqOp2mNtmCcG2haRoG2s6v4pVejDG8Z43WCUJxG3ViB/kB2IIq6gcK9RMiOsArxnbv5nh8z7KsfD6vadr8FyMgjuNgRjqTydA6mDumaeq63rst8NoQsoUIPF3ahbZoNpuUqxNBPp/Hy1t6tIVlWY7jcO4jj12nd++O7PWOg4WUENHB2BiMjHDcGkN3n0wmPa2fXL9Pd0VxxLctTNOMxWJkC17gvWmMsXQ67ekH0RYkTDni2xaYwOYsTKemoF6nUpWBQEqIOEa5DGNjHCvncaWVSqUWTD/MCd0fzpF8Pu/D3SMkTDnCGNu0aZOmaf6eatcWW7Zs4T63qOGmq33bwjTNdevW5Xid76HLyAYH9ZgmjsH1oHSxWGSMZTIZfzIIAFRV1TQNV2y8ZhVNgsggAMATRYqi5PN5vhOLILquq6rqW9y7tgPT+9EAACAASURBVNCp1CgYjLF8Ph/kGJwkSalUyrKsYrHIZ07u0WlicJASijboWDktbizLKhQKyWQy4DiyLFMADkixWGw0Gr5lkIuqqpIkUQAOAkpSvKk3CPF4nGcAjiR4YC5gjlOSpGQyaRgGt64Ho6MwMkIV9QOElFC0mZriWLaAiy3f2aBO4vE4AFAA9gcvSYqoqlqtVrEJAuEVy7K4SFIQEYAjBmasuWy7S5LEM3VNjRYHDSmhCDM1BaOjvDandV3HSlQuo6HT70d/szCCMoivLQqFApfRooau67wkKQBgQwqyhT84Lg8AAFtRcLMFumLaIxsQpISiSr0OmzdzTAhZlpVIJHiNBgCYW6JUhA+42wJFFaUivIJPL9/z5rIsY8sJjmNGAdM0E4kEl4y1i6qqPA0xOQnlMpTL3AYkeoaUUFQZH4fNm3k1sRDh8QFA0zTDMPiOGXowOcd9WEpF+MAwDO62kCRJkiR6L7xSKBRE2AI4rhBGRiCXoz2ygUBKKJKUy1Cvc0wIifD4QKkIX1iWJaIBgSzLdDGWV7gn5xDOqYgIIGipBgCapvFcIVx1FdTrlBbqP6SEIgnvK8bci6u4oygKOX1PMMb4bgEgkiRRVyFP4HaMiJElScJrakQMHkoEvRQAEIvFeBqCKuoHBCmh6IEFWVy7eIlzNADQbDYFjRw+LMsSp1ckSSJVOiRwDsBhp9lsCnovJEnibIhcjirq+w8poegxPs73wj/3AmcR4PlQQYOHD6GSVNzIoaRWqwn9i9F70TtC/1a4ccxzxOlpOi3UZ0gJRYzxccjluCeExOUhaO3rCaFKKBaL0ZU4nojFYoJGVhSF3oveEeqj+DMyQhX1fYaUUJSo10HX+SaEAECWZcdx+I7p0mq1BI1M+GAxhZNBI1SpiHvjQoksy0I9CX9bU0V9fyElFCW4Vs67CPUyjuPQVay9o6qquGNVrVZLURRBg4cPTdOERl9SpUNCq9Xi76Oo63R/ISUUGXhXzndCOaHhQagqpejbO7Isi1OlvG6NiAiapolL0Yl6L/AMA6WF+gIpocgwPs63ct5FlmVx55odxwl+dWV0EKRUUOkussMWg0bo34p2x7wiSJUKNARV1PcRUkLRYGpq5hSeGFRVFaSEaO3rCVmW4/E4d1tIkuQ4Du1UegKXByLeC9u2VVUlVdo7qVRKUK600WikUikRIwMcu6Oe9sjEQ0ooGnC9Ymw22WzWtm3uw9q2vW7dOvL4nkin0yIqvCzLEujxw4gsy6lUStB7weVy++iAK4RFaYvpadB1qNcF/gqClFAkEFA534WgVIRt27Q15hXMz3G3BWMsm83yHTP0ZLNZ7oZwHIcxRqrUKyJWa7Ztx+NxsYlSzOVTWkgwpITCTrkMui7ohFAn6XS6UqlwHBDDOXl8r4hIRVByzh+4QuDbdq9SqdBL4QN8evkK0z4l5yYnQdfp6LRQSAmFHd5XjB0PVVU5On3HcQzDmJiY4DJa1Mhms47j8LIFY6xSqdB2jD9yuZxt27wCMI6Ty+W4jBYpZFnOZrOVSoXXGWe82aYfqnRkhLpOi4aUUKjBDea++E1ZlnO5XLPZ5JKNqFQq2WyWzuf6Q5bliYkJXgG4UqlMTEyQLfwhy/KGDRu4BGBcHpAM8k0qlcpkMngvfUAYY81ms39LNTzbQJeRCYOUUKjpV0IIwQBsWVZAp1+pVGRZXr9+Pa+JRRBeAdgwjHXr1pEMCgIG4IB7x47jkCQNTiqVCp66HoAkpUaLgplDCR05cmTv3r3PPvtskHGbzeaePXtef/31IIMQgRBcOT8nsizjqsufr0EXE4vFaF8sOO4KOIgtMNXHe2qRI5VKpdPpUqnkT5gyxkqlUjqdJhkUEHyejx496tsWtm2XSqVcLtdvW2BFPbUXEsOSo0ePdv73jh07pqen0+n0K6+88tprr23ZsmXt2rU+xr3ssssqlcoDDzzw4Q9/uJfv1zSNLnfkSb0Oq1fDgQPc79boBcZYPp9XFMWTs2CMGYaRzWYpG8QRy7IefvhhSZLIFgOnWCyWSqVEIuHJFpZlNZvNAYTe8MIYM02zVColk0lPdQCYZB2YLep1GBuD3bsH4tVDiSs83qWEbr311scee+yRRx5Zs2YNANxxxx333HOPrusf+tCHPI2+bdu2O+64AwBICQ2MsTEYHRXaQ2h+XF+TSCTi8fj8F6TjiZZBuphQwxjTdb3RaCxoC8dxGo2GbduxWIxsIQJcJLRarQX1ENqiVqupqkopUhFYlpXP53GRkEgk5vnOIbLF1BTU6/088xBu5lBCxWJxYmLi+uuvv/HGG/GTdrt9wQUXrFixYufOnbFYrMehf/azn11zzTXY2pyU0GAol2FsDN6d7RsIqIcKhYIsy4qixGIxSZLwWWq1Wniet1arybKcTqcp/SAUxlihUKhWq3g7CgAczxbZbJbqtMXBGLMsq1arzWkLxhh2Q67VamgIal4gDrSFYRiNRgNtgeuEWCzWarUcx2m1WhjL0un0UNgC00LT030+9hBWupVQu93OZDIHDx4sFouYEEJuu+22Bx544Oabb7766qt7GbfVal1yySW33nrr+Pg4kBIaFGNjcNVV/SkZ6wXX9bv/lo+hKMpQ+JfI4NoCpY9rC+xgSbboJ522QDptQQuDftJlC/xwSG2h6zA1BQcODHoeYaBbCe3evfu6665bsWLFM8880/l9jz/++I033njGGWf86Ec/6mXc22677YQTTvjnf/5nfIBICQ0AXYd774Xduwc9D4IgCII39TqMjw/VWnfx4gqPmdqxXbt2AcBZZ53V9X2nnXYaALzwwgvPP//8goPu2bNn3759N998M+fJEp6Ymhrg8SCCIAhCIFRRL4AZJYSyaPapsXPOOQf/sWAh7quvvnrLLbfk8/kTTzyR9ySJnpmagtFR2kImCIIILejkqaKeH8vw/z333HMAMPtY9LJlM9+Ap8bm4ZZbbsnlcnRf5iCp12HzZto/JgiCCDmTkzA2BuUyrXu5MCN0Dh06BADLly8/3vfNvzv2ne9853e/+93GjRuDTGVOFUWHhzwwPg6bN1OrCYIgiJDj7pGREuqZeTI1y3oc4oQTTjjel371q1/deeedDz30kOd5vRsSPfODFQ2MsbnbkJTLUK/TCaH+4BaYUMedgdNZdTXouUQdskVfGR2Fqak500ILBIuoMltjuNpoRgktW7bsnXfemf2T7XYb/3H22WfPOXS73f6Hf/iHv//7v3/f+97HZ7JEB9iPp1ar4Tkt7HXhOI5bbfv7suf+XjEWQbAfj2VZjDG3OWGnLYar1DbUFItFt+C586UAAOxNRf2Q+sNsBxWLxVAMAYCqquijBj3NkIJpofFxPBGxoC3otpZ5mFFCq1atsm378OHDXV92OyucfPLJc/789PT0c889ZxiGYRizv/rNb37z+9///kc+8pFPf/rT/OYcCYrFomEY2ItWUZSutB62/MKeYKqqpt96SwWgNKkI0L+4ttA0rXO9i1cXtVqtSqUyYwtyN8JwG3ViX2BFUWbbAgVroVAYllZ4IcW1RSKRwNsGO3uXz7YFrROEkMvBvfeyfL4gy9VqFYNFIpHosgW2Tt2+fXssFiNbzMmMEjr33HNt237jjTe6vownqQGgs91iJwcOHPjd7373yCOPzPnVcrmM/yAl1Dvu3Qjz9ICXJEmSJFmW4/E4Y2y7ZWX+8i/p6eYO9uPXNC2ZTM55SQV+iLbAlvzbt2/PZDLka7hjmqau65qmdQVdF9cWiUTCcRxcJ5DfF4F7gVo2m53zG7psgesE6l0ugvz69Y1GI7FkSSaTmfMbuoIFpi0mJiZokdDJTGfFQqFw8803n3LKKT/+8Y87v4xXcCQSiSeeeGLOn//lL3958ODB2Z9fc801AHDzzTerqnr66acfT0i5UGdFxA29nvIKGIObzSY93xxBd+/1jkb0+wBAtuCIruvVatWHLUzTJGHKEVynMcbS6bTXH6zVaiRMOeLbFninL9kCZveYfvvtt9Pp9Ouvv14ul1etWuV+36ZNmx599NGbbrrp2muv9foLgHpMe8Rf6HXB55sWXlzI5/ONRiOVSs1/d+yckDDlSz6f9+HuEdcWW7Zs4T6xqMEY27Rpk9d1mgsK03Xr1uWoOXJg/K2ZXWiRgHT3mF6+fPl1110HADt37nS/qd1uP/3004qiXH755Z0/XK1Wt27d+tJLL/VxwuHHsqxSqZTJZHzHTlVVk8lkoVBwT3cR/igWi4yx4+3CLIh7u3U+n+c+t6gRRAbBMVsoikK2CI6u60GOwUmSlEqlLMsqFot8JxY1GGP5fD64LUqlkmmafOe2SFnq/mvjxo3pdFrX9VdffRU/2bZtW7PZvP3221euXOl+W7vdvvLKK++6665Nmzb1e7LhBZ/sZDIZcBxJksjpBwQlqe/Q64KnF3Vd5zGpiIKSNLgt8IQEBeAgoCQNmOOUJCmZTJZKpQUvLSDmAQ/MBbdFKpWilTOytPM/7rzzzo997GMbNmzYvHnzxo0bf/jDHz700EMf/ehHu37mpJNOAoBTTz21f9MMO7qu+94U60JVVUmSKAD7g5ckReLxOK2AfWNZVqFQ4GILDMCGYVAA9kexWGw0GsElKRyzBR5wCT5aBEFJyqVAFY+008oZujorSpK0devW+X9g6dKlTz755ILj0qGf3jFNkzHG8aISVVUxq0Hl3F5xq4K5jOYG4IhvxvsDZZC/DcrZSJKkaVqhUJiYmOAyYKQwDIPX8gAAZFmWJKlQKNCBIa8wxizLOl7Jng8SiYRt26ZpRvx06dKFv4UQjGEYfO9rQ6U/Z4cnYn5M0+QrHzGQ02a8VyzLsizreF0k/BGLxRqNBqWFvGKaJpZhcxxTVVUyhA9wqcZ3TFwh8B1z0UFKaMCgx+deYUSOxgemaXY1JeOCpmmkSr1iGAZ3j08rBH8YhsHdQdEKwR+WZXHP9OPN6xGPF6SEBgzftLMLORofiFhvAaUifME9OYfgyS3uw4YY3I4R8V5QKsIrmJzjvlTDFULEbUFKaMCISAghiqLQmURPBC+NmRP3AiDuI4cVy7JEeHw4tkIgMdQ7gmQQANBL4ZVarYb5G+5IkhRxW5ASGjDinj9Zlunceu8IdQSkSj0hSJISPui8b5gvKHZJlXpCkC0ECaxFBCmhwSPo4QbB0T1kiEvOAUAsFiNVOiQoikLRt3eazaa4MEkB2BPifBTmhKIcL0gJDRLR0VfQyGFFnCQVN3IoqdVqQnNCzWZT3OAhA3cqBQ1OOSFPCFUqsiyTEiIGRqvVEjQybf16xXGcRTdyKFEURdx7QXhC9DYlbYP2jtC/VavVirItSAkNEqFPHq8+pBGBPP5QIVSV8m3fFW6EpgroQJgnZFkWt0KI+GqNlNAgkWXZcRxBj6DjOORlekeol2GMKYoiaPDwoaoq5eeGBNHPLfkoTwh9eqNsC1JCA0ZVVUEBuNVqUfTtHVSlQscXN3jIEKpKW60W5Up7R1VVcceqaLXmCU3TBL0Xtm1H/KUgJTRgNE2zbVvEyPRwe0VVVbLFkCCo2QxjjKKvJ8SpUtu2I37XlVfEOSjapiQlNGBSqZQIj2/bdiwWo+jriWw2K6KSBT1+xB2NJ2RZTqfTIpy+bdscb6+MArIsx+NxQbagA1ueUFU1Ho8LihcRfy9ICQ0YQY6GMRbxJ9sHaAvujsayrHQ6zXfM0CNuhUB5CK+IWCFg9xqyhVfS6TT3zmS0VANSQsMAd0fjOA5tx/hARCqCknP+QFVaqVQ4jmlZFnl8H4hIRdRqNVqq+QDPlfI90WjbNi3VSAkNHlVV161bx9HpVyqVbDZLHt8HqqrGYjFewtRxnEqlksvluIwWNXK5HMe+t4wxir6+yWazlUqFVwDGxcb69eu5jBYpZFnesGEDx6u1DcOQZZmWaqSEhoJsNus4DpcAjE82eRl/yLKcy+Vs2+YSgFGSkpfxBzp9LgHYcRzDMCYmJmh54A9VVTOZDJcAzBij5UEQUqkUr5UzermJiYngQy12SAkNBbIsT0xMBA/AqKXoyQ4CrwBMkjQ4qVSKSwAmSRqc9evXB9+vxCzpxMQE2SIIXFbOuDwgSYqQEhoW3ADs+/muVCrNZpOe7OC4AdifLdDFoLrlPreogbYolUr+hKnjOKVSSVVVkqTByeVy8Xjcty0YY6VSacOGDSSDAoK+pdls+g4Wtm2XSiWSpC5Ljh49Oug5AABomkaXdQMAYyyfzyuK4ukBxZUWhV6++LMFY8wwjGw2S6GXI8VisVQqJRIJT7awbRs3YqhGiReMMdM0UVwmEonef9CyLFynUejlhWuLVCrl6ZZcwzAAgGwBHcKDlNDQwRgzv/KV0pEjiUQiHo/P/4g7jtNoNPAoKIVe7jDGdF1vNBq9xGDGmG3bjuOQixEBCtNWq9WLLSzLwqo9soUILMvK5/OyLCcSifn1EDoo27bj8Tit00SAiwS0xfzH4Nxgoa5YMfH1r/dthsMMKaEhplyGsTH2H/9hvvhioVBwn29JktwbIRhjrVYLfX06nSYNJBTGWKFQqFarsizLsozaFG2BFa2tVqtWq2ERPtlCHIwxy7IMw2g0Gq4tYrGYJEloBTxmh7bIZrOUChJH77ZAQ9BZdXFgcsgwDLxP3rUFfhVtgRdcptPp1KpV8v/+3zA9DaOjA531UEBKaIgZG4OrroJcDo65m1qtxo6BDkWWZU3TZFkmX983jmcLVVUVRSFf30/IFsMDrhPgmFEAQD6Goii0MOgnpmliGO2yBfby/r0tdB3uvRd27x7cTIcFUkLDiq7D1BQcODDoeRAEQRBhpF6H8XGYnKS0kCs8qHZsyJiagunpQU+CIAiCCCkjIzA5CePjg57HEEFKaJiYmoKREdLpBEEQhEBGR2FkBKamBj2PYYGU0NBQr8PmzTA5Oeh5EARBEGFnehp0Her1Qc9jKCAlNDSMj0MuRwkhgiAIQjgjI5DLUVoIWTboCRAAAFAuQ7kMw3F6XSjudSJU3TNw0BZkiGGAbDE8YDViJNpQXXUVjI1BuUwrcFJCw0GoD0pju4tarYaFndhxpLMdAJXa9o15bKGqqqZp1JehbxSLRaxbsSwLm1R12iKdTkciGA8B7kuBGqjTFkhobeEenY58tTJV0Q8B4e3uUCwWsd8X9ofExmv4JbdFJHb9ItcvlM7ea2iLzvSD2yLStm0ASKfT1JJHHGiLQqEgSVIvtqB1gjhcW2D3ozltgb3jY7FYOHt1YkX9sQ52UYP6CQ0Tq1eHr+One09FL/cTuS35M5kM+X3u4N0IeDHCgvoGbdFsNikGi8A0TV3XNU1b8CIdAMD7xh3HCWcMHjS9X2bnOA7qIQCYmJgI2yKhXI5sWoiU0NAwNQX1esi2xjD0aprm9R5Z9Psh9DWDA919Mpn09Cd1HMc0TRKmfNF1vVqterUFxuBkMkm24AWu0xhj6XTa0w/iPbIhXCSMjcHISMjCUC+QEhoO6nVYvRoOHICRkUFPhRv+Qi/iJiTo4kwu5PP5RqORyWR8/KxrCxKmwfEdehEUpnSJKRf8rdNcwrlIqNdhbCx8WxMLQj2mh4Pxcdi8OUwyyLKsUqmUyWT8xU5JknA3Tdd13lOLHBh6/ckgOGYLRVHIFsHRdb3VavmTQQAgSVIqlUI5xXVeUUTX9SBHEtEWpVKpWCzyndggiXxFPSmhwYGV8yFqpYiLrWQyGXCcRCJBATgglmVVq1XfodclHo8zxkLl9PtOsVhkjAV8LyRJSiaT1WrVNE1eE4sg+XxekqSAOU4UQ4ZhYAFmSLjqKqjXoVwe9DwGAymhwRG6ynlcbHHZSYnH45ZlUQD2B2OMiySFYwG4VCqFyun3EcySBpekcCwAFwoFtykX4QkukhTBur9QrdaifRkZKaEBga9QiAoXcanK60AJBmDDMLiMFjUKhYKmaXxtESqn30d0XecSepEQBuA+UigUONoCS2JDtVrL5WBkBCL5dJESGhDj42HaF4Nj0ZfjgFhjTHsBPsDTtRwHRFFFaSGvmKbZarX4njeXZRm7cHEcMwqYpplIJBbsXOAJTdPCtlqbno7maSFSQoMgdFeM8U0IuWiaVigU+I4ZekR4fABIJBJkC6/UajXuJZCSJEmSRCsErxQKhQUbm3klhCuEkREYHY3gHhkpob5Tr4OuhywhZBgGdy8DoXQ04hHh8YFSEb4wTVNEA4JEIhG2VIRgRCTnkBCuECYnZ6p5ogQpob4Tusp5AGCMCWo5oygKKSFPCLIFJplICfUOyiDuyTkgVeodcQ4KbSFi5IGBR6cjtkdGSqi/lMtQr4csIQQA7rWFImg2m4JGDh+WZYnrgihJEqlST4h7KUIYgEXSbDYFvReSJIXQEKOjUauoJyXUX8bHQ1Y5Dx03aYuAPL4nhEpScSOHklqtJvQvRu/FkBDCFQLevBGl00KkhPqIrs+cRwsX4jLPABCLxcjj947Qv5Usy1G8EicAsVhM0MiKotB70TtCc6XirDxIRkdhZCQ6e2SkhPpI6CrnEVmWHccRNHir1RI0clgRl4dwHIcuIOsdoUpF3BsXSmRZFupJwqlKp6dB16FeH/Q8+sEcSujIkSN79+599tlnvY7Vbrf379//1FNPvf766zzmFi5CVznvItTLOI5DV7H2jqqq4o5VtVotRVEEDR4+NE0TGn1JlQ4JrVYrnD4KdzCikRZa1vXfO3bsmJ6eTqfTr7zyymuvvbZly5a1a9f2MtCOHTt27Njx5ptv4n+ed955X/rSl0bCVSHln3IZdB2OHh30PERBK9ThQagqpejbO7IsN5tNQTGSMRbO6CsGTdPEbZCF+b2YnISxMSiXQ7mG7+RdOaFbb711+/bt99133+23337fffd94hOfuOKKK/bv37/gKLfddtvtt99+8sknj46O4qpx3759l156KZ0qmCF0V4x1IsuyuA0yxhjf1tXhRqhHFnogLHwI/VvR2sMrgv5iITdEZCrqf6+EisXit7/97fHx8TVr1uAnN9xww8qVK2+66ab5V5n79+9//PHH77rrrt27d+/YsePpp5+enJwEgNdff/2f/umfhM5+cYBbrSG6Ymw2siw3Gg0RI1P09YQsy3h7vIjBaafSE7hrLMIWtm3j8oP7yGEllUoJeikYY6lUSsTIwwJmg8J+GdmMEmq32/l8HgAuvvji339t6dILL7zw4MGD999//zxDPProo9u3b7/gggvcTz772c9ef/31APDzn//8+eefFzLxRUSoE0JINpu1bZv7sLZtx+Nxir6eSKfTInKxlmWF3OPzRpbldevWCVJC2WyW+7AhRtwKwbKsdDrNfdghIhppoRkltGfPnoMHD65YscJNCCHnnXceADz44IPzDLF27dp169Z1fXj55ZfjP0QEyMXE1FQoK+e7UFVVhKOxbTvkXkYAqqqKSEWQLXwgaIUQ/jyEAESsEKKyVMOK+lC3F5pRQrt27QKAs846q+vLp512GgC88MIL86R2LrvsstkfKoqybNkyADjjjDN4zXXxUa/D5s2hTwgh3B0N3idAHt8rmIrgG4Cj4vF5g6kIvrag5Jw/VFXlfkVJhJYH09MzFySElBklhDFs9sWN55xzDv7Daw/NI0eOvPPOO+9///tnq6sIgZXz0SigU1VVlmWOvVZrtdrExASv0SJFNpt1HIeX03ccp1Kp0HaMP3K5nGVZvM7VMsaazSbZwgeyLOdyuUqlwmtALEaLiiodGYFcLsR7ZDNK6LnnnoO5emViXge83/20d+9eALjyyiuDTnDxgtf5RiMhBMccjW3bXAKwYRiyLFMSwh+u0+cSgFEGkS38Icvyhg0bTNMMPpTjOIZh5HI5Oivtj1QqlclkuIghxlitVsuFug6mm6uuCvEd9TNC59ChQwCwfPny432f14PP3/3ud88444wrrrii9x+Zs1h6EdfhR+CgdBfo9B9++OFUKhWk0zEutighFARVVTOZTKlUymQyQcZBSbp+/XpeE4sgqVSqVqtVKpVkMul7EDczR5I0CGgLy7KC/BlRkk5MTERLkuLR6fFxOHBg0FPxyTwNWbo7Kx6PE044offf9+yzzxYKhfvuu+/EE0/s/acWseiZDdYcRmrFAADHqlVLpVIikfDnawzDAIBoLbbEgPKlVCr5E6YYekmSciGbzRYKhSC2ME1z3bp1JEkDgulSLJT256AYY5iZi6IkHR2Fe+8FXV+koW22xnC10YwSWrZs2TvvvDP7J9vtNv7j7LPP7vGXtdvtL37xi1/4whew7iyiRC8h5LJ+/fpUKuXD11Do5Y4rhrwKU3T32WyWQi8XZFnOZrOKopRKJVVVZ5/InAfbtiuVSi6Xi8qRFMGghzFN04cwtSyr2WxOTExEUQZBR1pocSqheZhRQqtWrbJt+/Dhw11fds98nHzyyT2O+LWvfW3NmjWf//zneU1x8TE1BaOjoa+cnwf0NbgI7iUGO47TaDRqtRqFXu64wtS27V5isOM4eMI3uu5eDLjJqKqqruvYL3RBW1iWZdt2LBYjW/DF3fAtlUpoiPn3udBBYQXlli1b+jXNoQRD2/h4yJb6S44ePQoAN9100w9+8IM///M/3759e+eXn3zyyb/+678GgF27dp155pkLDvfoo4/u2rVrx44dXuehaVpIdsfqdVi9Gg4ciEjJ2PwwxgqFQrVaxZa4kiTFYjFJkvAkL7a9QV+fTqdJA4mDMWZZlmEYjUZjTlswxlqtFtmiD3TaIpFIoBUwEjuO02q18P/WajXMJFEqSByMMdM0DcNotVrz2yKVSqXTadKjAAD1OoyNwfR0CFb7rvCYUUKFQuHmm28+5ZRTfvzjH3d+X7FYnJiYSCQSTzzxxIKD7tmz55vf/Obdd9/t6XhQ14QWPWNjMDoKk5ODnscQge6m2WxiDMAPMR5rmhahStQhYH5bpFKpaB0CHSi4TsC8O9oC//iqqiqKY6m4pgAAIABJREFUQrboG/gu1Go1dgx4ty1oYdDN1BSUy7B796DnEZRuJfT222+n0+nXX3+9XC6vWrXK/b5NmzY9+uijN91007XXXjv/iE899dTXv/71u+++e+XKlZ2fN5vNI0eOvO997+txQoubcnlRH63vD3SVGEEQxCImLGkhV3jM9BNavnz5ddddBwA7d+50v6ndbj/99NOKorhXZyDVanXr1q0vvfSS+8mTTz55xx137Nixo0sGVavVa6655j3veY+4/yXDRYQPSvcOySCCIIhFjHt0Oiz8vop+48aNTz31lK7rl1xyyamnngoA27Ztazab99xzT6e+abfbV1555aFDh37xi1/cfffdAPCjH/3ohhtuAIDOS1gB4K233gKAbDYbpLXMYgIr5xe5RiYIgiCIBcjlFnVFfRfv6id05513Tk5Obtiw4fzzz7dt++WXX37ooYfWrl3b9TMnnXTSoUOHUC3t27dv/jKxT3/609wnPaSMj4dg35QgCIIgFmZ6GsbGwqGEZs4JDZxFf04I84S0NUYQBEFEhEUe+LrPCRGBKJdB1/tQL8b3ImUiCGSL4YFsMSRwv+yd8E2fbDE5GY7LyHq9bYOYj6kp2LxZRAMhrHl2yzux+wuVdw6E49miswR90HOMCq4tsPi8yxbU96WfuC8F2WKwHK9BBv5fUT0y8Og0NhNezNDuWGDEVM67PQm7+n1BR8svxpjjOKqqkrsRSrFYdHuvuZ4Fv+TawrZtAEin09QGRhxdffBkWcZXAwA6e3U2m00AoP6QQkFbFAoFSZLmtEVnr07qDymU2bbodFBwzBbNZlNIsKjXYXwcJicXoxjq7ic0cBaxElq9mm9bBcaYruvYf5buqRgsjLF8Po9xt0dbNJtNisEisCwrn8/j3QgL3lOBvcsdxyFbiMA0TV3XNU2Lx+Pzlwbjgg3XCZG7vL0vFItFvNRokLZYtI30SAlxgnerTXT3mqZ5vbvUvSuKfA0v0N0nk0lP92XiteGZTIYCMEfQ3SeTSU+PN9lCBPl8vtFoeLUF3l1KwpQjuGZmjKXT6d5/yl2wcU7ULc7LFUgJcWLJEti9m1dCyDTNhx9+2KuLQdznO5fL0U5ZcHRdr1arAW1BwpQL+Xzeq7t3cRynUqkAQNQvzuSBv9DrQsKUI/7WzC6MsVqtxlOYYtfp3bsX14WbVDvGg/FxyOV4ySDLsjAD4S92SpKEN42jq+IypchSLBYty8pkMkFsoShKPp/nPreoEUQGAYAkSclkUpIkHRufEgHQdb3VagWxRSqVMgyjWCzynVjUwF37ICd+ZFlOJpOVSsU9Wx2UkREYHYWpKT6j9R1SQn7BynlOfRTcJztgCiGRSCiKQk4/CJZlFQoFTdMCjoNOimwRBMuyGo2G79CLoDCtVqsUgIOAS6xkMhlkEBSmhmFwC8CRBA9pBQwWeLya58p5chJ0fZFW1JMS8gvXK8a4PNlIPB5njJHT942u68ElKZJMJqvVqmmawYeKIJj/Dxh6ETcbQQHYH5ZlVavVgJIU4R+AI0axWGSMcTkCIcsyz9T1yAhMTy/StBApIV/oOtTrvLqMm6bJ68kGWnUFQ9f1zoYFAUFbFAoFLqNFjUKh4HuzeDYYgMkW/sCNe16jJRIJSZLIFv4oFApcJCkSj8cBgFuwwLMiizARTkrIF1wTQoZheKpOWhBJkiRJIiXkA8uy+J43x0BOtvAKdofj+17IsmxZFtnCK5jU5Hv2X1VVMoQPisUi92ChaRo3Veo2WlxskBLyztTUzOkwHqBr5vtwA4CqqoZh8B0z9JimiSKS77CUivBBoVDg/lJgWojeC69wX6oBAL5ltHHsFRG2iMVijUaDZ1poZGTmPrLFAykhj9TrsHkz34RQ8MO5s0FHQ6suTxiGIaLonVIRPjBNU0QzCEpF+EDEUg0ANE0jVeoJEck5ELFCmJ6GchnqdW4DioeUkEfGx/leMWZZFu7UckdRFHL6nhDk8fHgEZ0P7R3LskQk5wBAkqRWq0XvRe+YpinipQCAWCxGhvBErVYT1J8MV2vchhsZgVxuce2RkRLyAl66y7WNZpAAiXfKHA9Zlhdls8oBIVSpKIpCSqh3GGPiOlLGYjFBI4cScc8til16LzwhYnkAIl6Kq65aXHfUkxLyAteD0i6+H+4Ff5C8TO9YliU0+pIqHRKomMATzWZT6HtBPqp3xPkolKQ8bYFHpxfPaSFSQj2DlYGcKucR3AXgOGAntPb1ijhbiBs5lIjbBQCyhUeEKhXKCQ0P/Hfw8ej0IqmoJyXUM1NTi+t6OfIyXpl/t3E4Rw4liqK0Wi1Bg8disWazKWjwUELacUgQ6s9brRbn5ceiqqgnJdQb4+MwOsqrct5F6PWcHLs1RgHRV6XSVayeEKcdGWMiqjXDitDD/kIPhIUPVVW9rhB6f4+EvHEYNBfDHhkpoR6o10HXRSSEZFl2HEeQ03cch7xM78iyLC4PwRhTFEXQ4OFDVVXKog0Jop9b8lGe8PpeeMrnCbHF5OSiODpNSqgHeFfOd+JD5veIuLgeSlCVCh1f3OAhQ7QqpVxp76iqKm4zkVZrntA0TdB7IfClWCR7ZKSEFgI7RAk7ISTLsm3bIkZuNpu0C+AJVVUFbQTYtk3R1xOO44iwBaZgKfr2jjhVatt2KpUSMXJYUVVVULCwbVvgSzE6CvX6kKeFSAkthJjKeZdsNivC42NJJDkaT6TTaRG17ujxKfr2jizL2WxWhNO3LIteCk/IshyPx0XYwrZtWqp5Am0hKF5ks1nuw86wGCrqSQnNC1YA8j4o3Ymgh9u2bYFPdkjBnJAIW3C8OzoipFIpER6f3gsfZLNZ7h2YaKnmA1mWNU3jrkpt247H42KXarnckFfUkxKal/HxPlTOZ7NZvqkIx3Eo8+wDEakI9Pi0NeYVXCHwDcCUnPOHiNUaOSh/4AqB74nGPi3VpqdhampoLyMjJXR8xschlxOaEEJkWeZ77UulUslms+TxfZBKpVBH8hqwUqlMTEzwGi1S5HI527Z5BWDGWKVSoeScD2RZTqfTlUqFVwDGWJ7j2qU2IsiynMlk8CpWLmDf6n6o0pERGB0d2qPTpISOQ7ksqHJ+NrIs53K5ZrPJxenjk71+/frgQ0UQWZYnJiYsy+Li9A3DyGQylBDyhyzLGzZsqFQqXEar1WoTExNkC3+kUqlMJsPFFo7jGIZBMsg3qVQqHo9zsQVjrNls9s8WQ1xRT0roOExNiaucnw2KoeCrLnyyKQkRBAzAwVddlUqFJGlAeAVgwzBkWSYZFATcWAyYunYcBzPWZAvfYLBwHCe4LVCS9m/3YIgr6kkJzYWuC62cnxNVVTHt6fv5tiyrVqvRYis4GIBLpZJvYWoYhuM4JEmDk0qlksmkb1ugu8dUH/e5RQo3de3bQTmOY5qmqqq0PAgIPs9BbMEYK5VKuVyu35IUT5sMX1poydGjRwc9BwAATdOG6LLu1atheroPJ4RmwxjL5/OKonh6QHGlRe6eL8VisVQqJRIJH7ZQVZUkKS8YY6Zp+rAFY8wwjGw2S6GXF64tUqmUp/7Ftm3jmTnKBvGCMabreqPR8GoLy7JwU2wwtiiXYXwcDhwYwK+ehSs8SAnNYmoKymXYvXtQv58xVigUqtVqL37fcZxGo1Gr1cjdiwCFaavVUlU1kUjM/81kC6FYlvXwww8DQCKRWDCfj3sHeCyXQi93cJEgy3IvtrAsy7btWCxGtuBO5yIhHo/Pr4fQQWHN/IDXzGNjMDo6DDeakxI6PkuWwO7dA0kIdWJZlmEY1WoVK8skSXI9juM4rVYLm/A6jpNOp6k2WByMMbRFo9FIJBKxWGy2LRhj6OvT6TRpIHF02QKtQLYYCBiDDcPAdYIkSfhqwLG7sRhjrVarVqth6RnZQhxoi0KhgNp0HluoqorxYsAzrtdhbAx27+7bSdzjQUroOGAfTJFNpT2Bjzj+ZSzLkiTJvStAVVVFUci/9I3j2QLRNG3w/iUyYN6UHQM/dA2hqirlHvoDatNarXY8W9AirW902qIrWKAthitYDEeoJSU0F+UyjI3BcPxB5oQxRm5lSCBbDAkYfckWwwC9FMPDsNuiXofVqwe+/eIKD6od60DwFWPBGeonO2KQLYYETD8MehYEAL0Uw8Sw22JkBKanh+cyMlJCx8ArUajehyAIgiBEMzo6PJeRkRI6xtTUMBxlJwiCIIjwM0yNFkkJAQDA1NTMrSgEQRAEQfQBTAsNwR5ZVJTQfFd61euwefOQnxAKE3wvtSaCQLYYHsgWRBSZnoZyeeB31C8b7K8XB9Y847WmbkvyuWuex8f7ecVYBPFgC0IwXb0A4NjJSrRFOp2m+vO+4drCrT93G2QAANmin5imyRib0xaKolAvAIGMjEAuB+PjbjfjYrF4vGAhrnFMCKvoO9tMKYoiyzK2mcIeU9iT0LZtAFBVNX366eqFFw5z5fyiBvu+mKapaVpnT8LZtqD+kKIpFovYB6+rP2Rn7zXqSdgHOnsSzrYFvhRuT8JsNkvrBHG4wUKSpK5enV0NbLEnIWlTIdTrMDbG/u//NU88sdMWnf0hBdkitP2E8vk89p/1cE/F2rXr/8//Cf6riU7cO3F6tAXejUAxWATunSG92AJ7NJMtBGFZVj6flyRpwftb0O/jOmFiYoIWCdwpFouFQkHTtB7vqWg2m0C2EEPx5ptLR454ujMkk8kEd1AhVEIYehlj6XS6959yH3F6vjmC7h67/fb+U3hVNZfnm3AxTVPX9WQyueC9aZ2QLUSA13Ulk0lPrgbvyyRhyhdcM3u6u9QNFpSo48hgA3fYlJC/0Nv544O8mzdcmKb58MMPe3X3CAlTvui6Xq1WfdsCE3VbtmwRMbeokc/nvbp7FxKmHMEUqSRJyWTS34/btp1MJskWweESuIMsEkLVYxqf7CAbh6qqapqGypTv3KIGXhju+8QP7hooipLP57nPLWoUi0XLsjKZTEBb6MPR+mxRE0QGAYAkSalUyjCMYrHId2IRRNd13zIIAGRZVlW1VCqZpsl3YlGDS+BOJpOGYbhnq30TBiWk63o6nQ6YQsDj1RSAg4BPdjKZ7D3hPCd4tTUF4CBYllUoFHy7e0SSpHg8blkWBeAgFIvFIDIIweBdqVSCO/0og5I0+HuRSqXwDmBeE4sguq5rmhYwcOPx6uDBgrMSOnLkyN69e5999lm+w84DPtlcdlJQmVIA9g2XJxtRVbVarVIA9oe72AooSeFYAOay6oomKEk1TQs+lOv0KQD7w7KsRqMRUJIikiTRyjkI+BhzOY6SSCSCp655KqEdO3b86Z/+6be//e2pqalsNvvMM89wHHxOTNO0LIvLk40kk0kck9eA0cE0zUajweuglbsdwGW0qIGhl9dBK16rrmiCmTletpBlWZKkQqHAZbSogRlrXqOhr6M9Mh9g8wKOgRtT10ECNzcldOutt27fvv2+++67/fbb77vvvk984hNXXHHF/v37eY0/J7VajeOTDQCSJGmaRgHYB8H3YrrAfAY5Gh+YphmPxzkOiIGcVgheQe/sqWpvQVRVJUP4wDRNt2MQLzRNI1Xqg0KhwPelwNVaEFvwUULFYvHb3/72+Pj4mjVr8JMbbrhh5cqVN910U6vV4vIr5sQ0Te4VRqgu+Y4ZekzTbLVa3G1BjsYH6PF72RfDnoq9gHsBtELwimEYXPbFOqEVgj8Mw+AbfYFWCH4xTZN7mbYsy25/cB9wUELtdhu3Sy+++OLfj7t06YUXXnjw4MH7778/+K+Yk2Kx2KPH9wQ5Gh/UajURDQhisRiQo/FI7+stT+8OrRB8wD05h9AKwSuYnBPRmCNgKiKC9L5U84QkSZIk+Q7cHJTQnj17Dh48uGLFCjchhJx33nkA8OCDDwb/FXPSbDYFtZxJJBK0/PWEIC+DDzcFYE/wKiDoAj0X2aJ3MGPN3eND4OVvBGGMcU8IIWgLESOHFbxGRsTIiUTCd1dCDkpo165dAHDWWWd1fX7aaacBwAsvvPD8888H/y2zERR9weNamQAAxpigPxrZwhOWZYn7i0mSRE7fE+JsQQHYE+KiL70UXhH354rFYoPcHUMVNltxn3POOfgPQetIcdE3yB80gogzBADIsszrat4oICghhJDT94TQ94IYHihv7QmhPso3y4IP8dxzz8GxIx3vGnrZzOB4cd3igjEGU1ODnsXiwFqxQuiTTbboHfa738Ef/qGgwWOxWO2HP1z/058KGj9kNAFip50maHBFUaz/9//U97xH0Pghwzp8mG9layexWAx0HU48UdD4IUPoBkKr1fKntDgooUOHDgHA8uXLj/cNPe6OzVlkcbx8gND11szI9bqg8UOGvGpVrzVI/qjXycv0ipiDKb8f/o034I03xI0fJtjKlZIwJeQ4DtTrQEqoRw4fbmmauADMbBveekvE4CGDrVw5wN8+TyEnByW0ICeccEIv3+ZpE0SW5d5rgL0yM/L0tKDxQ4bMWOtf/1XQ4I7jqJ/8JORygsYPGaplGbou6CLhVqsV+7M/I1v0iFYsCt00kScmgG5E7w1ZZDNoxpi6ZQsM347PECIDyJs2OY4jSJU6jjNPQmi2xnC1EYdzQu4uWBftdhv/cfbZZwf/LbMRd2aQVxdwgug/4jp4OY7DvTtOuBG3WhvOwxZDi9AD5vNHX6ILWZYF+agghuCghFatWgUAhw8f7vrcffJOPvnk4L9lNkIfPnqye0eW5VgsJsjpM8YURRExcigR+tyKi+uhRFVVoX8x8lG9I86H0EvhFXH7OUFSGByU0LnnngsAb8w6PYAnqQGgq88QLzRNs21bxMi2bdPa1xOUnxsSZFmOx+Nki2FA3NqXMUZ5CE+oqiooWDDGUrRH6QVxgTuI3+OghMbGxgBg9hVjv/3tbwEgkUiceeaZwX/LbFKplDiPTw+3J7LZrIgjEbZtx+Nxir6eSKfTIvoO2LadSqUo+vYOqlJB70WOTmt5QVVVQSsEvleARwFVVbHCi/vItm1ns1l/P8tBCV100UUnnXTSb37zmxdffLHzc2zTfOmllwb/FXOCjoa7urQsi2SQVwQ5Gtu2yct4BR0N92HJ4/sgm82KWP7atk3LA6+IWCFgco5s4QlZltetW8f9vQi4VOOghJYvX37dddcBwM6dO90P2+32008/rSjK5ZdfHvxXHA8RqQiKvv7g7mgcx6HknA9whVCpVDiOSck5f4hYIeBSjZJzXhGRiqjVapSc80E2m+W+bA64VONzF/3GjRvT6bSu66+++ip+sm3btmazefvtt68U2T8AHQ1Hp28YBnl8f+AfjaMwrVQqvlOdESeXy3G8l8pxHLKFb7LZbKVS4XVElDFWq9VoqeYDWZYzmQzHYIHvFy3VfMDdFpZlBQzcfJQQANx5550f+9jHNmzYsHnz5o0bN/7whz986KGHPvrRj/Ia/3jkcjnHcbik2jCKT0xMBB8qgsiyPDExYds2lwBsGIYsy+vXrw8+VASRZXnDhg1zBmAfIRllEC0P/KGqaiaT8X1FdheVSmViYoJs4Y/169evW7eOSwBmjBmGQQkh36RSqVgsxmXlzBhrNpsBAzc3JSRJ0tatW5944onNmzfffffdhUJh7dq1vAafBwzAlmUFXHXhYoue7CDME4A9gVqKJGkQUqnUnKsurw3NKpUKSdKApFIpLqlrwzAymQzJoCBks1nHcQIGYMdxarUaSdIgyLKcy+WCr5wdx+EiSbkpoQGCAdg0Td/Pt23bhmHQkx0cDMCmafoWQ5ZlkSTlQiqVSqfTpVLJty0Mw3AchyRpQNDpo8v2NwL+LEnS4ODKudls+g4WjuOUSqV0Ok3BIiDuytm3LRhjpVIpl8sFt8WSo0ePBhyCC5qmBTxvyxjL5/OKonj9o2AOg8tfk0CKxWKpVEokEp7+pHgeBV2VuLlFDd+2ME1z3bp1JEl5wRgzTbNUKqVSKU+ZOdyIyWazJIN4wRgrFArVatWrLWzbpg1KvvgO3JZlNZvNgIHbFR7hUULQ4Wt68fuO4zQaDSyKodDLHXy+W62WqqqJRGL+b0Zb1Go1cvciQFtIkpRIJBasOXJtkcvl6DQod1CYyrLcoy1w35/WadzpDBbxeHxBPWRZlm3bsViMbMGdAQbucCohxLIswzCq1Sq6G+hoS4/bBFhLiXFXVVV6rAXBGENbNBoN1+932QJLnBzHSafTVBssDvQ1tVrNtUUsFnO9v+M4+FKgr0+n06RHxYG2MAwD1wmSJJEtBoUbg2VZlmVZkqROF4S2wK00WZbJFkJxE3WzbdEVuGVZzmazXNZpYVZCiOv6MdZKkoR/Tfwra5pGz3TfIFsMD+huUKRi9HXvbVBVVdM0ygP1BzQBvhSzbZFOp2VZJlv0B3fNBgBoiy4HRYu0vrFgsOBri/AroS7o6uYhASsFyBbDANlieCBbDA9ki+FBtC1c4bFM0C8YNuixHhLIEMMD2WJ4IFsMD2SL4aFvtghDFT1BEARBEIQ/SAkRBEEQBBFdSAkRBEEQBBFdSAkRBEEQBBFdSAkRBEEQBBFdSAkRBEEQBBFdSAkRBEEQBBFdoqKENE0b9BSI/kHmjhRk7khB5o4U/TF3VJQQQRAEQRDEbEgJEQRBEAQRXUgJEQRBEAQRXUgJEQRBEAQRXUgJEQRBEAQRXUgJEQRBEAQRXUgJEQRBEAQRXZYcPXp00HMAoBYRBEEQBEH0l1qtBsOjhAiCIAiCIPoP7Y4RBEEQBBFdSAkRBEEQBBFdSAkRBEEQBBFdSAkRBEEQBBFdSAkRBEEQBBFdSAkRBEEQBBFdSAkRBEEQBBFdSAkRBEEQBBFdSAkRBEEQBBFdSAkRBEEQBBFdSAkRBEEQBBFdSAkRBEEQBBFdSAkRBEEQBBFdSAkRBEEQBBFdIqGEjhw5snfv3meffXbQEyH4QAYluqjVaoOeAsENsibRbrf379//1FNPvf766334dcv68DsGy44dO6anp9Pp9CuvvPLaa69t2bJl7dq1g54U4Z+ABv3MZz5z8ODBzk82btx49dVX854m0Sf+8z//8/bbb69Wq88888yg50IEJYg16dUODTt27NixY8ebb76J/3neeed96UtfGhkZEfcbQ66Ebr311scee+yRRx5Zs2YNANxxxx1XXHGFrusf+tCHBj01wg8BDVosFn/2s591frJs2bJLLrlEyFwJwezbt2/79u179+49cuTIihUrBj0dIhABrUmvdmi47bbbHnjggdNPP/0jH/nIf//3fzebzX379l166aX333+/pmmCfumSo0ePChp64BSLxYmJieuvv/7GG2/ET9rt9gUXXLBixYqdO3fGYrHBTo/wSnCDXnzxxX/1V3+lKIr7iaIo5513nqgZEyJpNpuKonzrW9+amppasWIF5YQWNQGtSa92ONi/f//f/M3ffO1rX7vgggvwE3wkAOADH/jA9773PUG/N7Q5oXa7nc/nAeDiiy92P1y6dOmFF174wAMP3H///ZQ1XVwEN+jjjz9+6qmnXnnllWInSvQLDHtnnHHGoCdCcCCINenVDg2PPvro9u3b161b537y2c9+9pVXXtm2bdvPf/7z559//o//+I9F/N7Qnpjes2fPwYMHV6xYgdsoLrhKePDBBwc0L8InwQ165513UrY8fCxfvnzQUyC44c+a9GqHhrVr13bKIOTyyy/Hf9i2Lej3hlYJ7dq1CwDOOuusrs9PO+00AHjhhReef/75AUyL8EtAgz7xxBOWZW3atOlP/uRPNm3aRMUpBBEO6NUOE5dddtnsDxVFWbZsGYhMAIdWCeH7kEgkuj4/55xz8B+WZfV7TkQAAhr061//Ov7jzTfffPTRRz/1qU9t3rz58OHDAmZKEET/oFc79Bw5cuSdd955//vfP3slzIvQnhN67rnnAGD2KVqUlgDQbDb7PSciAAEN+tBDD1mW9eKLL+7Zs+exxx575513HnzwwV/96ld33XXXCSecIGjOBEGIhl7t0LN3714AEHoOLLQ5oUOHDsG8u860O7a4CGjQWCy2bt26iy666Ctf+Uq5XP74xz8OAIZh/Nu//Rv3qRIE0Tfo1Q493/3ud88444wrrrhC3K8IrRJaEFouhIzeDfre9773m9/85l/8xV8AwPT0dH96mBI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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":56448,"title":"nth permutation of 11...100...0","description":"Given some number of ones and zeros, numOnes and numZeros respectively, find the nth permutation of the vector [ones(1,numOnes), zeros(1,numZeros)] according to the lexicographical order.  For example, if numOnes = 3 and numZeros = 2 then there are nchoosek(5,3) = 10 permutations of 11100.  The lexicographic numbering is below\r\n1     11100\r\n2     11010\r\n3     11001\r\n4     10110\r\n5     10101\r\n6     10011\r\n7     01110\r\n8     01101\r\n9     01011\r\n10   00111\r\nso for instance nthPerm(3,2,8) = [0,1,1,0,1].  You can assume that numOnes and numZeros will always be greater than or equal to 1.  Lastly, your code should not enumerate all possibilities since one of the test cases contains billions of permutations.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 435px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 217.5px; transform-origin: 407px 217.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eGiven some number of ones and zeros, numOnes and numZeros respectively, find the nth permutation of the vector [ones(1,numOnes), zeros(1,numZeros)] according to the lexicographical order.  For example, if numOnes = 3 and numZeros = 2 then there are nchoosek(5,3) = 10 permutations of 11100.  The lexicographic numbering is below\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e1     11100\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e2     11010\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e3     11001\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e4     10110\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e5     10101\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e6     10011\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e7     01110\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e8     01101\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e9     01011\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e10   00111\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eso for instance nthPerm(3,2,8) = [0,1,1,0,1].  You can assume that numOnes and numZeros will always be greater than or equal to 1.  Lastly, your code should not enumerate all possibilities since one of the test cases contains billions of permutations.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = nthPerm(numOnes,numZeros,n)\r\n  y = [];\r\nend","test_suite":"%%\r\nnumOnes = 1;\r\nnumZeros = 1;\r\nn = 1;\r\ny_correct = [1,0];\r\nassert(isequal(nthPerm(numOnes,numZeros,n),y_correct))\r\n\r\n%%\r\nnumOnes = 3;\r\nnumZeros = 2;\r\nn = 4;\r\ny_correct = [1,0,1,1,0];\r\nassert(isequal(nthPerm(numOnes,numZeros,n),y_correct))\r\n\r\n%%\r\nnumOnes = 10;\r\nnumZeros = 1;\r\nn = 11;\r\ny_correct = [0,1,1,1,1,1,1,1,1,1,1];\r\nassert(isequal(nthPerm(numOnes,numZeros,n),y_correct))\r\n\r\n%%\r\nnumOnes = 18;\r\nnumZeros = 7;\r\nn = 408913;\r\ny_correct = [0,1,1,0,1,1,1,0,0,0,1,0,1,1,1,1,1,1,1,1,1,0,1,1,1];\r\nassert(isequal(nthPerm(numOnes,numZeros,n),y_correct))\r\n\r\n%%\r\nnumOnes = 17;\r\nnumZeros = 23;\r\nn = 40207127;\r\ny_correct = [1,1,1,1,1,1,1,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,1,1,0,1,0,0,0,1];\r\nassert(isequal(nthPerm(numOnes,numZeros,n),y_correct))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":2198965,"edited_by":2198965,"edited_at":"2022-11-04T08:56:28.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-11-03T23:19:23.000Z","updated_at":"2026-06-03T00:30:12.000Z","published_at":"2022-11-03T23:52:22.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven some number of ones and zeros, numOnes and numZeros respectively, find the nth permutation of the vector [ones(1,numOnes), zeros(1,numZeros)] according to the lexicographical order.  For example, if numOnes = 3 and numZeros = 2 then there are nchoosek(5,3) = 10 permutations of 11100.  The lexicographic numbering is below\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e1     11100\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e2     11010\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e3     11001\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e4     10110\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e5     10101\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e6     10011\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e7     01110\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e8     01101\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e9     01011\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e10   00111\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eso for instance nthPerm(3,2,8) = [0,1,1,0,1].  You can assume that numOnes and numZeros will always be greater than or equal to 1.  Lastly, your code should not enumerate all possibilities since one of the test cases contains billions of permutations.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":60227,"title":"Rotate matrix 60 degrees","description":"Given a 2*m-1 by 4*m-3 matrix in which certain elements form a hexagon of side length m, rotate the hexagon 60 degrees counterclockwise, as shown below for m=3:\r\nA=[\r\n'--A-A-A--'\r\n'-B-B-B-B-'\r\n'C-C-C-C-C'\r\n'-D-D-D-D-'\r\n'--E-E-E--']\r\n\r\nrot60(A)=[\r\n'--A-B-C--'\r\n'-A-B-C-D-'\r\n'A-B-C-D-E'\r\n'-B-C-D-E-'\r\n'--C-D-E--']\r\nA=rot60(A,n) should rotate the hexagon 60*n degrees counterclockwise (and thus clockwise if n\u003c0).","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 348.75px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 331.989px 174.375px; transform-origin: 331.996px 174.375px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42.017px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 308.991px 21.0085px; text-align: left; transform-origin: 308.999px 21.0085px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven a 2*m-1 by 4*m-3 matrix in which certain elements form a hexagon of side length m, rotate the hexagon 60 degrees counterclockwise, as shown below for m=3:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 265.724px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 328.991px 132.855px; transform-origin: 328.999px 132.862px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 328.991px 10.2131px; text-wrap: nowrap; transform-origin: 328.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eA=[\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 328.991px 10.2131px; text-wrap: nowrap; transform-origin: 328.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'--A-A-A--'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 328.991px 10.2131px; text-wrap: nowrap; transform-origin: 328.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'-B-B-B-B-'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 328.991px 10.2131px; text-wrap: nowrap; transform-origin: 328.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'C-C-C-C-C'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 328.991px 10.2131px; text-wrap: nowrap; transform-origin: 328.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'-D-D-D-D-'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 328.991px 10.2131px; text-wrap: nowrap; transform-origin: 328.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'--E-E-E--'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 328.991px 10.2131px; text-wrap: nowrap; transform-origin: 328.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 328.991px 10.2131px; text-wrap: nowrap; transform-origin: 328.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003erot60(A)=[\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 328.991px 10.2131px; text-wrap: nowrap; transform-origin: 328.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'--A-B-C--'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 328.991px 10.2131px; text-wrap: nowrap; transform-origin: 328.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'-A-B-C-D-'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 328.991px 10.2131px; text-wrap: nowrap; transform-origin: 328.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'A-B-C-D-E'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 328.991px 10.2131px; text-wrap: nowrap; transform-origin: 328.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'-B-C-D-E-'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 328.991px 10.2131px; text-wrap: nowrap; transform-origin: 328.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'--C-D-E--'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0085px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 308.991px 10.4972px; text-align: left; transform-origin: 308.999px 10.5043px; white-space-collapse: preserve; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eA=rot60(A,n) should rotate the hexagon 60*n degrees counterclockwise (and thus clockwise if n\u0026lt;0).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function A=rot60(A,n)\r\nA=rot90(A,2*n/3);\r\n","test_suite":"%%\r\nA=['--A-A-A--'\r\n   '-B-B-B-B-'\r\n   'C-C-C-C-C'\r\n   '-D-D-D-D-'\r\n   '--E-E-E--'];\r\nB=['--A-B-C--'\r\n   '-A-B-C-D-'\r\n   'A-B-C-D-E'\r\n   '-B-C-D-E-'\r\n   '--C-D-E--'];\r\nassert(isequal(rot60(A),B))\r\n%%\r\nA=['  A A A  '\r\n   ' B B B B '\r\n   'C C C C C'\r\n   ' D D D D '\r\n   '  E E E  '];\r\nB=['  C B A  '\r\n   ' D C B A '\r\n   'E D C B A'\r\n   ' E D C B '\r\n   '  E D C  '];\r\nassert(isequal(rot60(A,-1),B))\r\n%%\r\nA=['   3 1 4 1   '\r\n   '  5 9 2 6 5  '\r\n   ' 3 5 8 9 7 9 '\r\n   '3 2 3 8 4 6 2'\r\n   ' 6 4 3 3 8 3 '\r\n   '  2 7 9 5 0  '\r\n   '   2 8 8 4   '];\r\nB=['   1 5 9 2   '\r\n   '  4 6 7 6 3  '\r\n   ' 1 2 9 4 8 0 '\r\n   '3 9 8 8 3 5 4'\r\n   ' 5 5 3 3 9 8 '\r\n   '  3 2 4 7 8  '\r\n   '   3 6 2 2   '];\r\nassert(isequal(rot60(A),B))\r\n%%\r\nA=['    A B C D E    '\r\n   '   F G H I J K   '\r\n   '  L M N O P Q R  '\r\n   ' S T U V W X Y Z '\r\n   '1 2 3 4 5 6 7 8 9'\r\n   ' a b c d e f g h '\r\n   '  i j k l m n o  '\r\n   '   p q r s t u   '\r\n   '    v w x y z    '];\r\nB=['    E K R Z 9    '\r\n   '   D J Q Y 8 h   '\r\n   '  C I P X 7 g o  '\r\n   ' B H O W 6 f n u '\r\n   'A G N V 5 e m t z'\r\n   ' F M U 4 d l s y '\r\n   '  L T 3 c k r x  '\r\n   '   S 2 b j q w   '\r\n   '    1 a i p v    '];\r\nassert(isequal(rot60(A),B))\r\nn=randi([-100 100],1);\r\nassert(isequal(rot60(A,3*n),rot90(A,2*n)))\r\n%%\r\nA=[nan nan 120 nan  90 nan  60 nan nan\r\n   nan 150 nan 120 nan  60 nan  30 nan\r\n   180 nan 180 nan nan nan   0 nan   0\r\n   nan 210 nan 240 nan 300 nan 330 nan\r\n   nan nan 240 nan 270 nan 300 nan nan];\r\nfor n=1:6\r\n   assert(isequaln(rot60(A,n),mod(A-60*n,360)))\r\nend\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":245,"edited_by":245,"edited_at":"2024-05-10T20:49:04.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2024-05-08T01:12:38.000Z","updated_at":"2026-06-05T03:50:02.000Z","published_at":"2024-05-10T20:49:04.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a 2*m-1 by 4*m-3 matrix in which certain elements form a hexagon of side length m, rotate the hexagon 60 degrees counterclockwise, as shown below for m=3:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[A=[\\n'--A-A-A--'\\n'-B-B-B-B-'\\n'C-C-C-C-C'\\n'-D-D-D-D-'\\n'--E-E-E--']\\n\\nrot60(A)=[\\n'--A-B-C--'\\n'-A-B-C-D-'\\n'A-B-C-D-E'\\n'-B-C-D-E-'\\n'--C-D-E--']]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA=rot60(A,n) should rotate the hexagon 60*n degrees counterclockwise (and thus clockwise if n\u0026lt;0).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":1499,"title":"Kryptos - CIA Cypher Sculpture: Vigenere Encryption","description":"The \u003chttp://en.wikipedia.org/wiki/Kryptos Kryptos Sculpture\u003e contains four encypted messages.\r\n\r\nThis Challenge is to Encrypt two of the original messages for the sculptor.\r\n\r\nThe method employed is \u003chttp://en.wikipedia.org/wiki/Vigen%C3%A8re_cipher Vigenere Encryption\u003e. One clarification is that \"?\" are removed from the coding sequence and then re-inserted in the final encoded message.\r\n\r\nOriginal phrase: Between subtle shading and the absence of light lies the nuance of iqlusion.\r\n\r\nFor coding purposes spaces and punctuation are removed, except \"?\".\r\n\r\nPhrase to encode: BETWEENSUBTLESHADINGANDTHEABSENCEOFLIGHTLIESTHENUANCEOFIQLUSION\r\n\r\n*Input:* Encode Phrase, Vigenere alphabet word, Vigenere shift word\r\n\r\nVigenere alphabet word ='KRYPTOS';\r\n\r\nVigenere shift word ='PALIMPSEST';\r\n\r\n*Output:* EMUFPHZLRFAXYUSDJKZLDKRNSHGNFIVJYQTQUXQBQVYUVLLTREVJYQTMKYRDMFD\r\n\r\nThe encryption matrix for this case:\r\n\r\n  KRYPTOSABCDEFGHIJLMNQUVWXZ\r\n\r\n  PTOSABCDEFGHIJLMNQUVWXZKRY\r\n  ABCDEFGHIJLMNQUVWXZKRYPTOS\r\n  LMNQUVWXZKRYPTOSABCDEFGHIJ\r\n  IJLMNQUVWXZKRYPTOSABCDEFGH\r\n  MNQUVWXZKRYPTOSABCDEFGHIJL\r\n  PTOSABCDEFGHIJLMNQUVWXZKRY\r\n  SABCDEFGHIJLMNQUVWXZKRYPTO\r\n  EFGHIJLMNQUVWXZKRYPTOSABCD\r\n  SABCDEFGHIJLMNQUVWXZKRYPTO\r\n  TOSABCDEFGHIJLMNQUVWXZKRYP\r\n\r\nFollow Up Challenges:\r\n\r\n1) \u003chttp://www.mathworks.com/matlabcentral/cody/problems/1500-kryptos-cia-cypher-sculpture-vignere-decryption Vigenere Decryption\u003e\r\n\r\n2) Dictionary search\r\n\r\n3) KRYPTOS Part IV\r\n\r\n\u003chttp://math.ucsd.edu/~crypto/Projects/KarlWang/index2.html#1 KRYPTOS Solutions\u003e\r\n\r\n  \r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","description_html":"\u003cp\u003eThe \u003ca href = \"http://en.wikipedia.org/wiki/Kryptos\"\u003eKryptos Sculpture\u003c/a\u003e contains four encypted messages.\u003c/p\u003e\u003cp\u003eThis Challenge is to Encrypt two of the original messages for the sculptor.\u003c/p\u003e\u003cp\u003eThe method employed is \u003ca href = \"http://en.wikipedia.org/wiki/Vigen%C3%A8re_cipher\"\u003eVigenere Encryption\u003c/a\u003e. One clarification is that \"?\" are removed from the coding sequence and then re-inserted in the final encoded message.\u003c/p\u003e\u003cp\u003eOriginal phrase: Between subtle shading and the absence of light lies the nuance of iqlusion.\u003c/p\u003e\u003cp\u003eFor coding purposes spaces and punctuation are removed, except \"?\".\u003c/p\u003e\u003cp\u003ePhrase to encode: BETWEENSUBTLESHADINGANDTHEABSENCEOFLIGHTLIESTHENUANCEOFIQLUSION\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e Encode Phrase, Vigenere alphabet word, Vigenere shift word\u003c/p\u003e\u003cp\u003eVigenere alphabet word ='KRYPTOS';\u003c/p\u003e\u003cp\u003eVigenere shift word ='PALIMPSEST';\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e EMUFPHZLRFAXYUSDJKZLDKRNSHGNFIVJYQTQUXQBQVYUVLLTREVJYQTMKYRDMFD\u003c/p\u003e\u003cp\u003eThe encryption matrix for this case:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eKRYPTOSABCDEFGHIJLMNQUVWXZ\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003ePTOSABCDEFGHIJLMNQUVWXZKRY\r\nABCDEFGHIJLMNQUVWXZKRYPTOS\r\nLMNQUVWXZKRYPTOSABCDEFGHIJ\r\nIJLMNQUVWXZKRYPTOSABCDEFGH\r\nMNQUVWXZKRYPTOSABCDEFGHIJL\r\nPTOSABCDEFGHIJLMNQUVWXZKRY\r\nSABCDEFGHIJLMNQUVWXZKRYPTO\r\nEFGHIJLMNQUVWXZKRYPTOSABCD\r\nSABCDEFGHIJLMNQUVWXZKRYPTO\r\nTOSABCDEFGHIJLMNQUVWXZKRYP\r\n\u003c/pre\u003e\u003cp\u003eFollow Up Challenges:\u003c/p\u003e\u003cp\u003e1) \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/1500-kryptos-cia-cypher-sculpture-vignere-decryption\"\u003eVigenere Decryption\u003c/a\u003e\u003c/p\u003e\u003cp\u003e2) Dictionary search\u003c/p\u003e\u003cp\u003e3) KRYPTOS Part IV\u003c/p\u003e\u003cp\u003e\u003ca href = \"http://math.ucsd.edu/~crypto/Projects/KarlWang/index2.html#1\"\u003eKRYPTOS Solutions\u003c/a\u003e\u003c/p\u003e","function_template":"function encoded=encode_vigenere(phrase,word1,word2)\r\n encoded=phrase;\r\nend","test_suite":"phrase=upper('Between subtle shading and the absence of light lies the nuance of iqlusion.');\r\nphrase_encode=phrase(regexp(phrase,'[A-Z?]'));\r\n\r\nencoded_exp='EMUFPHZLRFAXYUSDJKZLDKRNSHGNFIVJYQTQUXQBQVYUVLLTREVJYQTMKYRDMFD';\r\nword1='KRYPTOS';\r\nword2='PALIMPSEST';\r\nencoded= encode_vigenere(phrase_encode,word1,word2);\r\n\r\nassert(strcmp(encoded_exp,encoded))\r\n%%\r\n\r\nphrase=upper('It was totally invisible Hows that possible? They used the Earths magnetic field X The information was gathered and transmitted undergruund to an unknown location X Does Langley know about this? They should Its buried out there somewhere X Who knows the exact location? Only WW This was his last message X Thirty eight degrees fifty seven minutes six point five seconds north Seventy seven degrees eight minutes forty four seconds west ID by rows');\r\nphrase_encode=phrase(regexp(phrase,'[A-Z?]'));\r\n\r\n\r\nencoded_exp='VFPJUDEEHZWETZYVGWHKKQETGFQJNCEGGWHKK?DQMCPFQZDQMMIAGPFXHQRLGTIMVMZJANQLVKQEDAGDVFRPJUNGEUNAQZGZLECGYUXUEENJTBJLBQCRTBJDFHRRYIZETKZEMVDUFKSJHKFWHKUWQLSZFTIHHDDDUVH?DWKBFUFPWNTDFIYCUQZEREEVLDKFEZMOQQJLTTUGSYQPFEUNLAVIDXFLGGTEZ?FKZBSFDQVGOGIPUFXHHDRKFFHQNTGPUAECNUVPDJMQCLQUMUNEDFQELZZVRRGKFFVOEEXBDMVPNFQXEZLGREDNQFMPNZGLFLPMRJQYALMGNUVPDXVKPDQUMEBEDMHDAFMJGZNUPLGEWJLLAETG';\r\n\r\nword1='KRYPTOS';\r\nword2='ABSCISSA';\r\nencoded= encode_vigenere(phrase_encode,word1,word2);\r\n\r\nassert(strcmp(encoded_exp,encoded))\r\n%%\r\nphrase=upper('The fox jumped over the moon');\r\nphrase_encode=phrase(regexp(phrase,'[A-Z?]'));\r\n\r\nencoded_exp='VUIPFSBYVQMMWPIMEVPZCVK';\r\nword1='KRYPTOS';\r\nword2='MATLAB';\r\nencoded= encode_vigenere(phrase_encode,word1,word2);\r\n\r\nassert(strcmp(encoded_exp,encoded))\r\n\r\n%%\r\nphrase=upper('Between the Devil and the deep blue sea');\r\nphrase_encode=phrase(regexp(phrase,'[A-Z?]'));\r\n\r\n\r\nword1='AWEIGH';\r\nword2='MATLAB';\r\nencoded= encode_vigenere(phrase_encode,word1,word2);\r\nencoded_exp='SENMEDWTZNDDFIBLNNCHVTEDIBBCEZOA';\r\n\r\nassert(strcmp(encoded_exp,encoded))\r\n\r\n\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":5,"comments_count":3,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":64,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":28,"created_at":"2013-05-11T20:36:34.000Z","updated_at":"2026-06-05T12:06:16.000Z","published_at":"2013-05-11T21:19:07.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Kryptos\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eKryptos Sculpture\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e contains four encypted messages.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to Encrypt two of the original messages for the sculptor.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe method employed is\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Vigen%C3%A8re_cipher\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eVigenere Encryption\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. One clarification is that \\\"?\\\" are removed from the coding sequence and then re-inserted in the final encoded message.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOriginal phrase: Between subtle shading and the absence of light lies the nuance of iqlusion.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor coding purposes spaces and punctuation are removed, except \\\"?\\\".\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePhrase to encode: BETWEENSUBTLESHADINGANDTHEABSENCEOFLIGHTLIESTHENUANCEOFIQLUSION\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Encode Phrase, Vigenere alphabet word, Vigenere shift word\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eVigenere alphabet word ='KRYPTOS';\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eVigenere shift word ='PALIMPSEST';\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e EMUFPHZLRFAXYUSDJKZLDKRNSHGNFIVJYQTQUXQBQVYUVLLTREVJYQTMKYRDMFD\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe encryption matrix for this case:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[KRYPTOSABCDEFGHIJLMNQUVWXZ\\n\\nPTOSABCDEFGHIJLMNQUVWXZKRY\\nABCDEFGHIJLMNQUVWXZKRYPTOS\\nLMNQUVWXZKRYPTOSABCDEFGHIJ\\nIJLMNQUVWXZKRYPTOSABCDEFGH\\nMNQUVWXZKRYPTOSABCDEFGHIJL\\nPTOSABCDEFGHIJLMNQUVWXZKRY\\nSABCDEFGHIJLMNQUVWXZKRYPTO\\nEFGHIJLMNQUVWXZKRYPTOSABCD\\nSABCDEFGHIJLMNQUVWXZKRYPTO\\nTOSABCDEFGHIJLMNQUVWXZKRYP]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFollow Up Challenges:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e1)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/1500-kryptos-cia-cypher-sculpture-vignere-decryption\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eVigenere Decryption\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e2) Dictionary search\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e3) KRYPTOS Part IV\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://math.ucsd.edu/~crypto/Projects/KarlWang/index2.html#1\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eKRYPTOS Solutions\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":60743,"title":"Bit Stream conversion to Audio Frequency Shift Key using Bell 202 (Modem)","description":"Convert a character binary bit-stream transmitted at a certain baud-rate into an audio stream using Audio Frequency Shift Key (AFSK) using the Bell 202 standard at a given sample rate. \r\nFrequency of sine audio wave: '1' = 1200 Hz, '0' = 2200 Hz\r\nDuration of each bit: based on baud-rate\r\nDigitized audio stream is produced at the sample rate and is smooth between bits (initially starts at zero phase shift). Normalize final signal to +-1 peak-to-peak.\r\nplot(binaryToBell202('101',1.2e5,600))\r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 500.438px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 408px 250.219px; transform-origin: 408px 250.219px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 21px; text-align: left; transform-origin: 385px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eConvert a character binary bit-stream transmitted at a certain baud-rate into an audio stream using Audio Frequency Shift Key (AFSK) using the Bell 202 standard at a given sample rate. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFrequency of sine audio wave: '1' = 1200 Hz, '0' = 2200 Hz\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eDuration of each bit: based on baud-rate\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 21px; text-align: left; transform-origin: 385px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eDigitized audio stream is produced at the sample rate and is smooth between bits (initially starts at zero phase shift). Normalize final signal to +-1 peak-to-peak.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 20.4375px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 405px 10.2188px; transform-origin: 405px 10.2188px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; text-wrap-mode: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eplot(binaryToBell202(\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'101'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e,1.2e5,600))\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 307px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 385px 153.5px; text-align: left; transform-origin: 385px 153.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline\" 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hgBg1GafGQOt3o7w7NGwAIDR13fwCIOXL1++/9efguu3nmvRWhjr8GcUEQ5/lIiy0M6FfR7ahmTXxVgY8wj2GWnravclA9Zes8+MAfaUsa84H6suhgUAzB7vAMo7+lYoCLj+ww8/VH38A1oLY90pQkTMBmTAMoWRTV6jXRO7qbUNydaBdu+1+6LlTgHA3jsGrDl5zmdoANR39PiY5/nz501jx7VlYOD1lu8A5MtbRgDMKCJWg4xs8hrtmlhrF7Tny9aBdu/ZZ2QJAM11GbB6F2tgnhkDln6151hymgCoqb9DqMGi8E5BFsj87R12AzLA2kaJKAttY+A6ZlOPCgCMp9EVW3/a8UZp4+q9q9X5ERgD+6RBfPHp06ePf2ZA/QioBQJkLwBkcXjI7zpfUUTMdzcYQyuibLSNgbNgGrG2LlsH2nWw62rHu0IAjFrDHiN6V3xRbpIzoH0JjGtwLV4jyDuA8mclskBBAgBiijKbiJgNMlMAYB4rAL4MW3+oqw2AEdpg9+4s+gYZvWvZp9onmewGQP0RTsvkS1rfAdTvIErqhTGNbSaTBNIgDBExmy0K5oG9PoJtxHcMAE3dUbpnanK23mUGgGefhgUAENOX3+wpP85pvUNACMi19XM19cKYTTNrADAaZJYAkMYYEQCoqwkAthFrgwfXMPU3ewAw68pYo/UtSL8xAkB6xjLW0ADIpEcAzCIiZoN4RJSBZR4ZAYA9PWJUADDPG1gDoLfumfsshjtL77Lno9WucJsAYJrEbCJiG5FVRBlYA2DE+rHvuJYFxhoRANrgGaV7pr4tuuoB+ywx1gqAz6kXBvNnBcBsIrp7AIwyiFHNK3VZRjx7AIx6h9cDdu9qz1K4VQBo3uZqmUlE7Dtgq4gyELPRBgCuZXDHAMBYMwcA8+YNaPe5BxkBYPG5WwUA09RmEhH7DskqogwsZsM04lEBIHU1mmIbsbauZW+YsG/eZrjBEdi9a92r2wQA+9BnEhEOfaSIMhgVAFLXEgAMI7aYq2VvNGCsmQNg9a4e9O0KgM+pFwbhskU02iQFtmFjLKYoPVjMNSMANHWZRmwxV6YRy1iWANBcy4Rt2DPc4Aije/cWAZAh3JlExG6QmQJAg1yLc45y1wDQjoVrewcAu+ZsAcDsXYwFP9ByqwBgNIwAAc0iIqxtpIgysAQA04gtYzF15anLMEXrGlh1tTD3WJjhBkdg37xhLOyXllsEgDTXVUXEbkqriDKwhJDFPI+wBA/TnKxrwLUrAHygd0ff4AhYGzMALPoFtwoAhkEIFoPKJKNBrCLKwBMAjD24WwBY9459x3qEdX4aMH+MORrRDzNQRb9aHd0qAJjMJiJmg1hFlIElAJh7YKkLUJfRwLLnWlh1pTe0Zz0qAJhatO51Fhm9a92vWwRAxoHPIqKMBskY04rlbTqzkc4SACwjtp416vb87usOvcvQrWDthVsEgLWpNYiIRpoksDawBqaherF+x4L5Moz4bgHgqdszADJ6N6NnPGT2rlaTKwCczCIiawNrsYgogzMFAMOIrXVxLaOuVT84kxUAHGQebCy9cIsAsJqJBjk8/DmSjAYBFhFlgPMaEQBWrbCM2BMADCOePQBQi61vuUse3bvWvddi6YVbBIDVTDTMIqLMAGAYmxer0VhEv8dZAoBlxNa61v2JklWPpZcIWb1r0eRtAoDRLDUziCirQVjG5sV6Zqz5oq5lP1GXoS2MYTEDlqZnDwDreWi5egBotXGLAMCGZJjZDCLKahCLiDLAmiz1WWds3U/rPLewGiuuZ9Q9QwAw1lmT5QkWsvbSsmcrAALMIKKsBskaV4t1b3E9Y77WdbP2CWNgLC3W67fAONg7Ldbro2T1GEsvEXB+jDOssWjjFgGQdac+i4gy5mARUQbWxmedhXU/WfuPcSz7zTof6zg4E+x1L9C7GQHAOrcIWXOwnOnlA8D6e7EWZhCR1Si1WI2BjXVdrLOw1mXtk3X+uJZhxNb5jwiAjN7Fui37nUFW71rO6DYBkPHbOjOIKOsOqXej11jXdbcAYJ0P6loDAGfTg+zeZZxbhMwA0J7R5QMA4rmyiLC2jDskjNmr0VtY18U6izsGgKVuT11kBwBj/yLM0Lu3CQCIiQ2rCb1kNshdAwB1rQHA0IA1eHAt43xmDoDMmzfW/nmR3s0MAI3n3SYAMphFRJkBkBGcR3jWxQwAS1NCAyMCgGXE3gDooQvp3YxarP3zktm7ln27fABkHvRoEWU2SObYR3iag3Unjro4Vy1nDwBv3R66uHLvejSuRXpXM/blA4DVoC16NkOLTJPOFOgRntqMc5a6OFctLCMZVdcaAJmaq8G8GGts0XMdLWbp3RUAAUaLiGUCLUREFlNiIfuqEbDADABL3dEBENXe7AFw1d7Nrq/V0+UDgPXZcIvRIspsEKAVERvPvmIv8JoIqIcxUF8LIwCkrmWvWdrDGJYA8OyRl0x991xHC4Zu9sDYKwA+f8D8swJgtIhWAPwzTCO2nKfUtcy1xlPXs0ctMIbljD1z9ZJ58wasa2eS3bsYWxPstwgAy285WBkpouwG0YqIzagAkLq9jTgSAJbXtMAYswZA5s0bsK6dSY8A0PjeLQIg08RGiqhHAGSG5xYeM2cGgMXMPa+p8Zgqw4hlDKt+Pa/xAG1n6m/UDQ7I7l3t3l0+ALIPeaSIcMgziIhNJAAiRjwqAGSMUQFgHQOv6REAq3f9oG9XAHz+gFizRTTCJEG2QWtFxMYTAEwjtozBMOJI3YgRe+ceraulRwBcuXc1AXPpAPjmd76fLtbsg9wju0G0ImKDNWFtFhgB4AmeUQEAotqePQCy64zs3R4BoOmhWwRApDmPGCkirC0zADxGzCASAJGzvlsAePcs+8YDyL5mBsCoGxyQvYcYG/t3xAqAICNFlN0gWhGx8QQAw4i9642egyd4QLSuN3h6BsCVezdzD7WaunQAfP0b33UJ3IL2rRabHg3iNaYoIwPAc5aoOyIAokZ89wDwnjeDqGaOWAHQKQBGiahnAGTuXwvPnZnsR6Sp7hYAkbo4o0y84WQBe+dZf5SZevfSAfAXzz5KP2BvE0WRBskUUY8mbOF9a465Row4EgARI/bWHRUAOJsrBMCo3u0RANr9u3QA/Pl3f+pqLAujAyCzQUYFAAxmRAB4gydqxJEAiBjxzAHQo6+kRm99S19lBoA2ZFYABBkloh4NohURmxUAOqJGjLoeDaFmdgB498TCqBucXnU1/XDpAPja3/38siLqEQBAIyI2XmOLGnEkACKGiNeOCgBPXe8+WegRAKNucGbq3csHQLZQR4moR4OAOwUA6nr0grrRAPDU9e6TMHMA9Kgxsnd7BcBRP1w6AL760a+6iai3SfYKgKipevAaanSuXkONGrHX7KIm6dVQtK6GHjXA3Xv38gEQaUwtI0TUq0GipurBWxOvi5w39tPzeu/rBLzec5ZRDXhf38PAvHti5eoBcKTLSwfAV37ymy7mNUJEvRokaqoeUNNzbtiPqBF7Xu81UsF7ltG63tf3CoDIWWrxai1C9Ny0aPZwBQCBESLq1SC96pQgUEcEgPccow3tnTdeEzFi77yxRzijTHr1FOpENOMBex7RixbN+V46AHrdmY8SUY+aXpOI4D236J6cLQAw10gAoK5n3jibqwRAVDMeetXU6PKyAfCDH33SLQBGiKhXg2hExMZ7btG5RgIgYsTeuowA8Oi2RwD06l2s37MHEXr1rkYfl34HgMX3+BWvESJCg8wiIiaR36rCGUQCwLun0T3yGgJeEzHiWQOg569nRjXjwXveVjT6uHQAZC2sZoSIcLA97pCiJmMl0vyMAPDsKfZoRABEjTgaADirDHoHQOTsPGBtPQJAo48VAAR6i6hng0RNxko0ACLngLreAIjsEV47IgC8wYOzQd2sAJDxe+gb6x/Rux6dWRF97J3TCgACUQOwMiIAspq9JrK2SDNHGjNqxKPqzh4APTQX3UMrEX1b0ezjqQLg1atXD0+ePHl8PHv27OHt27fvn/kyPQOgt4jkYGcREZPI2hgB4Kk7OgC8ZzN7APRgBcBJAuD169d/YvoIgxcvXjy8e/fu8d81dwiArAYskVo9BAsia4u8E4s0ZuT8pa4nAKI6wGs9ARDZKw09+0lqeffQSvTMLGjO6RQBAJOH2cP0BQTB8+fPH968efP+J3/KiADoJaKeDZLd7DWRBonsS2SdkfOP1I3sFcBrMXcrkTlriLyTsxLdQyu96x2d8SkCAGaPu3+8CxBaoVDSMwB6H2rkTtfDkYiYRPYyEgBSF39aicwZr4nW9bwW4LV3D4DstdRENOrh6IxPEwCtu/2XL19OEQC9RdSzQcCRiJhEGiTyWjFTj4lHXhvRDuO13nONvPaI6K/zWojug5XevXv0Pc+lAwBfFvcKgjuLiEnExOW1HiMeFQDy2lEB4HktwGuz9A7z7/nf1WSupWaW3hVffPr0aZo/Dv8ISBaXtcCSniLqeYcEICLU7EGkQSJGHAmPiJlG5ix1PbqLzBl462rw/gdqXjLXUhP9b1WsbPWu+KLcJGdwmy+BAUTU6y659x1Sz4YcHQAeRgUAwGtXAMSA3nr1LtY1U+9m+iQtAMDMvwYKeoqod4P0rBdpEDFTj6ndLQAiewUy9d6zlwDq9dI3tO3Vt4ejfjpNAACY/oz/IRjoKaLeDRIxZSuRBokYMfYTr/XiNeJI8ABv3WjwZGoQ8+qpb+itZwD0qgUuFQAWegdAz4MdEQCo2QPs455g94gGQGSNXiOOBoBXC7MHgGcvvUDfV+3dI12vACBxlLRM0CAziYhJJEglADzmEV2jt+6oAJC6kQDIMM1IiHvp2bve8/KCWnv6WgFAopeIIibnJWpSFqIN4t0bRgB45h2t692v6JlGgnqPUQEQOQMLXn16OTrnFQAkoo2sZUSD9AwA1PEYmuBtsKgJeI2YEQAeI541AOSjqZ76xhn00PfImzfUbrECgERvEY0IgC0RMYk2iPf1MLPIO7hRAeA14qheUTMzAHpoTRB9ZzOid4/2cwUAiV4iGtEgvWoyGsRrxIwA8BgiXjMqACJ1o/u1Ra8+KrlyABzVXAFAQkSUbZIjAqCXcBl1vAEAMxsVAJG6KwDi9OqpGXt3BQCJXoc7okFERKidiezhllg1RALAY6SC9/VRI/W+ftYAiM7LQ6/e7VWnZq93VwCQ6HW4IxoE7ImIBWMPsTceIx4VAHhdxEi9Rhw18CwdjtC33OBEbjw0jLh5A3u9uwKARC8RXTkAGA0SMeJIAHgNFa+JGPHVAiA6Ly899D2qd1ETtVusACDSQ0RokNlExGJkAETX5zUu73wFrx688xWwV9GzaoE5Rebl5eoBsKWxFQBEegXAiAbZExELRoN4DRV1zxgA3j1D3YiOGGHdIrofXrCWyPlr8Gokyt6ergAgEjURDdHG9dKjMRkB4G0yRgB45h6t692z6HlmBkB2D7WInoMGrzajoO7WWa8AINJDRD2MuMWeiFhg/GiDeMeI3gF6jZgRAB4jjuooKwB69FAL1M3WN/Y8qm8Pez2xAoBIDxGNahCvsVpgNIh3njCzyMd3owLAa8TYo4hWpS77t94w5gh9R/dDQ48aLVBzS5srAIj0OOCRAeAxOAuM/fPOkxEAHiOOGp43AKI6yvi1Z/lNusg5eIFuoL9MRvUuam71xAoAIj1ENKpBvAZnAXsXbZA9sW/BMB6vEY+qO3MAZP8qdYtevRvVt4c9jawAIJItopF3SF6jscC4Q4oEQMR4vPsTPU+pazXiFQB/ikc3VqJn7WVPmysAiGSLaGSDeA3OAsZnBIB1nqMCQOpGTMFrxHhNZK8ztOhdCwOPbixk7JcW0WZrX1cAEMkWkTTIbCJigfEjZggiRhzZV8/+MOqKJqzngtdE9pox9xrvWhh4dGMhY7+07O3rCgAi2SIa2SDZtVkN4jkDxto8YzDWLHWtY+A1swVAdv/sIbUjGtjDe04M9s5qBQCRXiIaQUbDl7DG95yB7Gvk3DxjMNbsGYNRF2CMSIjU4B109ufwWzA0sEf2+EdsndUKACLZhyzmNootETFg7d2ZAkBegz+9oJ51DM9rWmCMFQA6Zu3dFQBEWI21xcgGAeyGL2E1oGccRnN6zp61ZoxhOReWTq11j/D+NxwMWHuyBUNjEbbOagUAERERsylKRgcAamMOGbAaZAXAMVI3anYYg6mHHr+Lv4d1Hy3M2rsrAMhkimh0g2QGAKtBPEbMDACPEY8KgGhdth6g7RUAOaA2/KNmBQCZTBGNbpAtETEYGQCs2tazx7V4TRRr3ZkDIEtfGtjrKRl987a1tysAyGSKaHSDZNbHuCsAfFg1xwwAph4ye0cDez0l6J2RAYB1teqvACCTLaKssTVsiYgBc2yrEY8KANQdEQCs4GHrEXMaGQCZ/TVr764AIJN50KPvkLZExADjssYeGQCW82HVvVIAWM6NDdZy1d7d0toKADKZIhp9h8QyrBZMM7EaCeoywsfa5Kz9xBiWvZsxADwf3bFh6aCFVRtsULt15isAyGSKyGpsbFjG0YLZINZ9OnsAWI2YVZep9VkCgLEvLWbt3RUAZFjNVTNDg2QGAMZlBYDViGFirACwGDHLcK4QAPLF9Eh9Y18y9C29O0MA1F/8rwAgky2iGQKgFhEDZoOMCgCrEbMMFONcJQAytKUl6wZnht7d2t8VAGSyRDRDg2TNgd0g1jtxq3FvMTIALOPgelYAMMYBWX1j4Q4BUM9hBQAZERHbJLPM10KWkO8aALh+VAAw6rLeSYCsd84Wsnps5t5dAUAm67BnuEMSEWEuTGTPanF6sRoxTMzykdEWVmO1znMLjGEx4lkDgDWWl6zezRrXSqt3VwCQyTrsGRoEtEQUhb1ndwsAqzZQlxUAODcGM+hbbnBYNyLCDDdvoNW7KwDIZInoygHAbhCrEd8xABh1medm3bssMvQ9S++2dL4CIIEMEc3SICyzLGE3iHWvcF6MNVnXwdpLjGEx4hkDAHNaAZAL5lCf+wqABDJENEuDtEQUhd0gngBgnNeoALAaMfaGGQCMj+5Yc4rCOpMSqx6zaO3xCoAEMkQ0S4NkzAPjsQPAMh4zACxGjGtHBAA7eBgBkNEzHjAPtr7RMzMEANZVr20FQAJZImKP6QFzYIuZPSaMRBsA8p0NIwCsRjyqLstsmV/ezxIAGX02c++uAEgg48BnaRC2WQOMxxzTEwCML+1HB4DWiGcMANZeRIG+r9q7rb5YAZBAhojQILOKKAo7MDFH7JeGUQEgdRmmZzViXMvQEmvvmGcQBTpk3+DMFADY55IVAAmwRcQ0iygWk9PCbhCPETMDQGPEzLqeAGBoibUG5l5EQe+yb3BY+x2l1RcrABKAmTFFNFODWMxVC8YbFQBW89zDMhbzTKWudixcywyA6FjW+WfSukuOwNojBtIXpT5XACSQJaIZGqQloijsBrHMUcyHsR7LWMwztYzFrAswVvTsmGcQRbTDgr3fEVr7vAIgAbaIZmoQ9lwyGsQyR+Z6ZCzNWizXHmHZQ/Z+Y6xoALD7JYLMhaEHwDznKK2zXwGQQJaIZoBtIOzxgOyXZv+Z5mNZi2WOGjCWxojZ+62tuwf7I9MI7HNhjxelPq8VAAmwD51pUgxqEUXIaBDLmMy9RT2MhfpHsNeNsTRnInU1c9TA+AJ/xgBg7c/svbsCIAGLEWiYqUFALaII0nDMALDsP/v7Gu3esI1BW5e934wAyPjVSy8Zvcs85yiYS3leKwCS0DakhtkCgNH0AtsIgTUAmHurPfeMANCcyYwBAPOfJQCA9gw1zN67KwCSYIpopjskABFhTgwyGkQCQLP/7PrauuwAqBt7C6nLDICoFq4cABn/XUGE+rxWACShbUgNszUI5jJzAICRAaA5d3Zdrd7YwcPQAlNPDJi9i3XN3LtDA+Dt27cPz549e3jy5Mnj49WrV++f+TLv3r17ePHixRfX4oHXYowWVwuAmRoEc2HNJ6tBtAHArq8994wA0JzJjAHA7BUG2r3UgP3J0LeXWu/DAkAMXUxfwuD169eP/6558+bNw8cff/z4Og2jA4DRGMJsDVKLKEJWg9wtALR6Y9dl7J/2XVMvtHupgTkWA8ylPP9hAQCjr+/gEQYvX758/68/BddvPdfiSgEwW4MwTSSrQbRGjPp4sEBdzXpwDbOudh9nCwDL9zW9YJ6NVoe9wFzKd4DDAgBmj3cA5R19KxQEXP/hhx+qPv4BowOAKaLZGqQWUYSsBhkVABhLY8RMfQCMp6mLPZkxAFi/dskAa2LtEdaWoW8v9UeAQwOgvqPHxzzPnz9vGjuuLQMDr5/5OwCWiGZsEObnyJkBoDFErWFr0Y6H69gBoBmPpUshOp78WupM+maF5IzvbqR35bfAThMANfV3CDVYFN4pjAoCiIhhkjMHgIgoQlaDWIxYc50WjKWtOyoAmHWjZikBwNASC9YNzoy9K/v9gx998uiLT58+zQ8AmLR8dIMHPurBzywfAbVAgOwFgCxuRACwRDRjg7DmlNkgWmOHeTHfgWgNVjs/LRhLY8QZARDROatPmLB7N0PfXqTnvvfJp4++KDfJGdC+BMY1uBavEeQdQPmzklHGL4iIoiY5YwCwjHsFAC8AtHfiqMsMgKhZRt9BZMDquZl7F+cGMn1yNwDqj3BaJl/S+g6gfgdRMjoAWIc/4x0SKEXkJbNBtEaM+uwA0BgaO3gwlkYn7OC5cgDgzwhn6N1hAQDE9OWjofLjnNY7BISAXFs/VzM6ACRpoyKasUFAKSIvswRAdB0l2vNiB4DWbGYLAO059aS+S/Yya++W2hsaAJmMDgBwFxF5iZrHHto7ccYZlWjPi7F/Jdq9zKrrDXGY/2wBAO7SuysAEmGIaMY7JAARRe8kMxtEMzbrTq9Ea8Sj6pbNzyD6Lo79joQF1hTdJ+1NSG/KPV8BkAij2Wa9Q2I07iwBEP2YrmR0ABwZ8WwBwJ4Piyv3Lvp2BUAHIKKoSTKMNgPMKSpuxhhbaIx4VABIXWYAaI2YXTe6h7MGAKPvztC7KwASYQhg1gZhmDdeHx1jC40Ri2lmBMCeEWcEz1kDgD0fFtB3tHeh7Rl7F3OCr4AVAIkwTBINMruIvGBvok22hSUAjkzTgmbMjADQjCnXsA3XO2bGPrBg9O6sN29lb6wASCQqoqyGZaAx2CMyG0Tmhz3cYlQAyDVM49OYqeYaDxjzagHAuMHx7ks2Ze+uAEgkKqKZG0RjsEfg9VkBgD07ml/ZCCw0Z6aZmweMuWc4WXo6qrtF1j4wiGoja68ZlL27AiCRqIikQWYUUbR5sxtEM7/o+bTQrCu6d1tgzD0jlrrsPfe+k5P5zMiVA6DU3wqARMqk9VAe1GxEBZ7dIJrxYVq4hg3G3DPijOABR3Wz9OQNgKz9Z3CX3l0BkEhUBFlGwQJz2zOcPWRv8GcGpci3gAFFP+dtcbQvWed6ZMRRPW6Bup4v87P2n0F0r+T1syIaXQGQiMaE9pi5QYCIyEOWGZUczW9UAKAurmFzFACZweMJAMZv2mQhvevV9+y9K1pZAZDMHUTkIcuMSo72HgaUsb9H+5J1rhhzz4iz9tz767x43awBAFYAxFgB8DkREc18hwSODGePHg2iCYCM/ZXm2iJr7UdGPKruFt7X9eLoHPfI0hYL2fsVAMlERIRDOoOIPOB1GWZUcrT3WU2Kunv7krX2o/PICgDvPh6dz2iOznEP7EeGtlhgXXisAEgmKqKZG8Tb+KBHgxwZTNYcMObemUf2bQ+Mu1d3tgDAO7SZ9X10jntEXtsDObMVAMlEhDD7HRLW5TWUHg1yFL5ZczgaF8/jwQY198bF81kBYB03+iVrD472c4/Ze1duBlYAJBMR0ewNAhFhjh56NAj2Hfu/xdHzXjDmUV2vJvY40trR817ETCxEf0OuB9gv67qE2d/dwFcwxxUAyXiaA5yhQUREHno0yJHBZ4XQkdEezcvLkdZQNysArDqQXwOeWd+edYEzvLuR3v3md76/AiATr0meoUFkbRC8hV4NcmTEowIgq+6oAPBoXPRt1U5PvL17hps32f8VAMlEA2DmBvHOsVeDHBkx5jDCiDMDYE9r2IuMdx4ejXv7oicyR6u+pS+y9R1BevDr3/juCoBMvCZ5hgYBmCPmaqFXg8Ds9ozYM3cNR0acFQBHmsmua9E45rF3NjPg7V153exgjn/x7KMVAJlI0lrN7gwNArA2bwBYG8vK3h56z0XDkRF79kzDqADwnOfRu7MZEI1Yz+osvYs5/vl3f7oCIBuPiI7uXmfBYypHRsUC89qqc8UAODJiPDdLAGR9H8HGc1ZnCoCv/d3PVwBk4xHRWRoEIrJ+rtyrQfaMuEcAtAwxs64mADKCx7MmaDvj+wg2nhucM7y7AZjjCoAOeER0lgbBHK3z7NUgewHguWvVsjd2ZgDsjS3PZQaAZWxPT4zAc4MDbZ8hALCur370qxUA2XjM/CwN4jHzXg2ydyc+KgDkuVEBkFEXYGxLAFivHwX07QmAs/TuV37ymxUA2XhM8iwNAqFjrhZ6NcieEUs4ZLBntntzYoCxW7qZKQCy58LE07tnuXmTHlgBkAxEBFFoOVODeIy0V4PsmW2PAGgZ4qgAkLpZmrKcafZcmGBNnt5tncFsSA+sAEgGIsJGazljAGgNrWeD7O2jtbGtbK1R9iuLrbpiutpzsuIJgKy5MLGe157mZkPO4Qc/+uT9T7isAHiPVURnahDrXHs2iNRqGeJVA2DLiLM1hbraz8qz94CJzFW7b7LPPfQdRfrje598+v4nXFYAvMfafGdqEIC5tsyuRe8G2ZpbdgBsGXGPui0jztYUPifXBkD2HjCx9q5cfxYw17/9+1++/xeXFQDvkaTVmt6ZGgRgbdYA6MXW3Dxf7lkYFQBbRpxdFzW1AZC992ws+j5b72Kuf/PpP77/F5cVAAUWEZ2tQbbMrkXvBtmaG/Y3OwBahph9thi7VbdHAGjXlb33bK4eAN/+xe/e/4vLCoCCLSNqcbYGwVxbptNiRAC05maZs4et8S1G6WFrfPw8c98t42N+2l6YgSv3Lsx/BUAHtoyoxdkaxGJqvRsEtVr7vvVzFhh7q27m+rfOYuvnLCzBjjvqM+nbohXLtTMA88+6MVgBUABRaIVxtgaxNH/vBtmqZ7mr84CaLcPNXv/WWaBuaz4s8BEJdHuE9fuwGdg6yxbZumKDL4A15+ZhBUABRKQxyTM2iLb5Qe8G2WpezDc7AFrnnb1+jN06i+zguXIAbO1pjaxN+33BDEgAYO5sVgAUaBtEfkvmjAFwJKIRDbIXAJnz2DKN7ADY0hn2oEfdIw2IvjMMJ4utPa0RfZ+pd/HfAGSdxwqAgis3iFb4IxoEplffifeYx5Zp4Gd4LostnWUHj1a3rfOYnSv3LjwyS5MrAAqu3CBAI6IRDSLNW4L6+FnvAJC6Gc0mbO0xfpYZANo9PaO+tWtrnfnsrADoiGajtd8VzIbmDnNE87eaUkyyRwCURqw1kgiytlbdjCYXtDXwUVTrI7nZ0aztjOEGj8T/JHTG90MrACo0JnnWBsGcj0Q0MgBKQ2yZJJtWDflZZgCIEZc1Wj/LADU0AXDUAzNy1d6FR/7Vi5+l3BysAKiAiI5MUiO0Gdn6srVkRIO0jFhCIZOW6bbmkkFtxL0CQKNdzOOM+oZuj3pXc81sZPrkCoAKiONIIHXzngU0Nea+Bxqkd/O3zA9z6PFOpD7LHsED6rq9gufIAHsFUQZY19HNC9Z2tnBbAdCRI5M8c4OIue2ZzKgGQd3SEEcFQK+69Z24BEA2RyYp8zizvrc4a++uAOjIkYikQbLv1DI4mvvIBkHd0og1d3MMaiPuGQDlnfiR7lhgfXt1MI8e689A9nBL36L/FQD/zAqAiiOT7GUQWWBtpdGWHK09k9qIYf69AqA04l7Bgxpl3V66OjLJM+v76AbmrOG2AqAzeybZyyCyqI22ZGTzo25piLVBZlHXwb97nG+to166OjLJXuvPYq93zxpuKwA6U5tRydkbBOvaWlsvE2pRz2svqJjUa8bft/aHSW1GPXW1Z5K99j2LK/buCoDO1GZUguY5c4NgXVhDi5ENgnmVtXvtc23EvQxQPooRsPYtzbHZMsmjdwdnAOva2sezhtsKgM5AJC2TvEKDiPG0PgPGz0c1SGnEPfe5NmL8fevumEldt6c51WErYL977XsW9b4KZ+7dFQCd2TJJaZCzgzXUJje6QcrGHREAqNmzrmhJNIa/9wqALZMsQ/isHPVu/fMzsAJgABBLbZJXaBCANdRrQ4OMXFvZuD2btawlf8efPRCNoXbPulv7O/IjQCayryVn7t0VAAOAWOrPEq/SIK3PSdEgI9cmJog/ezerGMaWMWZR1+0VAEBql+D8sfdn52q9e8oAePXq1cPLly/f/6vNzAFQm2Tvu7RM0PhYS8kMzS+mNCoAetcVo2qdRzY476vqG+sqz1HWdtZwO10AwPyfPHly6gCQppS7wRF3aVnIWmRtszQ/mhZN2vtuTYwYj551pWbv4AF16ODfveeQxdV69zQB8ObNm4cPPvjg4dmzZw8vXrw4dQAAiAZiAlZzmH1tYnrA2vxZaxNDxFx63q3J2eLxtb/7ebdzwxqhMamdTX1upSn2mkMWrbVJ744IWCb12pjQA+Czzz57/DvM/+wBIMYgd8giKA2zr03ukgDWKGGgIWttYoilMfVA9gKPr3/ju93OrazbI/Dqc5Nzn+UdYIR6bWXvwvwtvTsb9dqYpH0HoA0AfFQkC5zt8c3vfP/x/4nn27/43cNXP/pV85qtx9OnT6deGx6420VzYG1Ya+ua1iNrbZgDjAh73no+8yF1e58bauJh2X/vo17bX3//x49n/5c/++0X73zO+midG9aE3h2hJ+YDa8OfGQwNAFAvdsbHD370SfPn63GdB874ruf8vU8+bf78Co+rrC0LdwDA3JG4eOBzf3z8U6INgMVisViMYfg7gMVisViMYQXAYrFY3JQVAIvFYnFT0gJgsVgsFnOzAmCxWCxuygqAxWKxuCkrABaLxeKmDAuAX//611/8dwTf+ta3Hv7whz+8f+Y8vH79+vF/8+iPf/zj+5/8f/bWhr/jZ/I8rp0J+R/yw6P133eUz+N/8+nt27fvn3l4/Dt+Js/j2tnAmW3NH5x9feDdu3ePuqznd+a14RdKZG541No889pKTbbml7m2IQGABZfGCBNsGenMyKHV895bGx74u5i+hAFeMwMQTykwzGvv37ge64Hh1KYjwpxlbQBzKY2jnD84+/oEzLE2gzOvDfPB/GTuNWdeG7QITcp86vllr617ANQmCGCEz58/f/j973///ifzIqaNQ/v4448f1yIBcLQ2HEwZDgDXzvDrsrWYgPwM8249D8FhbRBxLVSAa2f6VeD6V5Mxb8wZf15hfQBzxTowb1nL2deGOaLXsI6as6+t1mRJj7V1D4DWXW/LOGcF8//tb3/7+HfMF/OWADhaW309wLV1KMwChAWBYY7l34VSoHjg72WT4tpaoDNRzvkK68PcYJT4X+SVeYOzrw1z+fDDD7/4mKN8F3fmtbXmXtJjbUMCAAlW3+0jtc4QACWtANhbGx51OuNavGbGACgFBkHJnUcJ1iNirNeGa/Ga0Y1WgyZpGcnZ14d1YY6lSYCzrw3zK00N65SzO/PaZO5i2hJw+Hf5fObaVgAEuHIAQFxXM8gazEvusM6+PswBWsSfVwuAFpgvHmdeG+pDf2W4YW7ou16aXB8BBWgFwBU+AoKwSvMHItZybey3oyNAA+Fx9vVhDTL3VgBc7eww57OfG+pvGXyvta0vgQPUhn60NhxObfa4Foc9C5hLS0Cl8IRSwC3h4dpZ1taaP5BmO/P6MCfMTT5CKB9nX9veueFnV1gb5liCufU6t+4BADDx0ghbd8ZnoDXvvbXVAYFr6ncMI4F4akGV1ILD9VgPhFqLFdfg2lnWBur549/lO52zr09oGceZ14Z51ee29+8zra2eO7QoHwGB7LUNCQAAE5S7lNk+AtGyFVx7axPTl+clDEYj4pF5lQ8RGMDf5eelMEE9Rvm6WSjnX3/MBc6+PlAbg3DmtZVzv9q5wbBlbnjUBp65tmEBsFgsFouxrABYLBaLm7ICYLFYLG7KCoDFYrG4KSsAFovF4qasAFgsFoubsgJgsVgsbsoKgMVisbgpKwAWi8XipqwAWCwWi1vy8PD/APRV+aqkKcfgAAAAAElFTkSuQmCC\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function audioSignal = binaryToBell202(binaryStream, sampleRate, bitRate)\r\n  audioSignal = binaryStream;\r\nend","test_suite":"%%\r\nrng(2718);\r\nr=num2str(randi(2,1,1e6)-1);\r\nr(r==' ')=[];\r\na=sum(round(binaryToBell202(r,1.2e5,1200),4)*10000);\r\na_correct=0;\r\nassert(isequal(a,a_correct))\r\n%%\r\nrng(3141)\r\nr=num2str(randi(2,1,1e6)-1);\r\nr(r==' ')=[];\r\na=sum(round(binaryToBell202(r,1e5,300),4)*10000);\r\na_correct=-195091;\r\nassert(isequal(a,a_correct))\r\n%%\r\nrng(1728);\r\nr='101010111000';\r\na=sum(round(binaryToBell202(r,1e5,1200),3)*10000);\r\na_correct=5850;\r\nassert(isequal(a,a_correct))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":5,"created_by":145982,"edited_by":145982,"edited_at":"2025-03-25T00:29:31.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":"2025-03-25T00:29:31.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2024-09-26T22:45:14.000Z","updated_at":"2026-06-06T01:54:20.000Z","published_at":"2024-09-26T22:45:14.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eConvert a character binary bit-stream transmitted at a certain baud-rate into an audio stream using Audio Frequency Shift Key (AFSK) using the Bell 202 standard at a given sample rate. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFrequency of sine audio wave: '1' = 1200 Hz, '0' = 2200 Hz\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDuration of each bit: based on baud-rate\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDigitized audio stream is produced at the sample rate and is smooth between bits (initially starts at zero phase shift). 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":60746,"title":"List Ormiston prime pairs","description":"The numbers 1913 and 1931 form the first Ormiston prime pair. The numbers are consecutive primes, and they are anagrams of each other—that is, the digits of one number can be arranged to give the second number. 347 and 743 are not an Ormiston prime pair: they are anagrams of each other, but they are not consecutive primes. \r\nWrite a function that produces the nth Ormiston prime pair.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 93px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 46.5px; transform-origin: 407px 46.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe numbers 1913 and 1931 form the first Ormiston prime pair. The numbers are consecutive primes, and they are anagrams of each other—that is, the digits of one number can be arranged to give the second number. 347 and 743 are not an Ormiston prime pair: they are anagrams of each other, but they are not consecutive primes. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function that produces the nth Ormiston prime pair.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = OrmistonPrimePair(n)\r\n  p = primes(n); \r\n  y = isequal(sort(p(n)),sort(p(n+1)));\r\nend","test_suite":"%%\r\nn = 1;\r\ny = OrmistonPrimePair(n);\r\ny_correct = [1913 1931];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 18;\r\ny = OrmistonPrimePair(n);\r\ny_correct = [55313 55331];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 54;\r\ny = OrmistonPrimePair(n);\r\ny_correct = [131413 131431];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 126;\r\ny = OrmistonPrimePair(n);\r\ny_correct = [343337 343373];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 180;\r\ny = OrmistonPrimePair(n);\r\ny_correct = [481513 481531];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 1080;\r\ny = OrmistonPrimePair(n);\r\ny_correct = [2744279 2744297];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 1818;\r\ny = OrmistonPrimePair(n);\r\ny_correct = [4696613 4696631];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 5436;\r\ny = OrmistonPrimePair(n);\r\ny_correct = [14589013 14589031];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 7290;\r\ny = OrmistonPrimePair(n);\r\ny_correct = [19592813 19592831];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 10800;\r\ny = OrmistonPrimePair(n);\r\ny_correct = [29470879 29470897];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 17496;\r\ny = OrmistonPrimePair(n);\r\ny_correct = [48654979 48654997];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 11111;\r\ny = OrmistonPrimePair(n);\r\nsmf_correct = 81182;\r\nsmf = sum([max(factor(y(1)+1)) max(factor(y(2)+1))]);\r\nassert(isequal(smf,smf_correct))","published":true,"deleted":false,"likes_count":0,"comments_count":1,"created_by":46909,"edited_by":46909,"edited_at":"2024-10-09T05:13:23.000Z","deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2024-10-09T05:13:08.000Z","updated_at":"2026-06-06T01:40:24.000Z","published_at":"2024-10-09T05:13:23.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe numbers 1913 and 1931 form the first Ormiston prime pair. The numbers are consecutive primes, and they are anagrams of each other—that is, the digits of one number can be arranged to give the second number. 347 and 743 are not an Ormiston prime pair: they are anagrams of each other, but they are not consecutive primes. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function that produces the nth Ormiston prime pair.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":61292,"title":"The Singularity - Event Horizon (Absolute Zero)","description":"Inspired by Problem61291\r\nMô tả:\r\nBầy đàn gồm M agents phải di chuyển xuyên qua không gian 5D Hyper-Torus ( kích thước mỗi chiều L = 100 ) từ Node 1 đến Target Node. Để sống sót, thuật toán của bạn phải tuân thủ nghiêm ngặt các định luật vật lí sau:\r\n1. Hình học Torus 5D: Khoảng cách giữa 2 điểm là khoảng cách Chebyshev trên mặt Torus:\r\n        d = max(min( | x_i - y_i |,100 - | x_i - y_i | )) cho i = 1..5.\r\n2. Động lực học Pulse: Chi phí năng lượng tại thời điểm cập bến t bị nhân bởi hệ số \r\n        Pulse(t) = 1 + 2 sin^2(pi*t/2). Lưu ý: V_{swarm} = 20.\r\n3. Kẻ săn mồi ( Dynamic Predator ): Predator xuất phát từ pred_start với V_p = 15. Nếu tại bất kì node trung gian nào, Predator có thể đến trước hoặc cùng lúc với bầy đàn ( t_p \u003c= t_s ), bầy đàn sẽ bị tiêu diệt ( Energy = -1 ).\r\n4. Cấm quay đầu ( No-Revisit Rule ): Để tránh việc \"câu giờ\" đợi nhịp Pulse thấp, bầy đàn không được phép đi qua 1 node quá một lần trong cùng 1 hành trình.\r\n5. Hệ số bão hòa \u0026 Thuế Entropy: *Sat  = M + 0.125 x M x ( M - 1 ).\r\n6. Cộng hưởng Spin: Nếu tọa độ thứ 5(ω) của node đến gần sát một số nguyên ( sai số \u003c 0.05 ), một hình phạt Spin = 50 x (step + 1) sẽ được cộng vào chi phí","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 405px; display: block; min-width: 0px; padding-block-start: 0px; padding-inline-start: 2px; padding-left: 2px; padding-top: 0px; perspective-origin: 468.5px 202.5px; transform-origin: 468.5px 202.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 10.5px; text-align: left; transform-origin: 444.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eInspired by \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://ww2.mathworks.cn/matlabcentral/cody/groups/97667/problems/61291\"\u003e\u003cspan style=\"border-block-end-color: rgb(0, 91, 130); border-block-start-color: rgb(0, 91, 130); border-bottom-color: rgb(0, 91, 130); border-inline-end-color: rgb(0, 91, 130); border-inline-start-color: rgb(0, 91, 130); border-left-color: rgb(0, 91, 130); border-right-color: rgb(0, 91, 130); border-top-color: rgb(0, 91, 130); caret-color: rgb(0, 91, 130); color: rgb(0, 91, 130); column-rule-color: rgb(0, 91, 130); outline-color: rgb(0, 91, 130); text-decoration-color: rgb(0, 91, 130); text-emphasis-color: rgb(0, 91, 130); \"\u003e\u003cspan style=\"\"\u003eProblem61291\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 10.5px; text-align: left; transform-origin: 444.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eMô tả:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 21px; text-align: left; transform-origin: 444.5px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eBầy đàn gồm \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eM\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e agents phải di chuyển xuyên qua không gian 5D Hyper-Torus ( kích thước mỗi chiều \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eL\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e = 100 ) từ Node 1 đến Target Node. Để sống sót, thuật toán của bạn phải tuân thủ nghiêm ngặt các định luật vật lí sau:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 10.5px; text-align: left; transform-origin: 444.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e1. \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eHình học Torus 5D\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: Khoảng cách giữa 2 điểm là khoảng cách Chebyshev trên mặt Torus:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 10.5px; text-align: left; transform-origin: 444.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e        d = max(min( | x_i - y_i |,100 - | x_i - y_i | )) cho i = 1..5.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 10.5px; text-align: left; transform-origin: 444.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e2. \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eĐộng lực học Pulse\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: Chi phí năng lượng tại thời điểm cập bến \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003et\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e bị nhân bởi hệ số \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 10.5px; text-align: left; transform-origin: 444.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e        Pulse(t) = 1 + 2 sin^2(pi*t/2). Lưu ý: V_{swarm} = 20.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 21px; text-align: left; transform-origin: 444.5px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e3. \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eKẻ săn mồi ( Dynamic Predator ):\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e Predator xuất phát từ pred_start với V_p = 15. Nếu tại bất kì node trung gian nào, Predator có thể đến trước hoặc cùng lúc với bầy đàn ( t_p \u0026lt;= t_s ), bầy đàn sẽ bị tiêu diệt ( Energy = -1 ).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 21px; text-align: left; transform-origin: 444.5px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e4. \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eCấm quay đầu ( No-Revisit Rule ):\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e Để tránh việc \"câu giờ\" đợi nhịp Pulse thấp, bầy đàn không được phép đi qua 1 node quá một lần trong cùng 1 hành trình.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 10.5px; text-align: left; transform-origin: 444.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e5. \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eHệ số bão hòa \u0026amp; Thuế Entropy\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: *Sat  = \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eM\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e + 0.125 x M x ( M - 1 ).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 21px; text-align: left; transform-origin: 444.5px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e6. \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eCộng hưởng Spin\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: Nếu tọa độ thứ 5(ω) của node đến gần sát một số nguyên ( sai số \u0026lt; 0.05 ), một hình phạt Spin = 50 x (\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003estep\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e + 1) sẽ được cộng vào chi phí\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [max_energy, best_path] = solve_event_horizon_v3(nodes, pred_start, params)\r\n    V_p = 15;%\r\nend","test_suite":"%% PHASE 1: THE PULSE TIMING TRAP\r\nclear n p;\r\np.M = 1; p.target = 2; p.ATP_total = 2000;\r\nn(1).coord = [0,0,0,0,0.5]; n(1).res = 1;\r\nn(2).coord = [30,0,0,0,0.5]; n(2).res = 10; \r\nn(3).coord = [10,0,0,0,0.5]; n(3).res = 0.1;\r\n[e1, s1] = solve_event_horizon_v3(n, 0, p);\r\nassert(~isempty(s1) \u0026\u0026 length(s1{1}) == 3, 'FAILED T1: Phải đi vòng qua Node 3 để khớp t=2.0s.');\r\n\r\n%% PHASE 2: HYPER-TORUS WRAP-AROUND \r\nclear n p; p.M = 1; p.ATP_total = 2000; p.target = 2;\r\nbase.res = 1; base.coord = [0,0,0,0,0.5];\r\n\r\n% Test 2: Chiều 1\r\nn2 = repmat(base, 1, 2); n2(2).coord(1) = 90;\r\n[e2, ~] = solve_event_horizon_v3(n2, 0, p);\r\nassert(abs(e2 - 1980) \u003c 1e-3, 'FAILED T2: Lỗi Torus chiều 1');\r\n\r\n% Test 3: Chiều 5\r\nn3 = repmat(base, 1, 2); n3(2).coord(5) = 95.5; \r\n[e3, ~] = solve_event_horizon_v3(n3, 0, p);\r\nassert(e3 \u003e 1990, 'FAILED T3: Lỗi Torus chiều 5');\r\n\r\n%% PHASE 4: PREDATOR INTERCEPT \r\nclear n p; p.M = 1; p.ATP_total = 5000; p.target = 2;\r\nn(1).coord = [0,0,0,0,0.5]; n(1).res = 1;\r\nn(2).coord = [40,0,0,0,0.5]; n(2).res = 1; \r\n\r\nn(3).coord = [0,20,0,0,0.5]; n(3).res = 0.1; \r\n\r\nn(4).coord = [0,60,0,0,0.5]; \r\n\r\n[e7, s7] = solve_event_horizon_v3(n, 4, p);\r\n\r\nassert(~isempty(s7) \u0026\u0026 e7 \u003e 0, 'FAILED T7: Vẫn không tìm thấy đường an toàn');\r\n\r\n%% TEST 20: THE OMEGA LABYRINTH (Bản Chuẩn Hóa 100%)\r\nclear n p;\r\n% 1. Cấu hình cố định để Solver và Testcase đồng bộ\r\np.M = 10; \r\np.target = 5; \r\np.ATP_total = 10000;\r\nV_s = 20; \r\nV_p = 15; % Hằng số Predator\r\n\r\n% 2. Tạo 5 Nodes với tọa độ NẰM TRONG KHOẢNG [0, 100]\r\n% Node 1: Xuất phát\r\nn(1).coord = [10, 10, 10, 10, 0.5]; n(1).res = 1;\r\n% Node 2: Predator (Kẻ săn mồi đứng đây)\r\nn(2).coord = [30, 10, 10, 10, 0.5]; n(2).res = 1;\r\n% Node 3: Node trung gian (Đường vòng an toàn)\r\nn(3).coord = [10, 50, 10, 10, 0.5]; n(3).res = 0.5;\r\n% Node 4: Node rác\r\nn(4).coord = [80, 80, 80, 80, 0.5]; n(4).res = 1;\r\n% Node 5: Đích\r\nn(5).coord = [50, 50, 10, 10, 0.5]; n(5).res = 0.5;\r\n\r\n% 3. Chạy Solver\r\n[user_e, user_path] = solve_event_horizon_v3(n, 2, p);\r\n\r\n% 4. CHẶN LỖI CRASH (Nếu Solver trả về rỗng)\r\nassert(~isempty(user_path) \u0026\u0026 ~isempty(user_path{1}), 'Solver không tìm thấy bất kỳ con đường nào!');\r\n\r\n% 5. KIỂM TRA LOGIC SINH TỒN\r\npath = user_path{1};\r\nt_run = 0; is_captured = false;\r\nSat = p.M + 0.125 * p.M * (p.M - 1);\r\ne_val = p.ATP_total;\r\n\r\nfor k = 1:length(path)-1\r\n    u = path(k); v = path(k+1);\r\n    df = abs(n(u).coord - n(v).coord);\r\n    % Chuẩn hóa khoảng cách Torus chuẩn 100%\r\n    d = max(min(df, 100 - df));\r\n    \r\n    t_run = t_run + d/V_s;\r\n    \r\n    % Kiểm tra Predator (Node 2) có bắt được tại Node v không\r\n    df_p = abs(n(2).coord - n(v).coord);\r\n    dp = max(min(df_p, 100 - df_p));\r\n    \r\n    if dp/V_p \u003c= t_run + 1e-9\r\n        is_captured = true; break;\r\n    end\r\n    \r\n    % Tính năng lượng để đối chiếu\r\n    pulse = 1 + 2 * (sin(t_run * pi / 2)^2);\r\n    cost = (d * n(v).res * Sat * pulse) * (1.15^(k-1));\r\n    if abs(n(v).coord(5) - round(n(v).coord(5))) \u003c 0.05\r\n        cost = cost + 50 * k;\r\n    end\r\n    e_val = e_val - cost;\r\nend\r\n\r\n% ASSERTION CUỐI CÙNG\r\nassert(~is_captured, 'Omega Phase: Swarm captured! Code của bạn đang phớt lờ Predator.');\r\nassert(abs(user_e - e_val) \u003c 1e-5, 'Omega Phase: Năng lượng tính toán không khớp!');\r\n%% PHASE 5: SPIN SENSITIVITY \r\nclear n p; p.M = 20; p.target = 2; p.ATP_total = 10000;\r\nn(1).coord = [0,0,0,0,0.5]; n(2).coord = [10,0,0,0,1.02]; n(2).res = 1; \r\n[e12, ~] = solve_event_horizon_v3(n, 0, p);\r\nassert(abs(e12 - 8600) \u003c 1e-4, 'FAILED T12: Spin Penalty không kích hoạt đúng ngưỡng.');\r\n\r\nn(2).coord(5) = 1.06;\r\n[e13, ~] = solve_event_horizon_v3(n, 0, p);\r\nassert(abs(e13 - 8650) \u003c 1e-4, 'FAILED T13: Spin Penalty kích hoạt sai mục tiêu.');","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":5076265,"edited_by":5076265,"edited_at":"2026-03-23T17:47:36.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":"2026-03-23T17:47:36.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2026-03-22T19:51:08.000Z","updated_at":"2026-06-06T20:25:40.000Z","published_at":"2026-03-22T19:51:08.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInspired by \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://ww2.mathworks.cn/matlabcentral/cody/groups/97667/problems/61291\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem61291\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eMô tả:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBầy đàn gồm \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eM\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e agents phải di chuyển xuyên qua không gian 5D Hyper-Torus ( kích thước mỗi chiều \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eL\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e = 100 ) từ Node 1 đến Target Node. Để sống sót, thuật toán của bạn phải tuân thủ nghiêm ngặt các định luật vật lí sau:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e1. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eHình học Torus 5D\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: Khoảng cách giữa 2 điểm là khoảng cách Chebyshev trên mặt Torus:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e        d = max(min( | x_i - y_i |,100 - | x_i - y_i | )) cho i = 1..5.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e2. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eĐộng lực học Pulse\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: Chi phí năng lượng tại thời điểm cập bến \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003et\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e bị nhân bởi hệ số \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e        Pulse(t) = 1 + 2 sin^2(pi*t/2). Lưu ý: V_{swarm} = 20.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e3. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eKẻ săn mồi ( Dynamic Predator ):\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Predator xuất phát từ pred_start với V_p = 15. Nếu tại bất kì node trung gian nào, Predator có thể đến trước hoặc cùng lúc với bầy đàn ( t_p \u0026lt;= t_s ), bầy đàn sẽ bị tiêu diệt ( Energy = -1 ).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e4. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eCấm quay đầu ( No-Revisit Rule ):\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Để tránh việc \\\"câu giờ\\\" đợi nhịp Pulse thấp, bầy đàn không được phép đi qua 1 node quá một lần trong cùng 1 hành trình.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e5. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eHệ số bão hòa \u0026amp; Thuế Entropy\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: *Sat  = \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eM\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e + 0.125 x M x ( M - 1 ).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e6. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eCộng hưởng Spin\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: Nếu tọa độ thứ 5(ω) của node đến gần sát một số nguyên ( sai số \u0026lt; 0.05 ), một hình phạt Spin = 50 x (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003estep\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e + 1) sẽ được cộng vào chi phí\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":60989,"title":"Which YAxis does this Graphic object belong to?","description":"Your function gets a graphic object in an Axes that has 2 YAxis. Can you determine which YAxis does this object belong to?\r\nI provided my reference answer to prove the solvability of the problem, but Size 159 is definitely not optimal.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 51px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 408px 25.5px; transform-origin: 408px 25.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYour function gets a graphic object in an Axes that has 2 YAxis. Can you determine which YAxis does this object belong to?\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI provided my reference answer to prove the solvability of the problem, but Size 159 is definitely not optimal.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function YAxis=GetYAxis(GObject)\r\nYAxis=GObject.Parent.YAxis\r\nend","test_suite":"%%\r\nNumTrials=100;\r\nQA=gobjects(2,NumTrials);\r\nDirections=[\"left\",\"right\"];\r\nAx=gca;\r\nfor A=1:NumTrials\r\n    Random=randi([1,2],1);\r\n    yyaxis(Directions(Random));\r\n    QA(1,A)=scatter(A,A);\r\n    hold on;\r\n    QA(2,A)=Ax.YAxis(Random);\r\nend\r\nassert(~contains(fileread('GetYAxis.m'),'evalin'),'evalin is not allowed');\r\nfor A=1:NumTrials\r\n    assert(isequal(GetYAxis(QA(1,A)),QA(2,A)),sprintf('Incorrect YAxis for Scatter %u',A));\r\nend","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":362068,"edited_by":362068,"edited_at":"2025-08-01T10:41:54.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":"2025-08-01T10:37:08.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2025-08-01T04:35:44.000Z","updated_at":"2026-06-06T06:04:34.000Z","published_at":"2025-08-01T04:35:44.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour function gets a graphic object in an Axes that has 2 YAxis. Can you determine which YAxis does this object belong to?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI provided my reference answer to prove the solvability of the problem, but Size 159 is definitely not optimal.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":61046,"title":"Decipher Message Using Prime of Primitive Root of Two Sequence Key","description":"Provided a message with all characters \u003c= 255 (ascii) as an input, decode the padded message (removing the random character pad), XORing input converted to binary vector (8 bits each) with the inputted prime having primitive root of two as the key. The key is the repeated sequence of 2^x modulo prime key input starting from x=0 until the cycle repeats (unpadded zero binary represented vector of the repeated sequence). After XORing, remove the padded front and return the character array as output.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 105px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 408px 52.5px; transform-origin: 408px 52.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 52.5px; text-align: left; transform-origin: 385px 52.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eProvided a message with all characters \u0026lt;= 255 (ascii) as an input, decode the padded message (removing the random character pad), XORing input converted to binary vector (8 bits each) with the inputted prime having primitive root of two as the key. The key is the repeated sequence of 2^x modulo prime key input starting from x=0 until the cycle repeats (unpadded zero binary represented vector of the repeated sequence). After XORing, remove the padded front and return the character array as output.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function d = deCipher(x,key)\r\n  d=xor(x,key);\r\nend\r\n","test_suite":"%%\r\nx=[39   212    74    83   164    14     3   175    39   212    74    83   164    14     3   175    39   212    74    83   164   14     3   175    39   212    74    83   164    14     3   175    39   212    74    83   164    14     3   175    39   212   74    83   164    14     3   175    39   212    74    83   164    14     3   175    39   212    74    83   164    14     3   19   242    77   208   208    52   219   130    53   242    82   200   207    60   218];\r\nassert(isequal(deCipher(x,19),'I love to swim!'));\r\n%%\r\nx=double('şl‘……‘ŒŒœŚøùè©ădurŢŠª«Ădõ³f ÈijĨĻ™isœ´Ŕ~ê‚ĪŢŞjhĻľŒĒďċĈÓ»ŃÃÜĮîĜŐËīōï´ÖŜąđo‹tŚļŸ‰Ķ¦ĺąˆ®ŝšŊň˜Ç´ļōďƅł¶ÕľŐĔşºśŔ“dk’ĢŠŏĽžá³ÐqfʯĬŕĔăĢċĠŋÆÄĮōĈėĠŽĠ')-100;\r\nassert(isequal(deCipher(x,797),'abcdefghijklmnopqrstuvwxyz !@#$%^\u0026*()_+ 0123456789 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ĺçí°ŒŘčhÔùĺ‚Įąŝĝþ·’Ěĕët§úÓŠ®–vŅņȗŘÞŊЪĮâўèČŢfŕexîÌęĖ‡ë¯zĨàÇÙŠĸŝŘüÓóč©áÓĆĚļęsŒÅuñōď ēŕþøáIJīˆwđĪĈ×ñŔìÄïÌû‚ĻãđéĂþúsÞ©ìĀ×¼ĻĒêiĆàí»ġĩęŠāçökƒöœÀ©y¹šřğƒ’ÿþòt¯ŕêËÊ~ÔìÈð×ŕÚ¿ĭń¬ĩĜĆĒoāý³ĔĒŠĺÂijĈîĭךòËĪµŕÌ¥øŘĆÛţć±ï‘ĔōûŊ¦şĊ—‚ðőù¾ĞûŚ›ľņ£†²èĀĦ„¡ŒřʼnĔäj‚žŘĜÌĥsą¥ºÐľļ¤ÁµšăŚžÉĵû¼ég~ÆŔŞêŏŖæÉ{ً×u¸ŊeĶeęŎķőÑôôşŁŸŇŊéƐĘËœëĩÀţň¬ÆŎįî™għmĞ–Ãėjš¦ðćpĻ…œ»­ĦĐøkąÂċĶ·ğijęþľŠÆå ĹúÀÊĎÍnöĺ¦ĤnďdeîĂægěîrđĂƔ~Ĩtå®IJĕ“Ş™ĴšĥêºĖ½wŅ¶Øœ®æàřÐĺu‹ĬĤ®ĥ†ň³śķŚæüĖ±Ï”àŒôĤŔħġfñĬ•†ġħĚÎáíĘĥŒĘĥ‘ĕ²ĴĘŀfµi€ÓìÒÍdŢŜĦéiåêōĿĝŊĜ¯čģ¾„Šî´đľÇċĥ҈ôĪăŀğăÌÿ°ĥĻŀėë½¾ˆĦķ|s…ÎŝĨĦ«‘–‘–ºıw®íķ˜ÙÔìqԜŖµ°ÆŠģ~Ě¡ïśqīĸdòÛŕ€ãĵţʧĜĖ«ġ»ßÒ¢ŎĶ„ŠkĒÝĪĆŕºĴëĀđ·Đ©ĠoЁŽĴ¶ÄÙċģČėù³‚ØÎŌßĸl±¼ĜėŎŀóí¯ŸmĺĪĥÜĢŔuāyå‹įĆ¾ă؊ėŒ–êďĔķiĭő¢õŔîæÿŠúؤôĴŠ§wĥ©Ţ¶ĕijáoˆč ÔäĖĐÔnrħâf‰év¼ĀÄâĪĤőċäĉ¿ēď¼ļĶhÍÁø¨ÝĨÑŘéùģzįňϚ¤Ň¯™ižĞŠ€İź ŝğœ©yîĂÒąŏ¸ç¥Çijtīı¥tĂŏĥ™ĬČ~ÍĴêĬðŋĕāêiēÛıŘĎŃĭĐÈĞ“Ě†ķÑùĜĤ“ÇĢßĴÔĶğŢĿř¡öıśĮÀÖġ•|ėħn‹´º¨ÍĦ³ĜŁúþƒx‰ÌĀ¸¦ł’ĭ¢”±Ĺdñ›ŀ€ĩŚ¥ħ¬ŠxĨĉîíàèÊıÓĵĢ¡“ˆřkœŃÔĚövĝ´ģĚŎÊĈěëć|đyÒ³èäĝÐ{¸şÁīšø‰ŇÆĝÿőģ쵇‰ïšĈņęięğÀĒlŒØÎʼnŊÒ܌Į¯½æĢÍkĔšÉéĒ›³ĊċjòøkwÿóðōĄĆ÷~˜‚«ĺµ›hģŅĈ”ê̆ĖėŕĢĹązæômüðğŘf¬ĐŎçŌž—šIJďĘāĉ‘}õ—ýęùƒóÍËğìøîхêۓ±žğčó‘‹Ćxýi¨ŊřħĔÛĨϗĿĊ£ĜśĤð‰ž«ãÐĘĴİËįlƒĮoĘĸĸðŌâÖēëÉņĺžÀŽj¨˜Ęľń’ĸĔĐýº²ʼnŚ¾ąfŊŜèģnº‹ČŅēņăĜŠ‹§ĹĪ×ļìōeñº­ýÑÞőğĚ{Ħ©ƒīħĔĠt‘’Ė÷œĝįȏ¬üęČÙjōêàÀÊøŋæ÷îrĮ‚xoiĜįõÒöٙ¬îr“rĠňŖ©‡lĽ¹ìĵ¡āĿ®{sw‰ŠŒČÃŢĽÓŌŐçl¶ÿ¾ÀĻ„dōčŎĚéŜœoijħqÏj·ĵÉu…qðŕ•çĎŚçş­ìŽ‹Î¬ŀĒŘŖIJÖýò¬áŜąĮ̙ĚŊ£řĄãĽ£•Š¦§ğÑĶĢ¢ČçàdãÒøÛÉĨ‹ĩè¢ĂŔċþØϔŗğŎĻƒķñd­ŀĺ|Ęċàäü¦¦Đ鶾óÉØïŅòŒºĮýŁvĢĜč𱁸áŐØĥªğpüÁ÷ďÞśĎÍÔêÁĖ“ˆxöÐëoĝõĸĈŸĈĚÈŎŁþìţñ’ĸņńĺïēĘº~ġ˪Ģēçžħª×ĻgçĈ¶ĈÜóæàoöŔď™ŗķØĭģĕšĨjûÔïŞyt ĥĦ³ĪĮıØĽåˆ~­šðjśÂ†õŖIJ¡”ā×ĵïĄÁî}ėČěp¾Ąöŏ¡“Îō~ÍćpŚý‰‰œŒüÍóÙă·­ĺûĠŔŃĨĆªđÑèōĨÌį‹ŏ¹ªŘóĭ•´zýŐò½ßäĊØ­ëšxèŚľĀî“{ĤŘÈfj¡Ÿ’Ã÷ëŏ¥ĵµĴŖÇĩĺÎĿéŏíĜŝŌý̼Ŝ–Îĺŏ¹†Ńţç|mÉ¡ŐÀıĈŕæŔƒÕė¿¬ęĨÂijåĠĞÇxÂ~è´ôdȃ¸ďĹÆÓùĤÀķöʼnqřĐĝòķŠyúā­ÁƒãĽÿÌ}ʼnŠp–đÀčéĤÏőüĽrŕņĨŀ|ĎĚv„£Ñl©‚üôƅÕŀōœijnð¾âˆńíyćĆėĈ{çÎØģñ¼ĪŘjüĚmƒĕøŖ÷ºo–ŢÁ·›ş’ÜÔ´ÏŊŗ{Ĵč؊„Šʼnõĉłˆ¸ĤʦæžâÛçĭ¨şð•Ŏ¸İġƒċÙûøĀêœfžĘįļé­§ŏģh‘‡š¾ŏŁ}įę˜¿ÑĚïİåû|æĄėÆÛĪµ®Ðœ·™ĭŊý’Ězÿpueļq}Çg¡Ä„îĭʼn©ĵĴ›hŠ¹ÒĴĭÒtÊļtĿĄń±őĔĶíäĥëĊ}ćpÃè“ñ¾|ğİ䱯iôÍá¤ùhù“යōŚÍ•ċŗţÌtÄŌmxIJĿóí‡ŜåĿoäİŃ·»ĖØØĜęµïÒĄļÃİĸä“ü×ÿôîŐñĔpĈāpĬõ¥°ţ§ŝŠŃħéĜĮ™¤Į¨l‘ŞÐ}ĦĜ÷ĀğŠĨ»Ìóâwèðûġÿރıŀ­ÞŘĊĢ̋ˆÊķĥģêyŞƒŇ±Ò|¡ĹĊ—èŚĂxĸĤyĐyŠµōň१ìřçĀÛÏĬ½¸tě¶ŜĜÊôĬ™×†ëtêÚjĐèŋ}…œxŀÇđsĕlŏõăĴkðįĦ‘žĶºÅ×òIJxʑĖōİݔĖòê Ŕ’¿ø¶ğđ²ġÑĩù™İòõąù½ÀĩĠń¯Õţrľë€ÚɲÐúţ™ŝØĽú‡ö¸ŒŢ¢©ăńøĹĈŌŊĺöåţİÃĈezĶłÄņ€³žÑ…ÍėĭuŽä¾×m‘¼įÿţuf´pĝĶ«¡şñÚĥÓÞÖř÷ŊĤ¿n°¥ŐãæeņĎŒkl÷ïמuōĸīĴ¸™o·¸oŽÆōø©¬m­ń²þù§ĎÞ¥¤z~ŃðçË—ĹĐēŁ“ŜœĀð«rĹÃÃäđÅĻèôģńçĻŗĉzŽĴzĵôĺĸĂďĻgđ°¯žÛ¿Ħxwò½ĈţÅÿfĚ‘Í©„ĞꕟŒŠ‘Ð§Ń±ŌÇĶŋđ§¾ÿ§ī£Ñą¼£ĎĂäŎ¡‡ÅÆÿ¶e©˜°Ŗëåļ…ÝĎĶćĕïÖÚ»šň©…ÙĐЙŜĘó×oIJĢ{oćĪá ñĊ”ĈÇäÌşăÉüdsŋÆāgœÄ ŒĿ­ÝÊl|ÇêřÔĥeŘŢÕĀÕüŢ÷řńģá¬ēãŏěžÜ›žÏúĺ–÷Ô캊Ÿ±¢ņþėò¢ċŃřûœ§ÞÒţĹŋĻx£ħğł÷s¢eþĺňäð‘°ĥ‡ýÈĔñōŽşĮ弳ĹĬÚø‰«ĺvęėéªnĒĵíþŒĢöiĭŞ~đ°Ļ÷ÇrĔĕijªķŔìŢģõÔÂŝÖ¢İÄjÊĢăØĶŠġĐāň}äĜÇÞxŢş÷œ«ëÉIJ‹îĠŇ҃ý§áĈؙđļàjߥā’čŌØŖľ´ģļâđÇnĬıģŢōÖĒčõžĈŠyđ°}ĘýÛĠ¡oţ”Ś•Ëĭ©ñ¤Ş”Ÿ°ľ')-100;\r\ny='abcdefghijklmnopqrstuvwxyz !@#$%^\u0026*()_+ 0123456789 ABCDEFGHIJKLMNOPQRSTUVWXYZ';\r\ny=repmat(y,1,100);\r\nrng(2718);\r\ny=y(randperm(length(y)));\r\nassert(isequal(deCipher(x,16067),y));\r\n%%\r\nx=double('“ŀŝřřŝŠšŁţšőšĖþ¸xĤŒ‘ÇãŖ±äÿĨÚİIJŐÉÝz¦vgy•üňłŃijŐÑî‡kv¾äŅ߶Ħu…ðjĺ½igÄÀŞŜřÞÁ£ŋùdfä©kŇðoꊠ¸ÓĤ«ċ•jâ¯ċpg·Đ¦Õŀ}qĻĻg«ŸlÉĉŞÒġ•ÔÆķ¯»™èÁĜŀ¿ĻęÎ{möġæĽ½ÐĤ°ÉÈĔØœđÀġh‡āĠřu˂ċĜÊĂÕĨęâş×ř¼‹­É¯µĦϦøÓĨăøÌ|¿×ÎÔåá×´Òœİfý–™kĬµijş©×Ţç|īdºĩ€¤ıŖÇýqʂèívĜŔij¼˜ĎĂŝÐřšļnzČmŊãÂĺŅŒûįçıċkõĨ÷ĆÒףĦd…‹ċĮàÿĈĹňÙŐIJÒĊ탂ú›ĭðā‹ôȵ')-100;\r\ny='Men go abroad to wonder at the heights of mountains, at the huge waves of the sea, at the long courses of the rivers, at the vast compass of the ocean, at the circular motions of the stars, and they pass by themselves without wondering.';\r\nassert(isequal(deCipher(x,16067),y));\r\n%%\r\nx=double('˜īņŒŒņ³ÓdÓÅℐjīĭÃÚŔŘśÏĻ|f}Ŏu´ÇŕōæĵòöĬĝoÑ®²vÝř»ěíijĥvÍŌæ󼍑ò¶ĬqipˆÄwŜšğ’¥Ôŗ÷ovĀ{·Ň÷²ŅŒÎéªw‚ŗš²Øqśxr®ď¹”ĩiuóöe«ÖwÇńēÌĠ£ÝÆıÂeœúrśļ‚Īė“«lüĎįā|Ðì½Ý…ŜáĄď¾Œ«žåąŎyÄ¿ĐޖæàåŚÇŠÅŝ¡n½iñĔnê‹Ŀāó µ° ÔÅ÷’ËfÄŋüwùݙ£ħ¹ľęž‘ņĻ¤óu‚ħ‚òʼnÞðÖîí¿ĜŜĀl˜ğĿţ½ĚœĦ¯iĒµĒÕ¦ĿğţŃľöìśzûĽľėã¢İĨuПĈêÏłĘĀŋÙņħÓŗò´­ùšħòij“ñœowĽāÜĥĐÏā½ĸĈđ®Ńšô{‘řăğğôĶŇñeíp‡ģĭą—«°')-100;\r\ny='No man can be judged a criminal until he is found guilty; nor can society take from him the public protection until it has been proved that he has violated the conditions on which it was granted. What right, then, but that of power, can authorize the punishment of a citizen so long as there remains any doubt of his guilt?';\r\nassert(isequal(deCipher(x,16067),y));\r\n%%\r\nx=double('ÆõĘĜĜĘĕĔôĖĔĄĔţĻ}½ñćĒ–ģdıĺíŸå籌˜¿sò¼àĹč÷öæą„īÒ®Ã{ıКºŜ»ŽĦ­Ń·ijˆá¥ğţĝÅ¥ÞŇĺ²kï¼·Ŏā½Ņ…uÍĺy¾uĊšjʬċrn¶ċ¼”Ľ|uôĻuÀŸtÆĒġŠčœÕÆĻ©e†ö~ĉŃsĩČÇ¿gĻčłúºœø´Ì…ŗØœĔÂŝ«ŒëąŘ¸ÄºčĎÊāàåŕÇĖÏţÂØz„³iúĚ³ĵ„ŀÿīÏ´µãӄìÓ×´Òŝí|ø—™rĨªòń~á')-100;\r\ny='When one door closes, another opens; but we often look so long and so regretfully upon the closed door that we do not see the one which has opened for us.';\r\nassert(isequal(deCipher(x,16067),y));\r\n%%\r\nx=double('Ŋy”  ”™˜xš˜ˆ˜ß·āŁm‹ňĎĚŸè­¶qģik‰ĐĔŃïĿĮŀŜµ‘{zj‰Ĉ§ŎIJĿ÷­ŒĖĶÿoÌÔĺÕĽ»µ¦†ÅwŘŕĠÄÁÎŇľkyĩ§³ŋļ²Ą¡ ¸Ôݾv·ĈŽ³Ö©ċvg¯ċzÄĴ|rûö¦¸ØÀÏĖęÏŌ‰ØŸı¨ªÔçyĚIJ¿Ļęβ¾÷ėĤł°œðµÊÈĔÏœĒÂōs‰êčĊd߂ğģ£çÛĪęÜŞÈňn„q„n¦ñČ³ç…ŃÿíÌ|¯ØÌÙð΄zÒşòƒùÛà€éxŝšp¡Îŗİ}óf}êuÁĽō“ð{‹kįóxĚŞô¼£ćĴĎeĔŚŀrkēiĄÐ¹ĻĉřöòòæśyûĩĩčŚíŀd¢‘ņĮÉôĔĿ')-100;\r\ny='What is ominous is the ease with which some people go from saying that they don''t like something to saying that the government should forbid it. When you go down that road, don''t expect freedom to survive very long.';\r\nassert(isequal(deCipher(x,16067),y));\r\n%%\r\nx=[{'ī˜Ďė¨ŒŇ³ī˜Ďė¨ŒŇ³ī˜Ďė¨ŒŇ³ī˜Ďė¨ŒŇ³ī˜Ďė¨ŒŇ³ī˜Ďė¨ŒŇ³ī˜Ďė¨ŒŇ³ī˜Ďė¨Ğ'} ...\r\n    {'pţÅÜw'}    {'âñwnš»¾Śâñwnš»¾Śâñwnš»¾Śâñwnš»¾Śâñwnš»¾Śâñwnš»¾Śâñwnš»¾ŚâñwnšĜ'}...\r\n    {'Ł’ĔčÂŘŝ¹Ł’ĔčÂŘŝ¹Ł’ĔčÂŘŝ¹Ł’Ĕņ'}    {' ijµ¬ģy|Ę ijµ¬ģy|Ę ijµ¬ģy|Ę ijµ¬ģy|Ę ijµ¬ģy|Ę ijµ¬ģy|Ę ijµ¬ģy|Ę ijµ¬ģyė'} ...\r\n    {'Ƶijĺ…ïêŽĆµijĺ…ïêŽĆµijń'}    {'‰ĺ¬µĊpeđ‰ĺ¬µĊpeđ‰ĺ¬µĊpeđ‰ĺ¬µĊpeđ‰ĺ¬µĊpeđ‰ĺ¬µĊpeđ‰ĺ¬µz'}  ...\r\n    {'âñwnš»¾Śâñwnš»¾Śâñwnš»¾Śâñwnš»¾Śâñwnš»¾Śâñś'}    {'²ġ‡œ'}    {'oŜÊã솓çoŜÊã솓çoŜÊã솓çoŜÊã솓çoŜÊã솓çoŜÊã솓çoŜÊãìĕ'} ...\r\n    {'üÏřŐ~'}  {'Øë}dśÁ´ŠĂ'}    {'ÿÌŚœ|ĖģwÿÌŚœ|ĖģwÿÌŚœ|Ě'}    {'îÝŋŢmćĒfîÝŋŢmćĒfĀ'}   ...\r\n    {'Ĉ»ĭĴ‹ñäĈ»ĭĴ‹ñäĈ»ĭĴ‹ñäĈ»ĭĴ‹ñäĈ»ĭĴ‹ñäĈ»ĭĴ‹ñäy'}    {'ĿŒĚē¼Ŗţ·ĿŒĚē¼Ŗţn'} ...\r\n    {'Øë}dśÁ´ŠØë}dśÁ´ŠØë}dśÁ´ŠØë}dśÁ´ŠØë}dśÁ´ŠØë}dśÁ´ŠØë}ś'}    {'ßìzsŜ¶ÃŗßìzsŜ¶ÃŗßìzsŜ¶ÃŗßìzsŜ¶ÃŗßìzŘ'} ...\r\n    {'ªę–ĩÓÆIJªę–ĩÓÆIJªę–ĩÓÆIJªę–ĩÓÆIJªę–ĩÓÆIJªÖ'}    {'ņuóúÅē'}    {'ÝîxqŞ´ÁŕÝîxqŞ´ÁŕÝîxqŞ´ÁŕÝîxqŞ´ÁŕÝîxŗ'} ...\r\n    {'dŗÑØ獈ìdŗÑØ獈ìdŗÑØ獈ìdŗÑØ獈ìdŗÑØ獈ìdŗÑØ獈ìdŗÑØ獈ìdŗÑØ獈p'}    {'vŅãÊõŸšþvŅãÊõŸšþvŅãÊõŸšþvŅãÊõŸšþvŅãÊõŸšþvŅãÊõŸšþvŅãÊõŸšþvŅãÊj'}  ...\r\n    {'xŋÝÄû¡”ĀxŋÝÄû¡”ĀxŋÝÄû¡”ĀxŋÝÄû¡”ĀxŋÝÄû¡”ĀxŋÝÄû¡”ĀxŋÝŢ'}    {'ñâńŝrĈčiñâńŝrĈē'}    {'ńwñøÇĭĨÌńwñøÇĭĨÌńwñøÇĭĨÌńwñøÇĭĨÌńwñøÇĭĨÌńwñøÇĭĨÌńwñøÇĭĨÌńwñøÇĭĐ'}];\r\ny='ABCDEFGHIJKLMNOPQRSTUVWXYZ';\r\nfor k=1:26\r\n  assert(isequal(deCipher(double(x{k})-100,19),y(k)));\r\nend\r\n%%\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":145982,"edited_by":145982,"edited_at":"2025-10-23T11:07:00.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":"2025-10-23T01:21:29.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2025-10-22T22:43:16.000Z","updated_at":"2026-06-06T06:03:32.000Z","published_at":"2025-10-23T01:21:29.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eProvided a message with all characters \u0026lt;= 255 (ascii) as an input, decode the padded message (removing the random character pad), XORing input converted to binary vector (8 bits each) with the inputted prime having primitive root of two as the key. The key is the repeated sequence of 2^x modulo prime key input starting from x=0 until the cycle repeats (unpadded zero binary represented vector of the repeated sequence). After XORing, remove the padded front and return the character array as output.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":53064,"title":"Amazing circle of numbers 1 to n","description":"For given natural number n, create amazing circle of numbers 1 to n without a repeat.\r\nThis circle is that the sum of any two adjacent numbers is a perfect square.\r\nFor example, if n = 32,\r\n\r\nSo, output is\r\n                          [1 8 28 21 4 32 17 19 30 6 3 13 12 24 25 11 5 31 18 7 29 20 16 9 27 22 14 2 23 26 10 15]\r\nIf the condition is satisfied, it is the correct answer regardless of the order of the vectors.\r\nIf there is no amazing circle vector, return empty vector [].","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 984px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 492px; transform-origin: 407px 492px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eFor given natural number n, create amazing circle of numbers 1 to n without a repeat.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThis circle is that the sum of any two adjacent numbers is a perfect square.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eFor example, if n = 32,\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 774px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 387px; text-align: left; transform-origin: 384px 387px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" width=\"768\" height=\"768\" style=\"vertical-align: baseline;width: 768px;height: 768px\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eSo, output is\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e                          [1 8 28 21 4 32 17 19 30 6 3 13 12 24 25 11 5 31 18 7 29 20 16 9 27 22 14 2 23 26 10 15]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eIf the condition is satisfied, it is the correct answer regardless of the order of the vectors.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eIf there is no amazing circle vector, return empty vector [].\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function v = amazing_circle(n)\r\n  v = n;\r\nend","test_suite":"%%\r\nn=32;\r\nv = amazing_circle(n);\r\nv = v(1:n);\r\nflag = false;\r\nif length(unique(v)) == n \u0026 (min(v) == 1) \u0026 (max(v) == n)\r\n    for i = 1:n-1\r\n        lst(i) = mod(sqrt(v(i)+v(i+1)), 1) == 0;\r\n    end\r\n    lst(n) = mod(sqrt(v(n)+v(1)), 1) == 0;\r\n    if sum(lst) == n\r\n        flag = true;\r\n    end\r\nend\r\nassert(isequal(flag,true))\r\n\r\n%%\r\nn=33;\r\nv = amazing_circle(n);\r\nv = v(1:n);\r\nflag = false;\r\nif length(unique(v)) == n \u0026 (min(v) == 1) \u0026 (max(v) == n)\r\n    for i = 1:n-1\r\n        lst(i) = mod(sqrt(v(i)+v(i+1)), 1) == 0;\r\n    end\r\n    lst(n) = mod(sqrt(v(n)+v(1)), 1) == 0;\r\n    if sum(lst) == n\r\n        flag = true;\r\n    end\r\nend\r\nassert(isequal(flag,true))\r\n\r\n%%\r\nn=34;\r\nv = amazing_circle(n);\r\nv = v(1:n);\r\nflag = false;\r\nif length(unique(v)) == n \u0026 (min(v) == 1) \u0026 (max(v) == n)\r\n    for i = 1:n-1\r\n        lst(i) = mod(sqrt(v(i)+v(i+1)), 1) == 0;\r\n    end\r\n    lst(n) = mod(sqrt(v(n)+v(1)), 1) == 0;\r\n    if sum(lst) == n\r\n        flag = true;\r\n    end\r\nend\r\nassert(isequal(flag,true))\r\n\r\n%%\r\nn=35;\r\nv = amazing_circle(n);\r\nflag = false;\r\nif length(unique(v)) == n \u0026 (min(v) == 1) \u0026 (max(v) == n)\r\n    for i = 1:n-1\r\n        lst(i) = mod(sqrt(v(i)+v(i+1)), 1) == 0;\r\n    end\r\n    lst(n) = mod(sqrt(v(n)+v(1)), 1) == 0;\r\n    if sum(lst) == n\r\n        flag = true;\r\n    end\r\nend\r\nassert(isequal(flag,true))\r\n\r\n%%\r\nn=36;\r\nv = amazing_circle(n);\r\nv = v(1:n);\r\nflag = false;\r\nif length(unique(v)) == n \u0026 (min(v) == 1) \u0026 (max(v) == n)\r\n    for i = 1:n-1\r\n        lst(i) = mod(sqrt(v(i)+v(i+1)), 1) == 0;\r\n    end\r\n    lst(n) = mod(sqrt(v(n)+v(1)), 1) == 0;\r\n    if sum(lst) == n\r\n        flag = true;\r\n    end\r\nend\r\nassert(isequal(flag,true))\r\n\r\n%%\r\nn=37;\r\nv = amazing_circle(n);\r\nv = v(1:n);\r\nflag = false;\r\nif length(unique(v)) == n \u0026 (min(v) == 1) \u0026 (max(v) == n)\r\n    for i = 1:n-1\r\n        lst(i) = mod(sqrt(v(i)+v(i+1)), 1) == 0;\r\n    end\r\n    lst(n) = mod(sqrt(v(n)+v(1)), 1) == 0;\r\n    if sum(lst) == n\r\n        flag = true;\r\n    end\r\nend\r\nassert(isequal(flag,true))\r\n\r\n%%\r\nn=38;\r\nv = amazing_circle(n);\r\nv = v(1:n);\r\nflag = false;\r\nif length(unique(v)) == n \u0026 (min(v) == 1) \u0026 (max(v) == n)\r\n    for i = 1:n-1\r\n        lst(i) = mod(sqrt(v(i)+v(i+1)), 1) == 0;\r\n    end\r\n    lst(n) = mod(sqrt(v(n)+v(1)), 1) == 0;\r\n    if sum(lst) == n\r\n        flag = true;\r\n    end\r\nend\r\nassert(isequal(flag,true))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":517609,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":7,"test_suite_updated_at":"2021-11-29T05:26:18.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2021-11-15T02:36:58.000Z","updated_at":"2026-06-01T00:06:15.000Z","published_at":"2021-11-15T04:44:57.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor given natural number n, create amazing circle of numbers 1 to n without a repeat.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis circle is that the sum of any two adjacent numbers is a perfect square.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, if n = 32,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"768\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"768\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSo, output is\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e                          [1 8 28 21 4 32 17 19 30 6 3 13 12 24 25 11 5 31 18 7 29 20 16 9 27 22 14 2 23 26 10 15]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf the condition is satisfied, it is the correct answer regardless of the order of the vectors.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf there is no amazing circle vector, return empty vector 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":53980,"title":"Determine whether a player solved a Cody problem","description":"Write a function to determine whether a player has solved the specified Cody problems. The problem numbers are specified as a vector, and the player names are specified either as a character vector for a single name or a cell array of character vectors for multiple names. Because some names are not unique, the player IDs are included as well. Return 1 if the player solved the problem, 0 if the player did not solve the problem, and -1 if the problem does not exist. If there are  players and  problems, the first output should be an  matrix. The second output should be a cell array of the problem titles.\r\nI plan to check the tests regularly as players solve more problems. If you spot a problem, please feel free to contact me through my profile page. ","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 156px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 78px; transform-origin: 407px 78px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to determine whether a player has solved the specified Cody problems. The problem numbers are specified as a vector, and the player names are specified either as a character vector for a single name or a cell array of character vectors for multiple names. Because some names are not unique, the player IDs are included as well. Return 1 if the player solved the problem, 0 if the player did not solve the problem, and -1 if the problem does not exist. If there are \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003em\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e players and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e problems, the first output should be an \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"37.5\" height=\"18\" alt=\"mxn\" style=\"width: 37.5px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e matrix. The second output should be a cell array of the problem titles.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI plan to check the tests regularly as players solve more problems. If you spot a problem, please feel free to contact me through \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/profile/authors/1887879\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003emy profile page\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [y,CPtitle] = isSolved(CPnum,players)\r\n% CPnum    1xn vector of problem numbers\r\n% players  either a character vector or a 1xm cell array of player names\r\n% y        mxn matrix of results (1 = solved, 0 = not solved, -1 = problem does not exist)\r\n% CPtitle  cell array of problem titles\r\n\r\ny = randi(3)-2;\r\nCPtitle = 'Verb the adjective noun';\r\nend","test_suite":"%% \r\nn  = 44340:44345;\r\np  = 'ChrisR';\r\nID = 1887879;\r\n[y,t] = isSolved(n,p,ID);\r\ny_correct = [1 1 1 0 1 1];\r\nt_correct = {'Recaman Sequence - III','Hexagonal numbers on a spiral matrix','Spot the First Occurrence of 5','Pair Primes','The 5th Root','MATLAB Counter'};\r\nassert(isequal(y,y_correct))\r\nassert(isequal(t,t_correct))\r\n\r\n%%\r\nn  = 2100:2105;\r\np  = 'ChrisR';\r\nID = 1887879;\r\n[y,t] = isSolved(n,p,ID);\r\ny_correct = [1 -1 -1 0 1 -1];\r\nt_correct = {'distance to a straight line (2D) given any 2 distinct points on this straight line','Simple Robotics 1: On track?','construct matrix with identical rows'};\r\nassert(isequal(y,y_correct))\r\nassert(isequal(t(y~=-1),t_correct))\r\n\r\n%% Grab bag\r\nn  = [46031 46053 46114 46573 47415 51451 51675 51803];\r\np  = {'William','Tim','David Hill','Nikolaos Nikolaou','Dyuman Joshi','Ramon Villamangca'};\r\nID = [173294 496166 10491973 7310613 2873 13897958];\r\n[y,t] = isSolved(n,p,ID);\r\ny_correct = [1 0 1 1 1 1 1 1; 1 1 1 1 1 0 1 0; 0 0 0 1 1 1 1 1; 0 0 0 0 0 0 1 0; 0 0 1 1 1 0 1 0; 0 0 0 0 0 0 1 0];\r\nt_correct = {'Construct dimensionless parameters','Construct finite difference approximations of derivatives','Compute the date of a special marriage milestone','Determine the winner of a goofy golf tournament','List ways to reach a target sum',['Guess the card in Fitch Cheney’s five-card trick'],'Add 100','Eliminate redundant numbers in text'};\r\nassert(isequal(y,y_correct))\r\nassert(isequal(t,t_correct))\r\n\r\n%% My targets (2022/01/29)\r\nn  = [193 375 782 2523 42834 45272 45274 47623];\r\np  = 'ChrisR';\r\nID = 1887879;\r\n[y,~] = isSolved(n,p,ID);   \r\n%assert(all(y==0));\r\ny_correct = [1 0 0 1 1 1 1 0];   %  2022/12/29\r\nassert(isequal(y,y_correct))\r\n\r\n%% Problems with \u003e9 likes that I haven't solved as of 2022/01/29 \r\nn = [283 3075 42888 42996 43670 44376 44630];      % CP 375 is in the target list\r\np = {'William','Tim','David Hill','Nikolaos Nikolaou','Mehmet OZC','minnolina','Dyuman Joshi','Ramon Villamangca','ChrisR'};\r\nID = [173294 496166 10491973 7310613 3877541 9646924 12862873 13897958 1887879];\r\n[y,~] = isSolved(n,p,ID);\r\ny_correct = [1 1 1 1 1 1 1; 1 1 1 0 0 1 1; 0 0 0 1 1 0 0; 0 1 0 1 1 0 1; 0 1 0 1 0 0 0; 0 0 0 0 0 0 0; 0 1 0 0 1 0 0; 0 0 1 0 0 0 0; 0 1 1 1 1 0 1];  %  Updated 2022/04/24\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nfiletext = fileread('isSolved.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'assert'); \r\nassert(~illegal)","published":true,"deleted":false,"likes_count":2,"comments_count":6,"created_by":46909,"edited_by":46909,"edited_at":"2024-11-02T15:08:42.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":"2024-11-02T15:08:42.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2022-01-29T16:08:07.000Z","updated_at":"2026-06-01T00:34:35.000Z","published_at":"2022-01-29T16:17:43.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to determine whether a player has solved the specified Cody problems. The problem numbers are specified as a vector, and the player names are specified either as a character vector for a single name or a cell array of character vectors for multiple names. Because some names are not unique, the player IDs are included as well. Return 1 if the player solved the problem, 0 if the player did not solve the problem, and -1 if the problem does not exist. If there are \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"m\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003em\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e players and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"n\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e problems, the first output should be an \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"mxn\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003em\\\\times n\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e matrix. The second output should be a cell array of the problem titles.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI plan to check the tests regularly as players solve more problems. If you spot a problem, please feel free to contact me through \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/profile/authors/1887879\\\"\u003e\u003cw:r\u003e\u003cw:t\u003emy profile page\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":53995,"title":"Solve an ODE: nonlinear third-order equation","description":"Write a function to solve the ordinary differential equation\r\n\r\non the domain  with , , and either  or . The input variable ord indicates the order (either 1 or 2) of the specified derivative. The function should return the values of  at the requested values of the independent variable .","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 124px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407.5px 62px; transform-origin: 407.5px 62px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384.5px 10.5px; text-align: left; transform-origin: 384.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 176.35px 7.66667px; transform-origin: 176.35px 7.66667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to solve the ordinary differential equation\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384.5px 10.5px; text-align: left; transform-origin: 384.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"y'y'' = y'''\" style=\"width: 71px; height: 18px;\" width=\"71\" height=\"18\"\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 64px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384.5px 32px; text-align: left; transform-origin: 384.5px 32px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 46.2917px 7.66667px; transform-origin: 46.2917px 7.66667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eon the domain \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"0 \u003c= x \u003c= 1\" style=\"width: 62.5px; height: 18px;\" width=\"62.5\" height=\"18\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 16.3333px 7.66667px; transform-origin: 16.3333px 7.66667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e with \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"y(0) = y0\" style=\"width: 60px; height: 20px;\" width=\"60\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.88333px 7.66667px; transform-origin: 3.88333px 7.66667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"y(1) = y1\" style=\"width: 60px; height: 20px;\" width=\"60\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 36.95px 7.66667px; transform-origin: 36.95px 7.66667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, and either \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"y'(0) = y'0\" style=\"width: 70px; height: 20px;\" width=\"70\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 10.1083px 7.66667px; transform-origin: 10.1083px 7.66667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e or \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"y''(0) = y''0\" style=\"width: 78.5px; height: 20px;\" width=\"78.5\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 61.4583px 7.66667px; transform-origin: 61.4583px 7.66667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. The input variable \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 11.55px 7.66667px; transform-origin: 11.55px 7.66667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 11.55px 8px; transform-origin: 11.55px 8px; \"\u003eord\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.5083px 7.66667px; transform-origin: 31.5083px 7.66667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e indicates the order (either 1 or 2) of the specified derivative. The function should return the values of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"y(x)\" style=\"width: 30px; height: 18.5px;\" width=\"30\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 84.4px 7.66667px; transform-origin: 84.4px 7.66667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e at the requested values of the independent variable \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ex\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.94167px 7.66667px; transform-origin: 1.94167px 7.66667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = solve3ordNLODE(x,y0,y1,yd,ord)\r\n%  x = requested values of the independent variable\r\n%  y = values of function at the requested values of x\r\n%  y0, y1 = values of the function at x = 0 and x = 1\r\n%  yd = value of either y'(0) or y''(0)\r\n%  ord = 1 if yd is the first derivative or 2 if it is the second derivative\r\n\r\n   y = A1*exp(r1*x)+A2*exp(r2*x)+A3*exp(r3*x);\r\nend","test_suite":"%%\r\ny0 = 2.231252941;\r\ny1 = 6.297567308;\r\nyd = 1.557407725;\r\nord = 1;\r\nx  = [0.3 0.5 0.8];\r\ny_correct = [2.790588219 3.308319582 4.544300275];\r\nassert(all(abs(solve3ordNLODE(x,y0,y1,yd,ord)-y_correct)\u003c1e-6))\r\n\r\n%%\r\ny0 = 2.231252941;\r\ny1 = 6.297567308;\r\nyd = 1.71275941;\r\nord = 2;\r\nx  = [0.3 0.5 0.8];\r\ny_correct = [2.790588219 3.308319582 4.544300275];\r\nassert(all(abs(solve3ordNLODE(x,y0,y1,yd,ord)-y_correct)\u003c1e-6))\r\n\r\n%%\r\ny0 = 3.287682072;\r\ny1 = 9.107569130;\r\nyd = 1.154700538;\r\nord = 1;\r\nx  = 0.25:0.25:0.75;\r\ny_correct = [3.669824691 4.306714708 5.456245713];\r\nassert(all(abs(solve3ordNLODE(x,y0,y1,yd,ord)-y_correct)\u003c1e-6))\r\n\r\n%%\r\ny0 = 3.287682072;\r\ny1 = 9.107569130;\r\nyd = 8/3;\r\nord = 2;\r\nx  = 0.25:0.25:0.75;\r\ny_correct = [3.669824691 4.306714708 5.456245713];\r\nassert(all(abs(solve3ordNLODE(x,y0,y1,yd,ord)-y_correct)\u003c1e-6))\r\n\r\n%%\r\ny0 = 3.693147181;\r\ny1 = 3.046411846;\r\nyd = -2;\r\nord = 1;\r\nx  = 0.2:0.2:0.8;\r\ny_correct = [3.36426198 3.152361229 3.034571214 3.000213221];\r\nassert(all(abs(solve3ordNLODE(x,y0,y1,yd,ord)-y_correct)\u003c1e-6))\r\n\r\n%%\r\ny0 = 0.722781494;\r\ny1 = 2.637280183;\r\nyd = 1.030077779;\r\nord = 2;\r\nx  = 0.2:0.2:0.8;\r\ny_correct = [0.950884887 1.231252941 1.581096155 2.030246566];\r\nassert(all(abs(solve3ordNLODE(x,y0,y1,yd,ord)-y_correct)\u003c1e-6))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":46909,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-02-07T03:08:13.000Z","updated_at":"2026-06-01T00:31:01.000Z","published_at":"2022-02-07T03:11:39.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to solve the ordinary differential equation\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"y'y'' = y'''\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ey\\\\prime y\\\\prime\\\\prime = y\\\\prime\\\\prime\\\\prime\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eon the domain \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"0 \u0026lt;= x \u0026lt;= 1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 \\\\le x \\\\le 1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e with \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"y(0) = y0\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ey(0) = y_0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"y(1) = y1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ey(1) = y_1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, and either \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"y'(0) = y'0\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ey\\\\prime(0) = y\\\\prime\\n_0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e or \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"y''(0) = y''0\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ey\\\\prime\\\\prime(0) = y\\\\prime\\n\\\\prime_0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. The input variable \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eord\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e indicates the order (either 1 or 2) of the specified derivative. The function should return the values of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"y(x)\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ey(x)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e at the requested values of the independent variable \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"x\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":58827,"title":"Troubles With Spaces - Convert Some Messy Data Into A Clean Array","description":"I have a bunch of messy data I have to go through. The data is stored in an odd format, so str2num isn't working. And even when I get it to work, it is still too slow!\r\nI need you to turn the strings into numbers. Any '+++++++' values are values that overloaded the data collection device and should be set to NaN.\r\n'- 6.496' =\u003e -6.496\r\n'-10.430' =\u003e -10.430\r\n'+++++++' =\u003e NaN\r\n'+11.664' =\u003e 11.664\r\nOh, and output it into an array because I hate working with cells.  And there's a lot of data so it had better be fast!","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 215.795px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 406.989px 107.898px; transform-origin: 406.996px 107.898px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42.017px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 21.0085px; text-align: left; transform-origin: 383.999px 21.0085px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eI have a bunch of messy data I have to go through. The data is stored in an odd format, so str2num isn't working. And even when I get it to work, it is still too slow!\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42.017px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 21.0085px; text-align: left; transform-origin: 383.999px 21.0085px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eI need you to turn the strings into numbers. Any '+++++++' values are values that overloaded the data collection device and should be set to NaN.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7614px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 403.991px 40.8807px; transform-origin: 403.999px 40.8807px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 403.991px 10.2131px; text-wrap: nowrap; transform-origin: 403.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'- 6.496' \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e=\u0026gt; -6.496\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 403.991px 10.2131px; text-wrap: nowrap; transform-origin: 403.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'-10.430' \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e=\u0026gt; -10.430\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 403.991px 10.2131px; text-wrap: nowrap; transform-origin: 403.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'+++++++' \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e=\u0026gt; NaN\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 403.991px 10.2131px; text-wrap: nowrap; transform-origin: 403.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'+11.664' \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e=\u0026gt; 11.664\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0085px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 383.991px 10.4972px; text-align: left; transform-origin: 383.999px 10.5043px; white-space-collapse: preserve; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eOh, and output it into an array because I hate working with cells.  And there's a lot of data so it had better be fast!\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = str2numBetter(x)\r\n  y = str2num(cell2mat(x));\r\nend","test_suite":"%%\r\n% This first test should cover every possible case.\r\nx = {'- 6.496';'-10.430';'- 0.493';'+++++++';'+ 6.949';'- 5.368';'+13.214';'+11.664'};\r\nfinalData = [-6.496;-10.430;-0.493';NaN;+6.949;-5.368;+13.214;+11.664];\r\n\r\nassert(isequaln(str2numBetter(x),finalData))\r\n\r\n%%\r\nrng shuffle\r\nn = 30000; % Something small to start you off with.\r\nrNums=10*rand([n 1]);\r\nrNums(rNums\u003e9.99)=NaN;\r\nstr=char(sprintfc('%.3f', rNums, false));\r\nneg=2*randi([0 1],[n 1]);\r\ntens=randi([0 1],[n 1]);\r\nstr = [char(neg+'+') char(17*tens+' ') str];\r\nplus=('+');\r\nstr(isnan(rNums),:)=plus(ones(nnz(isnan(rNums)),7));\r\nx=mat2cell(str,ones(1,n),7);\r\ntic; y=str2numBetter(x); toc % The code I wrote took 0.005872 seconds to run\r\nfinalData = round(-1*(neg-1).*(rNums+10*tens),3);\r\n\r\nassert(isequaln(y,finalData))\r\n\r\n%%\r\nrng shuffle\r\nn = 12000000; % Yes! I have this much data!!!\r\nrNums=10*rand([n 1]);\r\nrNums(rNums\u003e9.99)=NaN; % Real world values overflow\r\nstr=char(sprintfc('%.3f', rNums, false)); % This is what num2str uses\r\nneg=2*randi([0 1],[n 1]); % Some numbers will be random\r\ntens=randi([0 1],[n 1]); % Some numbers will be \u003e10, but in an odd format \r\nstr = [char(neg+'+') char(17*tens+' ') str]; % Add the weird 10's place and multiply by negatives\r\nplus=('+');\r\nstr(isnan(rNums),:)=plus(ones(nnz(isnan(rNums)),7)); % Replace NaN with something more annoying\r\nx=mat2cell(str,ones(1,n),7); % The data is in cells, not an array!\r\ntic; y=str2numBetter(x); toc % The code I wrote takes about 3.5 seconds to run, with a size of 130.\r\nfinalData = round(-1*(neg-1).*(rNums+10*tens),3);\r\n\r\nassert(isequaln(y,finalData))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3115675,"edited_by":3115675,"edited_at":"2023-08-08T14:27:45.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":"2023-08-08T14:27:45.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2023-08-07T21:52:56.000Z","updated_at":"2026-06-04T20:25:23.000Z","published_at":"2023-08-07T22:00:31.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI have a bunch of messy data I have to go through. The data is stored in an odd format, so str2num isn't working. And even when I get it to work, it is still too slow!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI need you to turn the strings into numbers. Any '+++++++' values are values that overloaded the data collection device and should be set to NaN.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA['- 6.496' =\u003e -6.496\\n'-10.430' =\u003e -10.430\\n'+++++++' =\u003e NaN\\n'+11.664' =\u003e 11.664]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOh, and output it into an array because I hate working with cells.  And there's a lot of data so it had better be fast!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":58951,"title":"Neural Net: Convolutional NN wK Evolve Matrices Determination for LED Digit images; Variant Images Scored","description":"This challenge is to return final WH, WP, and K matrices, given initial XImgset, WH, WP, K, EPY using convolution(Ximg,K), ReLU on the hidden layer and Softmax on the output layer. Training images are LED binary matrices of digits 1:9 and 0. Sets of variants will be tested to show success beyond the training set. (Scale, Offset, Noise, zeroed node(s), intra-shifts).\r\nBack Propagation on WH and WP. Custom K evolve algorithm provided. Standard Back Propagation for K in future. \r\n\r\nTest sets 2 thru 5 show various Pixelations of numbers and the Neural Net success rate. Just fun to look at the data.\r\nAppears more training sets required and possibly more nodes to have high success. One translate not good.\r\n[WP,WH,dK]=ConvNN_Process(XImgset,K,WH,WP,EPY)\r\n \r\nThe matlab Latex code for making the CNN Processing chart included in template.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 781.5px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 390.75px; transform-origin: 407px 390.75px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 383px 8px; transform-origin: 383px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to return final WH, WP, and K matrices, given initial XImgset, WH, WP, K, EPY using convolution(Ximg,K), ReLU on the hidden layer and Softmax on the output layer. Training images are LED binary matrices of digits 1:9 and 0. Sets of variants will be tested to show success beyond the training set. (Scale, Offset, Noise, zeroed node(s), intra-shifts).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 362.5px 8px; transform-origin: 362.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eBack Propagation on WH and WP. Custom K evolve algorithm provided. Standard Back Propagation for K in future. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 8px; transform-origin: 0px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 363px 8px; transform-origin: 363px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTest sets 2 thru 5 show various Pixelations of numbers and the Neural Net success rate. Just fun to look at the data.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 342px 8px; transform-origin: 342px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAppears more training sets required and possibly more nodes to have high success. One translate not good.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 173px 8px; transform-origin: 173px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"border-block-end-style: solid; border-block-end-width: 1px; border-bottom-style: solid; border-bottom-width: 1px; perspective-origin: 2px 8.5px; transform-origin: 2px 8.5px; \"\u003e[\u003c/span\u003e\u003cspan style=\"perspective-origin: 33.5px 8px; transform-origin: 33.5px 8px; \"\u003eWP,WH,dK\u003c/span\u003e\u003cspan style=\"border-block-end-style: solid; border-block-end-width: 1px; border-bottom-style: solid; border-bottom-width: 1px; perspective-origin: 2px 8.5px; transform-origin: 2px 8.5px; \"\u003e]\u003c/span\u003e\u003cspan style=\"perspective-origin: 135.5px 8px; transform-origin: 135.5px 8px; \"\u003e=ConvNN_Process(XImgset,K,WH,WP,EPY)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 529.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 264.75px; text-align: left; transform-origin: 384px 264.75px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 875px;height: 524px\" 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\" data-image-state=\"image-loaded\" width=\"875\" height=\"524\"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 8px; transform-origin: 0px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 257px 8px; transform-origin: 257px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe matlab Latex code for making the CNN Processing chart included in template.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [WP,WH,dK]=ConvNN_Process(XImg,Kbase,WH,WP,EPY)\r\n %K=Kbase; % Required to calc dK\r\n %nX=size(XImg,3); %Number training images\r\n %nK=size(K,3); % Number kernels\r\n %XC=zeros(5,3,nK); %Pre-alloc for speed\r\n %nClasses=size(EPY,2);\r\n \r\n for epoch=1:2000 % Number of cycles to evolve WH and WP\r\n  dWH=0*WH;\r\n  dWP=0*WP;\r\n  for iCase=1:nX  %Cycle thru all Images while accumulating dWH and dWP\r\n %Forward Pass  X(7,5,nX)-Ximg,Conv-XC(5,3,nK),ReLU-XCY3(5,3,nK),XCY(1,nK*15+1)(1,91)\r\n % XCYV*WH-H(1,21), ReLU(H)-HY(1,21), HY*WP-P(1,10), Softmax(P)-PY\r\n   for k=1:nK\r\n    %XC(:,:,k)=(conv2(XImg(:,:,iCase),K(:,:,k),'valid')); %Use rot90 Kernel to make output align\r\n   end\r\n\r\n   %XCY3=max(0,XC); %ReLU  Not needed with all K coeff positive; safer to leave in for K offset case\r\n   %XCY=[XCY3(:)' 1]; % 1,nK*15+1  (1,91)  add a 1 for offset term\r\n \r\n   %[dWPi,dWHi]=Neural_ReLU_Back_Propagation(XCY,WH,WP,EPY(iCase,:));\r\n \r\n %Accumulate across all sample cases\r\n   %dWP=dWP+dWPi;\r\n   %dWH=dWH+dWHi;\r\n \r\n  end % iCase\r\n  \r\n  dKi=evolve_Kraz(epoch,XImg,WH,WP,K,nX,nK,nClasses);\r\n  K=K+dKi;\r\n  \r\n\r\n  %WP=WP+dWP/nX; % dWP/nX  Valid\r\n  %WH=WH+dWH/nX; % Usage /nX^2 requires 10K epochs for \u003e.8 PYs\r\n\r\n end % epoch\r\n dK=Kbase-K;\r\nend %ConvNN_Process\r\n\r\nfunction [dWP,dWH]=Neural_ReLU_Back_Propagation(X,WH,WP,EPY)\r\n% X will be single case [X1 X2 ... XN 1]\r\n  LR=1; % Learning Rate\r\n  \r\n  H=X*WH;\r\n  HY=max(0,H); % ReLU\r\n  \r\n  P=HY*WP ; %\r\n  PY=exp(P)./sum(exp(P),2); %Softmax [PY1 PY2 ... PYM]\r\n  \r\n  %Back Propagation Calculations\r\n  ErrPY=EPY-PY;\r\n  \r\n  dPY=PY.*(1-PY).*ErrPY;\r\n  dWP=LR*HY'.*dPY;\r\n  \r\n  dHY=HY.*(1-HY).*sum(WP.*dPY,2)';\r\n  dWH=LR*X'.*dHY;\r\nend % Neural_ReLU_Back_Propagation\r\n\r\nfunction [dK]=evolve_Kraz(epoch,XImgset,WH,WP,K,nX,nK,nClasses)\r\n%This is not standard back propagation for K.\r\n%This is a much slower raz original K evolve of 1 pixel per kernel by small delta\r\n%selected based upon full PY matrix of training\r\n%Random start does better than pre-defined features\r\n%Singular pixel required for stability. Small step size .0001\r\n%Kernel Forward sensitivity and Update based on +/- Sum prediction direction\r\n%Harder to make work than expected\r\n%Have not solved standard back propagation for defined X,WH,WP,K dimensions\r\n\r\n dK=0*K;\r\n XC=zeros(5,3,nK);\r\n if ~mod(epoch,10)==0,return;end % Option to reduce K update rate due to Slowness\r\n PYmask=-ones(nX,nClasses)+10*eye(nClasses); % One sample per class, Know it is 10\r\n        %Wt factor of 9 for truth diag and -1 for all non-truth answers;\r\n mXCY=zeros(nX,91);\r\n%Create Baseline mPY (nX,10)\r\n for iCase=1:nX  %Cycle thru all Images create mXCY\r\n  for k=1:nK\r\n   XC(:,:,k)=(conv2(XImgset(:,:,iCase),K(:,:,k),'valid')); %Need to rot90 Kernel to make output align\r\n  end\r\n  XCY3=max(0,XC); %ReLU  Not needed with all K coeff positive\r\n  mXCY(iCase,:)=[XCY3(:)' 1]; % 1,nK*15  1,91  add a 1 for offset term\r\n end\r\n mPY=Neural_ReLU_Forward(mXCY,WH,WP);\r\n\r\n for pK=1:nK %For each Kernel tweak one pixel +.1 then -.1 to see PY response ALL Images\r\n  for rK=1:3\r\n   for cK=1:3\r\n    eK=K;\r\n   \r\n    eK(rK,cK,pK)=K(rK,cK,pK)+.1;\r\n    for iCase=1:nX  %Cycle thru all Images create mXCY\r\n     for k=1:nK\r\n      XC(:,:,k)=(conv2(XImgset(:,:,iCase),eK(:,:,k),'valid')); %Need to rot90 Kernel to make output align\r\n     end\r\n     XCY3=max(0,XC); %ReLU  Not needed with all K coeff positive\r\n     mXCY(iCase,:)=[XCY3(:)' 1]; % 1,nK*15  1,91  add a 1 for offset term\r\n    end\r\n    \r\n    mPYK=Neural_ReLU_Forward(mXCY,WH,WP); %Performance All Training for single Pixel Tweaked\r\n    mPYD=mPYK-mPY;\r\n    dsump=sum(sum(PYmask.*mPYD)); %Improvement\r\n    if dsump\u003c0, dsump=0;end\r\n    dK(rK,cK,pK)=dsump; \r\n   \r\n    eK(rK,cK,pK)=K(rK,cK,pK)-.2; % Net -.1\r\n    for iCase=1:nX  %Cycle thru all Images create mXCY\r\n     for k=1:nK\r\n      XC(:,:,k)=(conv2(XImgset(:,:,iCase),eK(:,:,k),'valid')); %Need to rot90 Kernel to make output align\r\n     end\r\n     XCY3=max(0,XC); %ReLU  Not needed with all K coeff positive\r\n     mXCY(iCase,:)=[XCY3(:)' 1]; % 1,nK*15  1,91  add a 1 for offset term\r\n    end\r\n    \r\n    [mPYK]=Neural_ReLU_Forward(mXCY,WH,WP); %Performance All Training for single Pixel Tweaked\r\n    mPYD=mPYK-mPY;\r\n    dsumn=sum(sum(PYmask.*mPYD)); %Improvement\r\n    if dsumn\u003c0, dsumn=0;end\r\n    if dsumn\u003edsump\r\n     dK(rK,cK,pK)=-dsumn;\r\n    end\r\n  end % cK\r\n end%rK\r\nend % pK\r\n\r\n%Adjust only one pixel per kernel\r\n for k=1:nK\r\n  mK=dK(:,:,k);\r\n  [kr,kc]=find(abs(mK)==max(abs(mK(:))),1,'first');\r\n  dK(:,:,k)=0;\r\n  dK(kr,kc,k)=.001*sign(mK(kr,kc));\r\n end\r\n\r\nend %Evolve_K\r\n\r\nfunction [PY]=Neural_ReLU_Forward(X,WH,WP)\r\n%raz ReLU/Softmax\r\n  n=1;\r\n  \r\n  H=X*WH;\r\n%   HY=1./(1+exp(-H)); %[HY1 HY2 1]  .6803 .6637 1 Sigmoid\r\n%   HY(:,end)=1; %Sigmoid\r\n  HY=max(0,H);\r\n  \r\n  P=HY*WP ; %\r\n  PY=exp(P)./sum(exp(P),2); %Softmax [PY1 PY2]\r\n  \r\nend % Neural_ReLU_Forward\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","test_suite":"%%\r\nglobal WH WP K\r\nImgsetstr= ... %5x3 digits 1:9 0\r\n ['010111111101111100111111111111';\r\n  '010001001101100100001101101101';\r\n  '010111111111111111001111111101';\r\n  '010100001001001101001101001101';\r\n  '010111111001111111001111001111'];\r\nImgsetv=Imgsetstr-'0';\r\n\r\nnX=size(Imgsetv,2)/3;\r\nXImgset=zeros(7,5,nX); % Images Zero Ring for conv2(I,K,'valid') where K is 3x3\r\n\r\nnClasses=10;\r\nEPY=eye(nClasses); % One case for each digit from 1 to 9 and then 0 which is class type 10\r\n%One input for each Class\r\n\r\nKsetstr= ... %3x3 kernel mask (will be scaled/offset)\r\n ['000000010010010000'; %Centered corners and vert/horiz\r\n  '011110011110010111'; %Kernels are identifying Features\r\n  '010010000000010000'];\r\n\r\nKsetv=Ksetstr-'0';\r\nKh=size(Ksetv,1);\r\nnK=size(Ksetv,2)/Kh; %Number of Kernels\r\n%Kset=reshape(Ksetv,Kh,Kh,nK);\r\n%Kset=rand(Kh,Kh,nK); % Using Random Start\r\n%Kernel needs normalization either scale or offset; Two large scale slows convergence or possible fail\r\n%Ksetr90=rot90(Kset(:,:,:),2)/3; % Rotate and normalize 3, each kernel sum is 3 Valid  2-Fails\r\n%Ksetr90=(rot90(Kset(:,:,:),2)-.5)/5; % Rotate and offset -0.5  Valid \r\n%Kbase=Ksetr90;\r\nIh=size(Imgsetv,1);\r\nImgset53=reshape(Imgsetv,Ih,3,size(Imgsetv,2)/3);\r\nfor k=1:size(Imgset53,3)\r\n XImgset(2:end-1,2:end-1,k)=Imgset53(:,:,k);\r\nend\r\n\r\n%Stability sensitive to WP and WH values; rand start needs offset and normalization\r\n% X(7,5,nX) XC(5,3,nK) (XCY(1,91) WH(91,21) WP(21,10)\r\nztic=tic;\r\nfor ztry=1:5 %Allow three processing attempts to get a working solution\r\n Kset=rand(Kh,Kh,nK); % Using Random Starts\r\n Ksetr90=(rot90(Kset(:,:,:),2)-.5)/5; % Rotate and offset -0.5  and scaled\r\n Kbase=Ksetr90;\r\n WH=[(rand(90,20)-.5)/sqrt(90+20) zeros(90,1)]; % 90=nK*15, 20 is arbitrary at 2*nCases WH(91,21)  Valid\r\n WH=[WH; zeros(1,20) 1]; % offset row, Start with zero bias\r\n\r\nWP=(rand(21,10)-.5)/sqrt(21+10);  % 10 output states  Valid\r\n\r\n[WP,WH,dK]=ConvNN_Process(XImgset,Kbase,WH,WP,EPY);\r\nK=Kbase-dK;\r\nfprintf('Process Time: %.3f sec\\n\\n',toc(ztic));\r\n\r\n  X=zeros(nX,91); %Forward process all images in one sequence\r\n  for iCase=1:nX\r\n   for k=1:nK\r\n    XC(:,:,k)=(conv2(XImgset(:,:,iCase),K(:,:,k),'valid')); %Need to rot90 Kernel to make output align\r\n   end\r\n   XCY3=max(0,XC); %ReLU  Not needed with all K coeff positive\r\n   XCY=[XCY3(:)' 1]; % 1,nK*15  1,91  add a 1 for offset term\r\n   X(iCase,:)=XCY; \r\n  end\r\n\r\n  H=X*WH; % Process all 10 cases\r\n  HY=max(0,H);\r\n  P=HY*WP ; %\r\n  PY=exp(P)./sum(exp(P),2); %Softmax [PY1 ... PY10]\r\n  [~,pcase]=max(PY,[],2);\r\n  [~,pexp]=max(EPY,[],2);\r\n  \r\n  fprintf('Ideal Input Training Cases\\n');\r\n  for i=1:nX\r\n   fprintf('Exp:%2i Pred:%2i   ',pexp(i),pcase(i));\r\n   fprintf('.%04i  ',round(10000*PY(i,:)));fprintf('\\n');\r\n  end\r\n  fprintf('Accuracy %.0f%s\\n\\n',100*nnz(pcase==pexp)/nX,'%');\r\n  %fprintf('\\n\\n');\r\n  if ~isequal(pcase,pexp),continue;end %Bad random draw, retry\r\n  \r\n  %Scaled-50%\r\n  X=zeros(nX,91); %Forward process all images in one sequence\r\n  XN=XImgset/2; %Scale to 7x5 Image planes\r\n  XNS=XN; %figure variable\r\n  for iCase=1:nX\r\n   for k=1:nK\r\n    XC(:,:,k)=(conv2(XN(:,:,iCase),K(:,:,k),'valid'));\r\n   end\r\n   XCY3=max(0,XC); %ReLU  Not needed with all K coeff positive\r\n   XCY=[XCY3(:)' 1]; % 1,nK*15  1,91  add a 1 for offset term\r\n   X(iCase,:)=XCY; \r\n  end\r\n\r\n  H=X*WH; % Process all 10 cases\r\n  HY=max(0,H);\r\n  P=HY*WP ; %\r\n  PY=exp(P)./sum(exp(P),2); %Softmax [PY1 ... PY10]\r\n  [~,Xpcase]=max(PY,[],2);\r\n  [~,Xpexp]=max(EPY,[],2);\r\n  \r\n  fprintf('50%s Scale - NN Strong Scale Invariance, Worst is 8\\n',char(37));\r\n  for i=1:nX\r\n   fprintf('Exp:%2i Pred:%2i   ',Xpexp(i),Xpcase(i));\r\n   fprintf('.%04i  ',round(10000*PY(i,:)));fprintf('\\n');\r\n  end\r\n  fprintf('Accuracy %.0f%s\\n\\n',100*nnz(Xpcase==Xpexp)/nX,'%');\r\n  \r\n  %Scaled-50% and Noise 60% added\r\n  X=zeros(nX,91); %Forward process all images in one sequence\r\n  XN=XImgset/2+.3*(rand(size(XImgset))-.5); %Scale and add Noise to 7x5 Image planes\r\n  XNSn=XN; %figure variable\r\n  for iCase=1:nX\r\n   for k=1:nK\r\n    XC(:,:,k)=(conv2(XN(:,:,iCase),K(:,:,k),'valid'));\r\n   end\r\n   XCY3=max(0,XC); %ReLU  Not needed with all K coeff positive\r\n   XCY=[XCY3(:)' 1]; % 1,nK*15  1,91  add a 1 for offset term\r\n   X(iCase,:)=XCY; \r\n  end\r\n\r\n  H=X*WH; % Process all 10 cases\r\n  HY=max(0,H);\r\n  P=HY*WP ; %\r\n  PY=exp(P)./sum(exp(P),2); %Softmax [PY1 ... PY10]\r\n  [~,Xpcase]=max(PY,[],2);\r\n  [~,Xpexp]=max(EPY,[],2);\r\n  \r\n  fprintf('50%s Scale and 60%s Noise added - Imperfect Results Expected\\n',char(37),'%');\r\n  %Note: double % is a special Cody sequence so using %s to write % character\r\n  for i=1:nX\r\n   fprintf('Exp:%2i Pred:%2i   ',Xpexp(i),Xpcase(i));\r\n   fprintf('.%04i  ',round(10000*PY(i,:)));fprintf('\\n');\r\n  end\r\n  fprintf('Accuracy %.0f%s\\n\\n',100*nnz(Xpcase==Xpexp)/nX,'%');\r\n  \r\n\r\n  %Offset+50%\r\n  X=zeros(nX,91); %Forward process all images in one sequence\r\n  XN=XImgset-.5; %Offset to 7x5 Image planes\r\n  XNo=XN;\r\n  for iCase=1:nX\r\n   for k=1:nK\r\n    XC(:,:,k)=(conv2(XN(:,:,iCase),K(:,:,k),'valid'));\r\n   end\r\n   XCY3=max(0,XC); %ReLU  Not needed with all K coeff positive\r\n   XCY=[XCY3(:)' 1]; % 1,nK*15  1,91  add a 1 for offset term\r\n   X(iCase,:)=XCY; \r\n  end\r\n\r\n  H=X*WH; % Process all 10 cases\r\n  HY=max(0,H);\r\n  P=HY*WP ; %\r\n  PY=exp(P)./sum(exp(P),2); %Softmax [PY1 ... PY10]\r\n  [~,Xpcase]=max(PY,[],2);\r\n  [~,Xpexp]=max(EPY,[],2);\r\n  \r\n  fprintf('50%s Offset - NN Somewhat Strong Offset Invariance - Weak 1,3,8,9\\n',char(37));\r\n  for i=1:nX\r\n   fprintf('Exp:%2i Pred:%2i   ',Xpexp(i),Xpcase(i));\r\n   fprintf('.%04i  ',round(10000*PY(i,:)));fprintf('\\n');\r\n  end\r\n  fprintf('Accuracy %.0f%s\\n\\n',100*nnz(Xpcase==Xpexp)/nX,'%');\r\n  \r\n  %Offset-50% Noise-60%\r\n  X=zeros(nX,91); %Forward process all images in one sequence\r\n  XN=XImgset-.5+.6*(rand(size(XImgset))-.5); %Offset to 7x5 Image planes\r\n  XNon=XN;\r\n  for iCase=1:nX\r\n   for k=1:nK\r\n    XC(:,:,k)=(conv2(XN(:,:,iCase),K(:,:,k),'valid'));\r\n   end\r\n   XCY3=max(0,XC); %ReLU  Not needed with all K coeff positive\r\n   XCY=[XCY3(:)' 1]; % 1,nK*15  1,91  add a 1 for offset term\r\n   X(iCase,:)=XCY; \r\n  end\r\n\r\n  H=X*WH; % Process all 10 cases\r\n  HY=max(0,H);\r\n  P=HY*WP ; %\r\n  PY=exp(P)./sum(exp(P),2); %Softmax [PY1 ... PY10]\r\n  [~,Xpcase]=max(PY,[],2);\r\n  [~,Xpexp]=max(EPY,[],2);\r\n  \r\n  fprintf('50%s Offset 60%s Noise - NN Mildly Strong Offset/Noise Invariance - Weak 1,3,8,9\\n',char(37),'%');\r\n  for i=1:nX\r\n   fprintf('Exp:%2i Pred:%2i   ',Xpexp(i),Xpcase(i));\r\n   fprintf('.%04i  ',round(10000*PY(i,:)));fprintf('\\n');\r\n  end\r\n  fprintf('Accuracy %.0f%s\\n\\n',100*nnz(Xpcase==Xpexp)/nX,'%');\r\n  \r\n\r\n  figure(1); imagesc([reshape(XImgset,7,[]);reshape(XNS,7,[]);reshape(XNSn,7,50); ...\r\n                      reshape(XNo,7,[]);reshape(XNon,7,50)]);\r\n  %xticks([]);yticks([]);axis equal;axis tight\r\n  \r\n  %figure(1); imagesc([reshape(XImgset,7,[]);reshape(XNS,7,[]);reshape(XNSn,7,50);reshape(XNo,7,[])]);\r\n  xticks([]);yticks([]);axis equal;axis tight\r\n  \r\n  figure(2); imagesc([reshape(Kbase,3,[]);zeros(1,18);reshape(K,3,[]);zeros(1,18); ...\r\n                      reshape(-dK,3,[])]);\r\n  xticks([]);yticks([]);axis equal;axis tight\r\n  \r\n  if isequal(pcase,pexp),break;end\r\n  \r\nend % ztry  Allowed 5 trials as rand may not always converge\r\n  \r\nvalid=isequal(pcase,pexp);\r\nassert(valid)\r\n\r\n%%\r\nglobal WH WP K\r\nImgsetstr= ... %5x3 digits 1 variants,3 variants\r\n ['100001010010000011110011111011';\r\n  '100001010110010011110001001001';\r\n  '100001010010010011010111011011';\r\n  '100001010010010011010001001001';\r\n  '100001111010010011010111111011'];\r\nImgsetv=Imgsetstr-'0';\r\npexp=[1 1 1 1 1 1 1 3 3 3]';\r\n\r\nnX=size(Imgsetv,2)/3;\r\nXImgset=zeros(7,5,nX); % Images Zero Ring for conv2(I,K,'valid') where K is 3x3\r\n\r\n%nClasses=10;\r\n%EPY=eye(nClasses); % One case for each digit from 1 to 9 and then 0 which is class type 10\r\n%One input for each Class\r\n\r\nnK=size(K,3); %Number of Kernels\r\n\r\nIh=size(Imgsetv,1);\r\nImgset53=reshape(Imgsetv,Ih,3,size(Imgsetv,2)/3);\r\nfor k=1:size(Imgset53,3)\r\n XImgset(2:end-1,2:end-1,k)=Imgset53(:,:,k);\r\nend\r\n\r\nfigure; imagesc(reshape(XImgset,7,[]));\r\nxticks([]);yticks([]);axis equal;axis tight\r\n\r\n  X=zeros(nX,91); %Forward process all images in one sequence\r\n  for iCase=1:nX\r\n   for k=1:nK\r\n    XC(:,:,k)=(conv2(XImgset(:,:,iCase),K(:,:,k),'valid')); %Need to rot90 Kernel to make output align\r\n   end\r\n   XCY3=max(0,XC); %ReLU  Not needed with all K coeff positive\r\n   XCY=[XCY3(:)' 1]; % 1,nK*15  1,91  add a 1 for offset term\r\n   X(iCase,:)=XCY; \r\n  end\r\n\r\n  H=X*WH; % Process all 10 cases\r\n  HY=max(0,H);\r\n  P=HY*WP ; %\r\n  PY=exp(P)./sum(exp(P),2); %Softmax [PY1 ... PY10]\r\n  [~,pcase]=max(PY,[],2);\r\n  %[~,pexp]=max(EPY,[],2);\r\n  \r\n  fprintf('Morphed 1s and 3s; Need more training\\n');\r\n  for i=1:nX\r\n   fprintf('Exp:%2i Pred:%2i   ',pexp(i),pcase(i));\r\n   fprintf('.%04i  ',round(10000*PY(i,:)));fprintf('\\n');\r\n  end\r\n  fprintf('Accuracy %.0f%s\\n',100*nnz(pcase==pexp)/nX,'%');\r\n  fprintf('\\n\\n');\r\n\r\nassert(1)\r\n\r\n\r\n%%\r\nglobal WH WP K\r\nImgsetstr= ... %5x3 digits 2 variants,4 variants\r\n ['011011100111110001000101101001';\r\n  '001001010001001101101111101011';\r\n  '111111001010010111111001101111';\r\n  '100100010100100001001001111001';\r\n  '111110001111011001001001001001'];\r\nImgsetv=Imgsetstr-'0';\r\npexp=[2 2 2 2 2 4 4 4 4 4]';\r\n\r\nnX=size(Imgsetv,2)/3;\r\nXImgset=zeros(7,5,nX); % Images Zero Ring for conv2(I,K,'valid') where K is 3x3\r\n\r\n%nClasses=10;\r\n%EPY=eye(nClasses); % One case for each digit from 1 to 9 and then 0 which is class type 10\r\n%One input for each Class\r\n\r\nnK=size(K,3); %Number of Kernels\r\n\r\nIh=size(Imgsetv,1);\r\nImgset53=reshape(Imgsetv,Ih,3,size(Imgsetv,2)/3);\r\nfor k=1:size(Imgset53,3)\r\n XImgset(2:end-1,2:end-1,k)=Imgset53(:,:,k);\r\nend\r\n\r\nfigure; imagesc(reshape(XImgset,7,[]));\r\nxticks([]);yticks([]);axis equal;axis tight\r\n\r\n  X=zeros(nX,91); %Forward process all images in one sequence\r\n  for iCase=1:nX\r\n   for k=1:nK\r\n    XC(:,:,k)=(conv2(XImgset(:,:,iCase),K(:,:,k),'valid')); %Need to rot90 Kernel to make output align\r\n   end\r\n   XCY3=max(0,XC); %ReLU  Not needed with all K coeff positive\r\n   XCY=[XCY3(:)' 1]; % 1,nK*15  1,91  add a 1 for offset term\r\n   X(iCase,:)=XCY; \r\n  end\r\n\r\n  H=X*WH; % Process all 10 cases\r\n  HY=max(0,H);\r\n  P=HY*WP ; %\r\n  PY=exp(P)./sum(exp(P),2); %Softmax [PY1 ... PY10]\r\n  [~,pcase]=max(PY,[],2);\r\n  %[~,pexp]=max(EPY,[],2);\r\n  \r\n  fprintf('Morphed 1s and 3s; Need more training\\n');\r\n  for i=1:nX\r\n   fprintf('Exp:%2i Pred:%2i   ',pexp(i),pcase(i));\r\n   fprintf('.%04i  ',round(10000*PY(i,:)));fprintf('\\n');\r\n  end\r\n  fprintf('Accuracy %.0f%s\\n',100*nnz(pcase==pexp)/nX,'%');\r\n  fprintf('\\n\\n');\r\n\r\nassert(1)\r\n\r\n\r\n%%\r\n\r\nglobal WH WP K\r\nImgsetstr= ... %5x3 digits 5,6,7,8 variants\r\n ['110110010111110100011111010010';\r\n  '100100100100100100001001101100';\r\n  '111111111111111110001001010010';\r\n  '001001001101101101001010101001';\r\n  '111011010111111110001100010010'];\r\nImgsetv=Imgsetstr-'0';\r\npexp=[5 5 5 6 6 6 7 7  8 5 ]';\r\n\r\nnX=size(Imgsetv,2)/3;\r\nXImgset=zeros(7,5,nX); % Images Zero Ring for conv2(I,K,'valid') where K is 3x3\r\n\r\n%nClasses=10;\r\n%EPY=eye(nClasses); % One case for each digit from 1 to 9 and then 0 which is class type 10\r\n%One input for each Class\r\n\r\nnK=size(K,3); %Number of Kernels\r\n\r\nIh=size(Imgsetv,1);\r\nImgset53=reshape(Imgsetv,Ih,3,size(Imgsetv,2)/3);\r\nfor k=1:size(Imgset53,3)\r\n XImgset(2:end-1,2:end-1,k)=Imgset53(:,:,k);\r\nend\r\n\r\nfigure; imagesc(reshape(XImgset,7,[]));\r\nxticks([]);yticks([]);axis equal;axis tight\r\n\r\n  X=zeros(nX,91); %Forward process all images in one sequence\r\n  for iCase=1:nX\r\n   for k=1:nK\r\n    XC(:,:,k)=(conv2(XImgset(:,:,iCase),K(:,:,k),'valid')); %Need to rot90 Kernel to make output align\r\n   end\r\n   XCY3=max(0,XC); %ReLU  Not needed with all K coeff positive\r\n   XCY=[XCY3(:)' 1]; % 1,nK*15  1,91  add a 1 for offset term\r\n   X(iCase,:)=XCY; \r\n  end\r\n\r\n  H=X*WH; % Process all 10 cases\r\n  HY=max(0,H);\r\n  P=HY*WP ; %\r\n  PY=exp(P)./sum(exp(P),2); %Softmax [PY1 ... PY10]\r\n  [~,pcase]=max(PY,[],2);\r\n  %[~,pexp]=max(EPY,[],2);\r\n  \r\n  fprintf('Morphed 1s and 3s; Need more training\\n');\r\n  for i=1:nX\r\n   fprintf('Exp:%2i Pred:%2i   ',pexp(i),pcase(i));\r\n   fprintf('.%04i  ',round(10000*PY(i,:)));fprintf('\\n');\r\n  end\r\n  fprintf('Accuracy %.0f%s\\n',100*nnz(pcase==pexp)/nX,'%');\r\n  fprintf('\\n\\n');\r\n\r\nassert(1)\r\n\r\n\r\n%%\r\nglobal WH WP K\r\nImgsetstr= ... %5x3 digits 7,9,0 variants\r\n ['110111111011111010010111101101';\r\n  '010101101101111101000001101100';\r\n  '010111111111111101010011111111';\r\n  '010001001001111101000001101001';\r\n  '010011111001111010010001111001'];\r\nImgsetv=Imgsetstr-'0';\r\npexp=[1 9 9 9 10 10 1 7  8 4 ]'; %Shape/Missing LEDs\r\n\r\nnX=size(Imgsetv,2)/3;\r\nXImgset=zeros(7,5,nX); % Images Zero Ring for conv2(I,K,'valid') where K is 3x3\r\n\r\n%nClasses=10;\r\n%EPY=eye(nClasses); % One case for each digit from 1 to 9 and then 0 which is class type 10\r\n%One input for each Class\r\n\r\nnK=size(K,3); %Number of Kernels\r\n\r\nIh=size(Imgsetv,1);\r\nImgset53=reshape(Imgsetv,Ih,3,size(Imgsetv,2)/3);\r\nfor k=1:size(Imgset53,3)\r\n XImgset(2:end-1,2:end-1,k)=Imgset53(:,:,k);\r\nend\r\n\r\nfigure; imagesc(reshape(XImgset,7,[]));\r\nxticks([]);yticks([]);axis equal;axis tight\r\n\r\n  X=zeros(nX,91); %Forward process all images in one sequence\r\n  for iCase=1:nX\r\n   for k=1:nK\r\n    XC(:,:,k)=(conv2(XImgset(:,:,iCase),K(:,:,k),'valid')); %Need to rot90 Kernel to make output align\r\n   end\r\n   XCY3=max(0,XC); %ReLU  Not needed with all K coeff positive\r\n   XCY=[XCY3(:)' 1]; % 1,nK*15  1,91  add a 1 for offset term\r\n   X(iCase,:)=XCY; \r\n  end\r\n\r\n  H=X*WH; % Process all 10 cases\r\n  HY=max(0,H);\r\n  P=HY*WP ; %\r\n  PY=exp(P)./sum(exp(P),2); %Softmax [PY1 ... PY10]\r\n  [~,pcase]=max(PY,[],2);\r\n  %[~,pexp]=max(EPY,[],2);\r\n  \r\n  fprintf('More training needed\\n');\r\n  for i=1:nX\r\n   fprintf('Exp:%2i Pred:%2i   ',pexp(i),pcase(i));\r\n   fprintf('.%04i  ',round(10000*PY(i,:)));fprintf('\\n');\r\n  end\r\n  fprintf('Accuracy %.0f%s\\n',100*nnz(pcase==pexp)/nX,'%');\r\n  fprintf('\\n\\n');\r\n\r\nassert(1)\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2023-09-03T16:21:37.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-09-03T03:05:26.000Z","updated_at":"2026-06-04T20:55:33.000Z","published_at":"2023-09-03T16:21:37.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to return final WH, WP, and K matrices, given initial XImgset, WH, WP, K, EPY using convolution(Ximg,K), ReLU on the hidden layer and Softmax on the output layer. Training images are LED binary matrices of digits 1:9 and 0. Sets of variants will be tested to show success beyond the training set. (Scale, Offset, Noise, zeroed node(s), intra-shifts).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBack Propagation on WH and WP. Custom K evolve algorithm provided. Standard Back Propagation for K in future. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTest sets 2 thru 5 show various Pixelations of numbers and the Neural Net success rate. Just fun to look at the data.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAppears more training sets required and possibly more nodes to have high success. One translate not good.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[WP,WH,dK]=ConvNN_Process(XImgset,K,WH,WP,EPY)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"524\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"875\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe matlab Latex code for making the CNN Processing chart included in 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":59541,"title":"Compute the polylogarithm for real arguments","description":"The polylogarithm  appears in quantum statistics and quantum electrodynamics, and for special cases of  and , it connects to the logarithm, ratios of polynomials, the zeta function, the Dirichlet eta function, and other functions. \r\nWrite a function to compute the polylogarithm  for real arguments. ","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 74px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 37px; transform-origin: 407px 37px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 43px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21.5px; text-align: left; transform-origin: 384px 21.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 57.575px 8px; transform-origin: 57.575px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe polylogarithm \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"39\" height=\"20\" alt=\"Li_n(z)\" style=\"width: 39px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 267.225px 8px; transform-origin: 267.225px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e appears in quantum statistics and quantum electrodynamics, and for special cases of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 15.5583px 8px; transform-origin: 15.5583px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ez\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 9.325px 8px; transform-origin: 9.325px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, it connects to the logarithm, ratios of polynomials, the \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/45988\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"text-decoration-line: underline; \"\u003ezeta\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.94167px 8px; transform-origin: 1.94167px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/45997\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"text-decoration-line: underline; \"\u003efunction\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 146.242px 8px; transform-origin: 146.242px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, the Dirichlet eta function, and other functions. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 22px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 11px; text-align: left; transform-origin: 384px 11px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 142.233px 8px; transform-origin: 142.233px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to compute the polylogarithm \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"39\" height=\"20\" alt=\"Li_n(z)\" style=\"width: 39px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 62.6167px 8px; transform-origin: 62.6167px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e for real arguments. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = polylogarithm(n,z)\r\n  y = sum(z^k/k^n);\r\nend","test_suite":"%%\r\nn = 2:2:16;\r\nz = 1;\r\ny = arrayfun(@(m) polylogarithm(m,z),n);\r\ny_correct = pi.^n.*[1/6 1/90 1/945 1/9450 1/93555 691/638512875 2/18243225 3617/325641566250];\r\nassert(all(abs(y-y_correct)\u003c1e-12))\r\n\r\n%%\r\nn = 3;\r\nz = 1;\r\ny = polylogarithm(n,z);\r\ny_correct = 1.202056903159594;\r\nassert(abs(y-y_correct)\u003c1e-12)\r\n\r\n%%\r\nn = 0;\r\nz = 1./(10:-1:2);\r\ny = arrayfun(@(s) polylogarithm(n,s),z);\r\ny_correct = 1./(9:-1:1);\r\nassert(all(abs(y-y_correct)\u003c1e-12))\r\n\r\n%%\r\nn = 1;\r\nz = [-2 -3/2 -1 -1/2 0 1/10 1/7 1/5 1/3 1/2];\r\ny = arrayfun(@(s) polylogarithm(n,s),z);\r\ny_correct = [-1.098612288668110 -0.916290731874155 -0.693147180559945 -0.405465108108164 0 0.105360515657826 0.154150679827258 0.223143551314210 0.405465108108164 0.693147180559945];\r\nassert(all(abs(y-y_correct)\u003c1e-12))\r\n\r\n%%\r\nn = -1;\r\nz = [-2 -3/2 -1 -1/2 1/7 1/5 1/2];\r\ny = arrayfun(@(s) polylogarithm(n,s),z);\r\ny_correct = [-2/9 -6/25 -1/4 -2/9 7/36 5/16 2];\r\nassert(all(abs(y-y_correct)\u003c1e-12))\r\n\r\n%%\r\nn = -2;\r\nz = [-2 -3/2 -1 -1/2 0 1/17 1/13 1/11 1/5];\r\ny = arrayfun(@(s) polylogarithm(n,s),z);\r\ny_correct = [2/27 6/125 0 -2/27 0 153/2048 91/864 33/250 15/32];\r\nassert(all(abs(y-y_correct)\u003c1e-12))\r\n\r\n%%\r\nn = 1:3;\r\nz = 1/2;\r\ny = arrayfun(@(m) polylogarithm(m,z),n);\r\nzeta3 = 1.202056903159594;\r\ny_correct = [log(2) (pi^2-6*log(2)^2)/12 (4*log(2)^3-2*pi^2*log(2)+21*zeta3)/24];\r\nassert(all(abs(y-y_correct)\u003c1e-12))\r\n\r\n%%\r\nn = 2;\r\nz = -1;\r\ny = polylogarithm(n,z);\r\ny_correct = -pi^2/12;\r\nassert(abs(y-y_correct)\u003c1e-12)\r\n\r\n%%\r\nn = -1/pi;\r\nz = exp(-1);\r\ny = polylogarithm(n,z);\r\ny_correct = 0.6541056465726233;\r\nassert(abs(y-y_correct)\u003c1e-12)\r\n\r\n%%\r\nn = 4;\r\nz = 1/2;\r\ny = polylogarithm(n,z);\r\ny_correct = 0.5174790616738994;\r\nassert(abs(y-y_correct)\u003c1e-12)\r\n\r\n%%\r\nn = 1/2;\r\nz = 1/2;\r\ny = polylogarithm(n,z);\r\ny_correct = 0.8061267230428522;\r\nassert(abs(y-y_correct)\u003c1e-12)\r\n\r\n%%\r\nn = -3/2;\r\nz = -1./2.^(1:4);\r\ny = arrayfun(@(s) polylogarithm(n,s),z);\r\ny_correct = [-0.1516441361365191 -0.1313580923579523 -0.0892942267818043 -0.05260782861980105];\r\nassert(all(abs(y-y_correct)\u003c1e-12))\r\n\r\n%%\r\nn = 5*randn(1,randi(10));\r\nz = 0;\r\ny = arrayfun(@(m) polylogarithm(m,z),n);\r\ny_correct = zeros(size(n));\r\nassert(all(abs(y-y_correct)\u003c1e-12))\r\n\r\n%%\r\nfiletext = fileread('polylogarithm.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'assert') || contains(filetext,'regexp'); \r\nassert(~illegal)","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":46909,"edited_by":46909,"edited_at":"2024-01-07T03:51:00.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2024-01-06T17:30:34.000Z","updated_at":"2026-06-05T00:31:59.000Z","published_at":"2024-01-06T17:31:22.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe polylogarithm \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"Li_n(z)\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e{\\\\rm Li}_n(z)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e appears in quantum statistics and quantum electrodynamics, and for special cases of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ez\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, it connects to the logarithm, ratios of polynomials, the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/45988\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ezeta\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/45997\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003efunction\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, the Dirichlet eta function, and other functions. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to compute the polylogarithm \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"Li_n(z)\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e{\\\\rm Li}_n(z)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e for real arguments. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":59079,"title":"The Hacker Parole Problem","description":"The hacker parole problem\r\n\r\n100 hackers have been imprisoned, but have been given a way to get parole.\r\nEach of the 100 prisoners has been given a number.  There is also a room with 100 numbered boxes.  Each box contains a number.  Each prisoner goes into the room and has 50 tries to find their number in a box.\r\nIf all 100 prisoners can find their number within 50 tries, then they all get parole.  But, if one prisoner cannot find their number within 50 tries all the hackers go back to prison for another year.\r\nThe hackers meet and devise an algorithm that has better than a 30 percent chance of going free that year.\r\nThe hacker's solution\r\nIt is obvious that having each prisoner randomly open boxes will fail.  Each prisoner has a 0.5 chance of finding their number and so the chance that all 100 are able to do it is 0.5^100.  Essentially giving a 0 probablility.\r\nThe hacker solution is to have each prisoner go into the room and use their number to find a box.  They open the box, and if their number is in it they are done.  If their number is not in it, then they use the number in the box to choose another box.  They continue following the numbers and opening boxes until they find the box with their number on it.\r\nThis solution will free all the prisoners over 30% of the time.  This is because the random numbers in boxes form loops.  For example if we have five boxes and five number we might see this set of numbers stored in boxes.\r\n\r\nThe hacker holding number 3 would open box 3 and find a 4.  Then the hacker would open box 4 and find a 1.  Then the hacker would open box 1 and find a 3. \r\n\r\nThis solution works because numbers stored this way always form at least one loop.  In this case we have the following loops:\r\n\r\n3 -\u003e 4 -\u003e 1 -\u003e 3\r\n5 -\u003e 2 -\u003e 5\r\n\r\nThe hackers know that they will get their freedom if the longest loop among the 100 boxes is 50 boxes or shorter.  This happens 30% of the time, thus they get their freedom 30% of the time.\r\nTesting the solution\r\nWe want to test the hacker's algorithm by running 10,000 trials and seeing how often the hackers won their freedom.  Math says that the number should be greater than or equal to 3000 times in 10,000 trials.\r\nYour assignment is to write a function named runTrial() that takes a vector of boxes in the room and runs a trial of the hacker's algorithm. It returns whether the hackers found the number after looking at only half the boxes. The boxes are a vector that contains randomized numbers from 1:numBoxes.\r\nThe testbench will run your trial 10000 times and capture the number of times the hackers won their freedom. That number should be above 3000.\r\nThe function looks like this:\r\nfunction foundAll = runTrial(boxes)\r\n        %%  Insert your code here. I solved this problem in 11 lines inside the function\r\nend","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 1078.6px; display: block; min-width: 0px; padding-block-start: 0px; padding-inline-start: 2px; padding-left: 2px; padding-top: 0px; perspective-origin: 408px 539.3px; transform-origin: 408px 539.3px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 20px; font-family: Helvetica, Arial, sans-serif; font-size: 20px; font-weight: 700; line-height: 20px; margin-block-end: 5px; margin-block-start: 3px; margin-bottom: 5px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 3px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10px; text-align: left; transform-origin: 384px 10px; white-space-collapse: preserve; margin-left: 4px; margin-top: 3px; margin-bottom: 5px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe hacker parole problem\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e100 hackers have been imprisoned, but have been given a way to get parole.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eEach of the 100 prisoners has been given a number.  There is also a room with 100 numbered boxes.  Each box contains a number.  Each prisoner goes into the room and has 50 tries to find their number in a box.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf all 100 prisoners can find their number within 50 tries, then they all get parole.  But, if one prisoner cannot find their number within 50 tries all the hackers go back to prison for another year.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe hackers meet and devise an algorithm that has better than a 30 percent chance of going free that year.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20px; font-family: Helvetica, Arial, sans-serif; font-size: 20px; font-weight: 700; line-height: 20px; margin-block-end: 5px; margin-block-start: 20px; margin-bottom: 5px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 20px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10px; text-align: left; transform-origin: 384px 10px; white-space-collapse: preserve; margin-left: 4px; margin-top: 20px; margin-bottom: 5px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe hacker's solution\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIt is obvious that having each prisoner randomly open boxes will fail.  Each prisoner has a 0.5 chance of finding their number and so the chance that all 100 are able to do it is 0.5^100.  Essentially giving a 0 probablility.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe hacker solution is to have each prisoner go into the room and use their number to find a box.  They open the box, and if their number is in it they are done.  If their number is not in it, then they use the number in the box to choose another box.  They continue following the numbers and opening boxes until they find the box with their number on it.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis solution will free all the prisoners over 30% of the time.  This is because the random numbers in boxes form loops.  For example if we have five boxes and five number we might see this set of numbers stored in boxes.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 44.6px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 22.3px; text-align: left; transform-origin: 384px 22.3px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"vertical-align:-15px\"\u003e\u003cimg 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\" width=\"114\" height=\"44.5\" style=\"width: 114px; height: 44.5px;\"\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe hacker holding number 3 would open box 3 and find a 4.  Then the hacker would open box 4 and find a 1.  Then the hacker would open box 1 and find a 3. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis solution works because numbers stored this way always form at least one loop.  In this case we have the following loops:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e3 -\u0026gt; 4 -\u0026gt; 1 -\u0026gt; 3\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e5 -\u0026gt; 2 -\u0026gt; 5\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe hackers know that they will get their freedom if the longest loop among the 100 boxes is 50 boxes or shorter.  This happens 30% of the time, thus they get their freedom 30% of the time.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20px; font-family: Helvetica, Arial, sans-serif; font-size: 20px; font-weight: 700; line-height: 20px; margin-block-end: 5px; margin-block-start: 20px; margin-bottom: 5px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 20px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10px; text-align: left; transform-origin: 384px 10px; white-space-collapse: preserve; margin-left: 4px; margin-top: 20px; margin-bottom: 5px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTesting the solution\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWe want to test the hacker's algorithm by running 10,000 trials and seeing how often the hackers won their freedom.  Math says that the number should be greater than or equal to 3000 times in 10,000 trials.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYour assignment is to write a function named \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003erunTrial()\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e that takes a vector of boxes in the room and runs a trial of the hacker's algorithm. It returns whether the hackers found the number after looking at only half the boxes. The boxes are a vector that contains randomized numbers from 1:numBoxes.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe testbench will run your trial 10000 times and capture the number of times the hackers won their freedom. That number should be above 3000.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe function looks like this:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 54px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 404px 27px; transform-origin: 404px 27px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.8px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.8px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.8px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.8px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 9px; text-wrap-mode: nowrap; transform-origin: 404px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"border-block-end-color: rgb(14, 0, 255); border-block-start-color: rgb(14, 0, 255); border-bottom-color: rgb(14, 0, 255); border-inline-end-color: rgb(14, 0, 255); border-inline-start-color: rgb(14, 0, 255); border-left-color: rgb(14, 0, 255); border-right-color: rgb(14, 0, 255); border-top-color: rgb(14, 0, 255); caret-color: rgb(14, 0, 255); color: rgb(14, 0, 255); column-rule-color: rgb(14, 0, 255); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(14, 0, 255); text-decoration-color: rgb(14, 0, 255); text-emphasis-color: rgb(14, 0, 255); \"\u003efunction \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003efoundAll = runTrial(boxes)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.8px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.8px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.8px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.8px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 9px; text-wrap-mode: nowrap; transform-origin: 404px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"border-block-end-color: rgb(0, 100, 0); border-block-start-color: rgb(0, 100, 0); border-bottom-color: rgb(0, 100, 0); border-inline-end-color: rgb(0, 100, 0); border-inline-start-color: rgb(0, 100, 0); border-left-color: rgb(0, 100, 0); border-right-color: rgb(0, 100, 0); border-top-color: rgb(0, 100, 0); caret-color: rgb(0, 100, 0); color: rgb(0, 100, 0); column-rule-color: rgb(0, 100, 0); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(0, 100, 0); text-decoration-color: rgb(0, 100, 0); text-emphasis-color: rgb(0, 100, 0); \"\u003e        %%  Insert your code here. I solved this problem in 11 lines inside the function\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.8px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.8px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.8px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.8px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 9px; text-wrap-mode: nowrap; transform-origin: 404px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"border-block-end-color: rgb(14, 0, 255); border-block-start-color: rgb(14, 0, 255); border-bottom-color: rgb(14, 0, 255); border-inline-end-color: rgb(14, 0, 255); border-inline-start-color: rgb(14, 0, 255); border-left-color: rgb(14, 0, 255); border-right-color: rgb(14, 0, 255); border-top-color: rgb(14, 0, 255); caret-color: rgb(14, 0, 255); color: rgb(14, 0, 255); column-rule-color: rgb(14, 0, 255); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(14, 0, 255); text-decoration-color: rgb(14, 0, 255); text-emphasis-color: rgb(14, 0, 255); \"\u003eend\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function foundAll = runTrial(boxes)\r\n  foundAll = true; % Set true only if the hackers find all the numbers\r\n                   % a number of trials equal to 1/2 the length of `boxes`.\r\nend","test_suite":"%% Test code\r\nnumTrials = 10000;\r\nnumBoxes = 100;\r\ntests = false([1,numTrials]);\r\nfor ii=1:numTrials\r\n   boxes = randperm(numBoxes);\r\n   tests(ii) = runTrial(boxes);\r\nend\r\ndisp([\"Number of times the hackers went free: \" nnz(tests)])\r\nproved_algorithm = nnz(tests) \u003e= numTrials * .30;\r\nassessVariableEqual('proved_algorithm', true);\r\ntoo_many_true = nnz(tests) \u003e= numTrials * .35;\r\nassessVariableEqual('too_many_true', false);","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":2842368,"edited_by":2842368,"edited_at":"2026-01-23T15:14:01.000Z","deleted_by":null,"deleted_at":null,"solvers_count":9,"test_suite_updated_at":"2023-10-08T19:02:17.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2023-10-08T18:58:11.000Z","updated_at":"2026-06-04T21:37:00.000Z","published_at":"2023-10-08T19:02:17.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"heading\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe hacker parole problem\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e100 hackers have been imprisoned, but have been given a way to get parole.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEach of the 100 prisoners has been given a number.  There is also a room with 100 numbered boxes.  Each box contains a number.  Each prisoner goes into the room and has 50 tries to find their number in a box.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf all 100 prisoners can find their number within 50 tries, then they all get parole.  But, if one prisoner cannot find their number within 50 tries all the hackers go back to prison for another year.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe hackers meet and devise an algorithm that has better than a 30 percent chance of going free that year.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"heading\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe hacker's solution\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIt is obvious that having each prisoner randomly open boxes will fail.  Each prisoner has a 0.5 chance of finding their number and so the chance that all 100 are able to do it is 0.5^100.  Essentially giving a 0 probablility.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe hacker solution is to have each prisoner go into the room and use their number to find a box.  They open the box, and if their number is in it they are done.  If their number is not in it, then they use the number in the box to choose another box.  They continue following the numbers and opening boxes until they find the box with their number on it.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis solution will free all the prisoners over 30% of the time.  This is because the random numbers in boxes form loops.  For example if we have five boxes and five number we might see this set of numbers stored in boxes.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\left(\\\\begin{array}{cccccc} 1 \u0026amp; 2 \u0026amp; 3 \u0026amp; 4 \u0026amp; 5\\\\\\\\ 3 \u0026amp; 5 \u0026amp; 4 \u0026amp; 1 \u0026amp; 2 \\\\end{array}\\\\right)'\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe hacker holding number 3 would open box 3 and find a 4.  Then the hacker would open box 4 and find a 1.  Then the hacker would open box 1 and find a 3. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis solution works because numbers stored this way always form at least one loop.  In this case we have the following loops:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e3 -\u0026gt; 4 -\u0026gt; 1 -\u0026gt; 3\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e5 -\u0026gt; 2 -\u0026gt; 5\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe hackers know that they will get their freedom if the longest loop among the 100 boxes is 50 boxes or shorter.  This happens 30% of the time, thus they get their freedom 30% of the time.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"heading\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTesting the solution\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWe want to test the hacker's algorithm by running 10,000 trials and seeing how often the hackers won their freedom.  Math says that the number should be greater than or equal to 3000 times in 10,000 trials.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour assignment is to write a function named \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003erunTrial()\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e that takes a vector of boxes in the room and runs a trial of the hacker's algorithm. It returns whether the hackers found the number after looking at only half the boxes. The boxes are a vector that contains randomized numbers from 1:numBoxes.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe testbench will run your trial 10000 times and capture the number of times the hackers won their freedom. That number should be above 3000.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe function looks like this:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[function foundAll = runTrial(boxes)\\n        %%  Insert your code here. I solved this problem in 11 lines inside the function\\nend]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":59526,"title":"Count paths between corners of a grid that remain on or below the diagonal","description":"Consider motion from the lower left corner of a square grid to the upper right corner. The motion is constrained in two ways: (1) the only legal moves are (0,1), (1,0), and (1,1)—i.e., right, up, and up-and-right between points of the lattice—and (2) the motion must remain on or below the line connecting the starting and finishing corners. For a 2x2 grid, the number of paths is 6, as shown below. \r\nWrite a function to count the legal paths for an x grid. Return the count as a string. Check the last test for banned functions.\r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 273.867px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 136.933px; transform-origin: 407px 136.933px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 383.125px 8px; transform-origin: 383.125px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eConsider motion from the lower left corner of a square grid to the upper right corner. The motion is constrained in two ways: (1) the only legal moves are (0,1), (1,0), and (1,1)—i.e., right, up, and up-and-right between points of the lattice—and (2) the motion must remain on or below the line connecting the starting and finishing corners. For a 2x2 grid, the number of paths is 6, as shown below. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 144.558px 8px; transform-origin: 144.558px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to count the legal paths for an \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.5px 8px; transform-origin: 3.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ex\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 202.242px 8px; transform-origin: 202.242px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e grid. Return the count as a string. Check the last test for banned functions.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 129.867px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 64.9333px; text-align: left; transform-origin: 384px 64.9333px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = Schroeder(n)\r\n  y = factorial(n);\r\nend","test_suite":"%%\r\nn = 0;\r\ny = Schroeder(n);\r\ny_correct = '1';\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 2;\r\ny = Schroeder(n);\r\ny_correct = '6';\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 10;\r\ny = Schroeder(n);\r\ny_correct = '1037718';\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 17;\r\ny = Schroeder(n);\r\ny_correct = '111818026018';\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 23;\r\ny = Schroeder(n);\r\ny_correct = '2832923350929742';\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 53;\r\ny = Schroeder(n);\r\ny_correct = '77073779272885977020023254295915628266';\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 113;\r\ny = Schroeder(n);\r\ny_correct = '214645018815655382073160317691190772088909088782578589367620531418920165400447421666';\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 157;\r\ny = Schroeder(n);\r\ny_correct = '635298195989878453594162293551305562386458118544676764232073734715032717517063580963828995662005206580225562745343802';\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 88;\r\ny = Schroeder(n);\r\nyy = Schroeder(str2num(y(1:3)));\r\nyy_correct = '24528665763748049389685052543171711960129970876829498326068598388698578380240132802889284459385709363425133815992875744127843722179914611505186848090410102929094478101446';\r\nassert(isequal(yy,yy_correct))\r\n\r\n%%\r\nfiletext = fileread('Schroeder.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'assert') || contains(filetext,'java') || contains(filetext,'py'); \r\nassert(~illegal)","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":46909,"edited_by":46909,"edited_at":"2024-01-04T04:53:07.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2024-01-04T04:42:17.000Z","updated_at":"2026-06-04T22:55:55.000Z","published_at":"2024-01-04T04:53:07.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eConsider motion from the lower left corner of a square grid to the upper right corner. The motion is constrained in two ways: (1) the only legal moves are (0,1), (1,0), and (1,1)—i.e., right, up, and up-and-right between points of the lattice—and (2) the motion must remain on or below the line connecting the starting and finishing corners. For a 2x2 grid, the number of paths is 6, as shown below. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to count the legal paths for an \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"n\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003ex\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"n\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e grid. Return the count as a string. 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Sequence","description":"Given a vector x, find the \"counting sequence\" y.\r\n\r\nA counting sequence is formed by \"counting\" the entries in a given sequence.\r\n\r\nFor example, the sequence\r\n\r\n x = 5, 5, 2, 1, 1, 1, 1, 3\r\n\r\ncan be read as\r\n\r\n Two 5's, one 2, four 1's, one 3\r\n\r\nwhich translates to\r\n\r\n y = 2, 5, 1, 2, 4, 1, 1, 3\r\n\r\nSo y is the counting sequence for x.\r\n\r\nFor this problem, all elements in the sequences x and y will be in the range from 1 to 9.\r\n","description_html":"\u003cp\u003eGiven a vector x, find the \"counting sequence\" y.\u003c/p\u003e\u003cp\u003eA counting sequence is formed by \"counting\" the entries in a given sequence.\u003c/p\u003e\u003cp\u003eFor example, the sequence\u003c/p\u003e\u003cpre\u003e x = 5, 5, 2, 1, 1, 1, 1, 3\u003c/pre\u003e\u003cp\u003ecan be read as\u003c/p\u003e\u003cpre\u003e Two 5's, one 2, four 1's, one 3\u003c/pre\u003e\u003cp\u003ewhich translates to\u003c/p\u003e\u003cpre\u003e y = 2, 5, 1, 2, 4, 1, 1, 3\u003c/pre\u003e\u003cp\u003eSo y is the counting sequence for x.\u003c/p\u003e\u003cp\u003eFor this problem, all elements in the sequences x and y will be in the range from 1 to 9.\u003c/p\u003e","function_template":"function y = CountSeq(x)\r\ny = x;\r\nend","test_suite":"%%\r\nx = [5 5 2 1 1 1 1 3];\r\ncorrect = [2 5 1 2 4 1 1 3];\r\nassert(isequal(correct, CountSeq(x)));\r\n\r\n\r\n%%\r\nx = [9];\r\ncorrect = [1 9];\r\nassert(isequal(correct, CountSeq(x)));\r\n\r\n\r\n%%\r\nx = ones(1,9);\r\ncorrect = [9 1];\r\nassert(isequal(correct, CountSeq(x)));\r\n\r\n\r\n%%\r\nx = 1:9;\r\ncorrect = [1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9];\r\nassert(isequal(correct, CountSeq(x)));\r\n\r\n%%\r\nx = [1 2 2 1];\r\ncorrect = [1 1 2 2 1 1];\r\nassert(isequal(correct, CountSeq(x)));\r\n\r\n","published":true,"deleted":false,"likes_count":30,"comments_count":13,"created_by":1,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":2188,"test_suite_updated_at":"2013-03-14T15:22:01.000Z","rescore_all_solutions":false,"group_id":2,"created_at":"2012-01-18T01:00:25.000Z","updated_at":"2026-05-21T21:51:04.000Z","published_at":"2012-01-18T01:00:25.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a vector x, find the \\\"counting sequence\\\" y.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA counting sequence is formed by \\\"counting\\\" the entries in a given sequence.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, the sequence\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ x = 5, 5, 2, 1, 1, 1, 1, 3]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ecan be read as\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Two 5's, one 2, four 1's, one 3]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhich translates to\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ y = 2, 5, 1, 2, 4, 1, 1, 3]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSo y is the counting sequence for x.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor this problem, all elements in the sequences x and y will be in the range from 1 to 9.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"}],"problem_search":{"problems":[{"id":2318,"title":"Combine Cards to make 21","description":"Given between two and six cards, e.g.\r\n\r\n A _ 3 _ 7 _ 6 _ 2\r\n\r\nplace one of the mathematical symbols (+,-,*,/) in the space between each\r\npair of cards to make the equation equal 21.\r\n\r\nThe input will be a string, e.g.\r\n\r\n* 'A3762'\r\n\r\nThe output should be a cell array of mathematical symbols in the order that satisfies the puzzle, e.g. for the string given above, the output would be:\r\n\r\n  {'+-*/'}\r\n\r\nWhich means that A + 3 - 7 * 6 / 2 = 21.\r\n\r\n*  A + 3 = 14\r\n* 14 - 7 = 7\r\n*  7 * 6 = 42\r\n* 42 / 2 = 21\r\n\r\nRules:\r\n\r\n* The Ace can represent either 1 or 11.\r\n* All operations should be performed from left to right\r\n* Cards will be represented by the characters: A,2,3,4,5,6,7,8,9,J,Q,K\r\n","description_html":"\u003cp\u003eGiven between two and six cards, e.g.\u003c/p\u003e\u003cpre\u003e A _ 3 _ 7 _ 6 _ 2\u003c/pre\u003e\u003cp\u003eplace one of the mathematical symbols (+,-,*,/) in the space between each\r\npair of cards to make the equation equal 21.\u003c/p\u003e\u003cp\u003eThe input will be a string, e.g.\u003c/p\u003e\u003cul\u003e\u003cli\u003e'A3762'\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eThe output should be a cell array of mathematical symbols in the order that satisfies the puzzle, e.g. for the string given above, the output would be:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e{'+-*/'}\r\n\u003c/pre\u003e\u003cp\u003eWhich means that A + 3 - 7 * 6 / 2 = 21.\u003c/p\u003e\u003cul\u003e\u003cli\u003eA + 3 = 14\u003c/li\u003e\u003cli\u003e14 - 7 = 7\u003c/li\u003e\u003cli\u003e7 * 6 = 42\u003c/li\u003e\u003cli\u003e42 / 2 = 21\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eRules:\u003c/p\u003e\u003cul\u003e\u003cli\u003eThe Ace can represent either 1 or 11.\u003c/li\u003e\u003cli\u003eAll operations should be performed from left to right\u003c/li\u003e\u003cli\u003eCards will be represented by the characters: A,2,3,4,5,6,7,8,9,J,Q,K\u003c/li\u003e\u003c/ul\u003e","function_template":"function mathSyms = solveBlackjackPuzzle(cards)\r\n  mathSyms  = {'+'};\r\nend","test_suite":"%% Example Case\r\ncards = 'A3762';\r\nmathSymbols = {'+-*/'};\r\nassert(isequal(sort(solveBlackjackPuzzle(cards)), sort(mathSymbols)))\r\n\r\n%%\r\ncards = 'A23QJK';\r\nmathSymbols = {'+-/++'};\r\nassert(isequal(sort(solveBlackjackPuzzle(cards)), sort(mathSymbols)))\r\n\r\n%%\r\ncards = '923';\r\nmathSymbols = {'*+', '-*'};\r\nassert(isequal(sort(solveBlackjackPuzzle(cards)), sort(mathSymbols)))\r\n\r\n%%\r\ncards = 'A23456';\r\nmathSymbols = {'**+++', '*+*-+', '*-+*+', '+++++', '+++-+', '-+/*+', '--+++', '/-*++', '/--+*'};\r\nassert(isequal(sort(solveBlackjackPuzzle(cards)), sort(mathSymbols)))\r\n\r\n%%\r\ncards = 'JQKA';\r\nmathSymbols = {'*/+', '+-+', '-++', '/*+'};\r\nassert(isequal(sort(solveBlackjackPuzzle(cards)), sort(mathSymbols)))\r\n\r\n%%\r\ncards = 'KA';\r\nmathSymbols = {'+'};\r\nassert(isequal(sort(solveBlackjackPuzzle(cards)), sort(mathSymbols)))\r\n\r\n%%\r\ncards = 'AAA';\r\nmathSymbols =  {'+-','-+'};\r\nassert(isequal(sort(solveBlackjackPuzzle(cards)), sort(mathSymbols)))","published":true,"deleted":false,"likes_count":4,"comments_count":4,"created_by":1446,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":43,"test_suite_updated_at":"2014-05-16T13:53:18.000Z","rescore_all_solutions":false,"group_id":15,"created_at":"2014-05-13T15:52:33.000Z","updated_at":"2026-06-17T09:45:09.000Z","published_at":"2014-05-13T15:53:27.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven between two and six cards, e.g.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ A _ 3 _ 7 _ 6 _ 2]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eplace one of the mathematical symbols (+,-,*,/) in the space between each pair of cards to make the equation equal 21.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe input will be a string, e.g.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e'A3762'\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe output should be a cell array of mathematical symbols in the order that satisfies the puzzle, e.g. for the string given above, the output would be:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[{'+-*/'}]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhich means that A + 3 - 7 * 6 / 2 = 21.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA + 3 = 14\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e14 - 7 = 7\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e7 * 6 = 42\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e42 / 2 = 21\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRules:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Ace can represent either 1 or 11.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAll operations should be performed from left to right\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCards will be represented by the characters: A,2,3,4,5,6,7,8,9,J,Q,K\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":61177,"title":"[Master Regular Expression] Unique Email Addresses","description":"Every valid email consists of a local name and a domain name, separated by the '@' sign. Besides lowercase letters, the email may contain one or more '.' or '+'.\r\nFor example, in \"alice@leetcode.com\", \"alice\" is the local name, and \"leetcode.com\" is the domain name.\r\nIf you add periods '.' between some characters in the local name part of an email address, mail sent there will be forwarded to the same address without dots in the local name. Note that this rule does not apply to domain names.\r\nFor example, \"alice.z@leetcode.com\" and \"alicez@leetcode.com\" forward to the same email address.\r\nIf you add a plus '+' in the local name, everything after the first plus sign will be ignored. This allows certain emails to be filtered. Note that this rule does not apply to domain names.\r\nFor example, \"m.y+name@email.com\" will be forwarded to \"my@email.com\".\r\nIt is possible to use both of these rules at the same time.\r\nGiven an array of strings emails where we send one email to each emails[i], return the number of different addresses that actually receive mails.\r\n \r\nExample 1:\r\nInput: emails = [\"test.email+alex@leetcode.com\",\"test.e.mail+bob.cathy@leetcode.com\",\"testemail+david@lee.tcode.com\"] Output: 2 \r\nExplanation: \"testemail@leetcode.com\" and \"testemail@lee.tcode.com\" actually receive mails. \r\n\r\nExample 2:\r\nInput: emails = [\"a@leetcode.com\",\"b@leetcode.com\",\"c@leetcode.com\"] \r\nOutput: 3 \r\n \r\nConstraints:\r\n1 \u003c= emails.length \u003c= 100\r\n1 \u003c= emails[i].length \u003c= 100\r\nemails[i] consist of lowercase English letters, '+', '.' and '@'.\r\nEach emails[i] contains exactly one '@' character.\r\nAll local and domain names are non-empty.\r\nLocal names do not start with a '+' character.\r\nDomain names end with the \".com\" suffix.\r\nDomain names must contain at least one character before \".com\" suffix.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 843.812px; display: block; min-width: 0px; padding-block-start: 0px; padding-inline-start: 2px; padding-left: 2px; padding-top: 0px; perspective-origin: 408px 421.9px; transform-origin: 408px 421.906px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eEvery\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003evalid email\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003econsists of a\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003elocal name\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eand a\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003edomain name\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, separated by the\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e'@'\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003esign. Besides lowercase letters, the email may contain one or more\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e'.'\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eor\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e'+'\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 20.4375px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 391px 10.2125px; transform-origin: 391px 10.2188px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example, in\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\"alice@leetcode.com\"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\"alice\"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eis the\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003elocal name\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, and\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\"leetcode.com\"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eis the\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003edomain name\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf you add periods\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e'.'\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ebetween some characters in the\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003elocal name\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003epart of an email address, mail sent there will be forwarded to the same address without dots in the local name. Note that this rule\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003edoes not apply\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eto\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003edomain names\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 20.4375px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 391px 10.2125px; transform-origin: 391px 10.2188px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\"alice.z@leetcode.com\"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eand\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\"alicez@leetcode.com\"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eforward to the same email address.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf you add a plus\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e'+'\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ein the\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003elocal name\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, everything after the first plus sign\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003ewill be ignored\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. This allows certain emails to be filtered. Note that this rule\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003edoes not apply\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eto\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003edomain names\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 20.4375px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 391px 10.2125px; transform-origin: 391px 10.2188px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\"m.y+name@email.com\"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ewill be forwarded to\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\"my@email.com\"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIt is possible to use both of these rules at the same time.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven an array of strings\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eemails\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ewhere we send one email to each\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eemails[i]\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, return\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ethe number of different addresses that actually receive mails\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample 1:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e emails = [\"test.email+alex@leetcode.com\",\"test.e.mail+bob.cathy@leetcode.com\",\"testemail+david@lee.tcode.com\"] \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e 2 \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExplanation:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \"testemail@leetcode.com\" and \"testemail@lee.tcode.com\" actually receive mails. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample 2:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e emails = [\"a@leetcode.com\",\"b@leetcode.com\",\"c@leetcode.com\"] \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e 3 \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eConstraints:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 163.5px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 391px 81.75px; transform-origin: 391px 81.75px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e1 \u0026lt;= emails.length \u0026lt;= 100\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e1 \u0026lt;= emails[i].length \u0026lt;= 100\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eemails[i]\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003econsist of lowercase English letters,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e'+'\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e'.'\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eand\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e'@'\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eEach\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eemails[i]\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003econtains exactly one\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e'@'\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003echaracter.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAll local and domain names are non-empty.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eLocal names do not start with a\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e'+'\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003echaracter.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eDomain names end with the\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\".com\"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003esuffix.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eDomain names must contain at least one character before\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\".com\"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003esuffix.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function result = solution(emails)\r\n\r\nend","test_suite":"%%\r\nemails = [\"test.email+alex@leetcode.com\" \"test.e.mail+bob.cathy@leetcode.com\" \"testemail+david@lee.tcode.com\"];\r\ncorrect_answer = 2;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"a@leetcode.com\" \"b@leetcode.com\" \"c@leetcode.com\"];\r\ncorrect_answer = 3;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"wjhlbrie@t..j.mrg.com\" \"hwbrvpohlgr+ed@rn.uafx.ix.fmut.com\" \"v.tejo@x+oprw.com\" \"gerpzqhpheaur@pfngovbsx+a...com\" \"phgn+d@eategig.com\" \"ejoaqlqtcyc@d+rj+mhzraz.com\" \"yaqli@pn.bk.com\"];\r\ncorrect_answer = 7;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"gdksgnj@.zcm.c..com\" \"ia+rirqhpz+.bh@+nir.mna+.narn.com\" \"tdmafwenkjn+@v+eawfkimygd.com\"];\r\ncorrect_answer = 3;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"htbv+v+xhaq+@shs+qknwhfev.com\" \"buayr.eqllgo@vn.nmootopsw.com\" \"xp+tqxmaz.@.sghdcvgxj.com\" \".t.sbvxhm+kbxf@.pv..lssiliniy.com\" \"lmogy@+.o+f.com\" \"nfjjsnl+.+r@maard+jybz..com\" \"pieizze@i.qfzj..com\" \"ocdz.oobbf@k+rbhwh+k..com\" \"shfewmgunty..j@gbzyd+ycvyjsdcg.com\"];\r\ncorrect_answer = 9;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"lutiy.imybcj.r@fcqze.ldj.dwmi.com\" \"jhn.cj@.isopja.com\" \"spogtwhtrq@c+cca+qsaqp.com\"];\r\ncorrect_answer = 3;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"w+.jqzvv+u@ok.kkgc+j..com\" \"ngoikkt@iblhmfx.com\" \"kj.qby@tdipvy.com\" \".lbtymy@gj.rwwa.com\" \"ugigzcfwgbfn@vnjoabkxsrvp.com\" \"exudwkules@xrqfp+qqfi..com\" \"fhwhmh@olgc+p.com\" \"mhpmqixa@u.+xtqic.com\" \".ligb.fvvgu@wkhghvyxeka.com\"];\r\ncorrect_answer = 9;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"a.uyfnngdr@rwxpkk.ujz.com\" \".sjpzarzekgp@ya+kridafidw.com\"];\r\ncorrect_answer = 2;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\".ccrxzyiud@ti+mrviezj.com\" \"qelcesbdgqqmlj@+yxfafndxvccan.com\" \"wtyo+l@olhjyj.com\" \"ah.mphqmt@trvfb+.wyz.com\" \"um+xu.mona+x+p@zteq++m+rk+nil.com\" \"dlvvupcrlxwbrjm@fgohf+uwo+uofum.com\" \"uq.hrr@cuarwk.com\" \"ydbghox+kl+noe@boexhfj.ctecwq.com\" \"dlrshw+k.c.w@msuzwzocmhsz.com\"];\r\ncorrect_answer = 9;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"xmdykvtahgyp@lterrniu+.ur.com\" \"qld+wq.ek@oi.mw.hy+.com\" \"tsg+ut@mz++ox.com\" \".trwkzce@lcxmomue.com\" \"t.b+bfpeh.v@lb+uny.gr+i.com\" \"qsy.lno@mzknpk+u.com\" \"hct+uqr@vdcs.et.com\" \"qmiussebdmpfn@wmzn.c.sjgoc..com\"];\r\ncorrect_answer = 8;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"cqqh.avzp@robtj+htf.com\" \"qjdwzhvn@pjlpq++y.com\" \"bmlr+q@htqwupm.com\" \"wpddmnz.txfuvw@xudvlgxnv+zj++.com\" \"xnqtqbhtfz@m+ssz.qyn+.com\"];\r\ncorrect_answer = 5;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\".zmjgh@+sbvhe.com\" \"nh+onxodyfnp@ps.a+fk+of++.com\"];\r\ncorrect_answer = 2;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"emqg+nociysj@ell+.gcndyeg.com\" \"xudupb..dmsl@hx+qxejgvzou.com\" \"gi+yj+vzd@go.shnerp.com\" \"exegawmmybuhj@oynjvqdwmwetc.com\"];\r\ncorrect_answer = 4;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"ivyvy@.koud.com\" \"zlya+d@v+zlxo.com\" \"hfepaczs@lvaqpc+a.com\" \"mkhiihjwgpsh@.coaqlfhrbgz.com\" \"wjvgijfibmzk@.uqfo+u.+mxr.com\" \"bknkbmznylqm@wxt++n+zbm+y.com\" \"gflhomxq@tdmmhksp.com\" \"hknwtdctuun+sa@abxrmyyn+q+jzp.com\"];\r\ncorrect_answer = 8;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"mzo+sjve@gejsbgth.com\" \"hhwdnr@zzvzee.com\" \"hezr+ov@bb.aloi.com\" \"ngzazovqa.nvb+@x+lraofkkqx.ar.com\" \"blup+@+ymca.com\" \"szkznwyoqh@.ot.kkwdql.com\" \"nfkyat+iu@cxasim+z+.com\" \"lqdhzf+.@vms+n+zo.com\" \"fosq.wjbkw+xikw@g.axsoacnrmvusm.com\" \"a+egj.de@byyk+iox.com\"];\r\ncorrect_answer = 10;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"wosbgfbq+iohjs@w+a.wf+h+khs.t.com\" \"yoryltsqgprg+jg@hy.t.qrwdkodwqd.com\" \"dcknj@e.r+i.com\"];\r\ncorrect_answer = 3;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"nrzlyiewbu@sb.+peednq.com\" \"zj.pre.agf+soq@udki.emvlyzujz.com\" \"h+aeeap.iiocu.@.+z.oglfkpxsbu.com\" \"h+yhm@fvzki.com\"];\r\ncorrect_answer = 4;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"koguyqhghscjiob@xi+.r+hlx+tcxqw.com\" \"fdqgr.tqgjlyyra@mtbvrcqenvkibqg.com\" \"bpaxe.lo.hrydqy@tru+b.irfstinxy.com\"];\r\ncorrect_answer = 3;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"ncsyteko@qfxr+++dm.com\" \".qdh.hz@gvecrk+.com\" \".j+hb@.sol+.com\"];\r\ncorrect_answer = 3;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"vgqkvatdx@qrdsolaz..com\" \"xei+++osalaqm@xxeeqqjgsnsao.com\"];\r\ncorrect_answer = 2;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"onutdg@eqbhng.com\" \"rsptijxxsnadwn@v+hdlhhpliptgn.com\" \"kuofap@lq+qdg.com\" \"btfpitlmblpnr@+cb.uzrxk.drv.com\" \"vxolxcm+rwlq@..nbwpgxjy+..com\"];\r\ncorrect_answer = 5;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"h.npduspt+xt@.gm+trja+gvy.com\" \"eekvae++kjs@pr.txifzbdi.com\" \"n.+syybgkgg@jivviajknfl.com\" \"j++zrdfoc..uj@ishji..po.sed.com\"];\r\ncorrect_answer = 4;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"oslxuvd+j+ppdtj@+++akbe.kjoukhz.com\" \".fgnbscrtpc.j+@.+hgpqbbwourp+.com\" \"y+sh.bf.n@xh+sm+krf.com\"];\r\ncorrect_answer = 3;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"rkfqxlo.y.+@ibeduayh.i+.com\" \"xcuiy.au@vmfwel...com\" \"kkx..lwnuw@eueutajdrs.com\" \"v+oqoufiqmdzo@lgroppjdjbehe.com\" \"d.znfhymi..ybu@zhcsbqguazgwhc.com\" \"r+ulbtcjs@m++o+cfa..com\" \"aajyp+dyuyxnw.@eyjiyhgxjlarpa.com\"];\r\ncorrect_answer = 7;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"dp.+vp+k@kaagwxkb.com\" \"r.lhe.o@kjvljpa.com\" \"vxhjyeu+llbdd@wz+cfo+pgbtmg.com\" \"nrv+p@lwmsz.com\" \"pylevc@wv.zjd.com\"];\r\ncorrect_answer = 5;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"pniur@pb.iu.com\" \"jadg.@uk+bp.com\" \"vrdq.hlhmu.f.@uvlysycaayckm.com\" \"glffs+ezwlhxjl@+nobjdaejgtlwfy.com\" \"rjeay@vx+ji.com\" \"movemhf+rjdu+b@v.w++fvrlpyg+t.com\" \"nrj.kjwhdrd@pqhrhiqnzbf.com\" \"qbkn.@ugotg.com\"];\r\ncorrect_answer = 8;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"d+mhas.tsgqorpw@srjrbeqzfngvhcc.com\" \".wd.nycikhwpio@uluma+y.zptdvxo.com\" \"ewfu+c.cpkgxqi@cpkwcquwmhfjlk.com\" \"rtojs..@yxpal.a.com\" \"ftogpu@wy.zpf.com\" \".qjrog++yxy@te.i+nttbbi.com\" \"skqac@wbpfw.com\" \".yus+rotyed@+yvfccxjtxs.com\" \"znfa.tlseed@+sjidvzaufr.com\" \"eocdtookevafe@.vejunakxmwmu.com\"];\r\ncorrect_answer = 10;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"nnazmvkxsqseu@owlov+y++sh++.com\" \"mcd+.zaoqvjt@mshwokdsnbpa.com\" \"rvgxh@uepgp.com\" \"pyxs.tccxm@hwqtesq+ac.com\" \"nkohjc.rdoom@lnwnahosijdx.com\" \"squpelgagh@fysvplojt..com\" \"svr.thd.akqo@pnvu++nc.g+..com\" \"atx+qtgps@krecrkgco.com\" \"hfbngnwgsjdy@csxah+ujrhbf.com\" \".gblz+gv+@+ccoudafz.com\"];\r\ncorrect_answer = 10;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"lumrfllp@ycgw..+y.com\" \"e+meeqfiv+sgdb@kfatozbkmjqnwu.com\" \"oopl.tztedjk@vwnwy+sbdwj..com\"];\r\ncorrect_answer = 3;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\".hy.flt@oduitys.com\" \"r+kyc@q+pkp.com\" \".htrhfbptj@febgrmels+.com\" \"cgiy+aoodkoyuad@tmczkzjutq.rmp..com\" \"nrcbiwxcjg@atvxzuuya..com\" \"yy.ica+vy@evulrlsul.com\" \"kxdu.xgqr@qkehuqjzl.com\" \"cdiyt++@mpb.jos.com\" \"jvx+opucdmj@zpsgwhusqw..com\"];\r\ncorrect_answer = 9;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"vvtjpnbwceyz@gdcgjaxiiiqo.com\" \"dxb+pfbibxfm@cxk.fqegt+w+s.com\" \"xlxiq.sdffx@iczdyroddo..com\" \"udvnp.@bzldti.com\" \"myvdwpwk+@pxahvnofh.com\" \".i+.t@srhjn.com\" \"q.nt+cmrxj@jfefhhvqyn.com\" \"g.bjes.m@+m+ncvfw.com\" \"dcumhljkpg+@bkinkypk.o+.com\" \".rmsmixyyiyn@mw+vozgvkev..com\"];\r\ncorrect_answer = 10;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"kbzkwnexjd+j+f@i+.ttkuphkfuf..com\" \"vzz+.mgo@nofllmhs.com\" \".iodt@recpp.com\" \"wfjixhqgtx+xk@tbcu.mfcc.mmq.com\" \"olk.ivns.tt++@tvgfits.ighr+.com\" \"eehxp+cj+onn+ku@lxbdwjocmfjlxnu.com\" \"doycs.zkeqwiywn@pe+c.dwo+mgtyzi.com\" \"n+voq@ietp+.com\" \".zkwm.gredn@umj+uhugnza.com\"];\r\ncorrect_answer = 9;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"iyexebhw.iau+hl@wlkxwcizawtsgxk.com\" \"fqdruepre.swxnb@jaqjnp.dvfiveug.com\" \"i+vwklzdm@xpeejkmlp..com\" \"qpla+@.vmbz.com\" \"pg+etmaqi+wae@rrxhpqk.frevh.com\" \"bdfqqoejbcb@qprhwxqzgmd.com\" \".en+qpx.dtkg@aapkhyixcvzm.com\"];\r\ncorrect_answer = 7;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"luffyuctpfb@lcrcudc+oas.com\" \"liedcyn@skgq.ny.com\" \"vdiuwqvnbbt@zomxloeqbu+.com\" \"dei.yrqq+tjb@+nrvays+bj+x.com\" \"wd..bxdxxamf@rtphghmicffc.com\" \"g+emmrm.au+t@rpl.sv+gvfnk.com\" \"uenakj.jtvg@ldduvmakrpg+.com\" \"nt+zofb@rhmc.oq.com\" \"yosrqs@exesju.com\" \"kehogbp.nol@+bxlsqqj.fg.com\"];\r\ncorrect_answer = 10;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n%%\r\nemails = [\"xamepf@m+ie...com\" \"blwpot@evqzxa.com\" \"ibyuogiqh@hwhangzte.com\" \"rdri.la.emkpa@guupavcgqsnq..com\" \"fxtes@cxxir.com\" \"krwy..x++i.exba@jhniby.cxy+f..v.com\" \"abgobuyol@rsexxbwlm.com\" \"jnalpyhai@bccsuj.af.com\"];\r\ncorrect_answer = 8;\r\nyour_answer = solution(emails)\r\nassert(isequal(correct_answer, your_answer))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":4945898,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":15,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2026-01-31T14:56:56.000Z","updated_at":"2026-06-20T18:01:56.000Z","published_at":"2026-01-31T14:56:56.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEvery\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003evalid email\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003econsists of a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003elocal name\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eand a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edomain name\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, separated by the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e'@'\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003esign. Besides lowercase letters, the email may contain one or more\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e'.'\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eor\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e'+'\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, in\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e\\\"alice@leetcode.com\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e\\\"alice\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eis the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003elocal name\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e\\\"leetcode.com\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eis the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edomain name\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf you add periods\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e'.'\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ebetween some characters in the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003elocal name\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003epart of an email address, mail sent there will be forwarded to the same address without dots in the local name. Note that this rule\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edoes not apply\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eto\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edomain names\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e\\\"alice.z@leetcode.com\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eand\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e\\\"alicez@leetcode.com\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eforward to the same email address.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf you add a plus\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e'+'\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ein the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003elocal name\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, everything after the first plus sign\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ewill be ignored\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. This allows certain emails to be filtered. Note that this rule\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edoes not apply\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eto\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edomain names\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e\\\"m.y+name@email.com\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ewill be forwarded to\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e\\\"my@email.com\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIt is possible to use both of these rules at the same time.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven an array of strings\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eemails\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ewhere we send one email to each\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eemails[i]\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, return\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ethe number of different addresses that actually receive mails\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample 1:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e emails = [\\\"test.email+alex@leetcode.com\\\",\\\"test.e.mail+bob.cathy@leetcode.com\\\",\\\"testemail+david@lee.tcode.com\\\"] \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e 2 \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExplanation:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \\\"testemail@leetcode.com\\\" and \\\"testemail@lee.tcode.com\\\" actually receive mails. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample 2:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e emails = [\\\"a@leetcode.com\\\",\\\"b@leetcode.com\\\",\\\"c@leetcode.com\\\"] \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e 3 \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eConstraints:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e1 \u0026lt;= emails.length \u0026lt;= 100\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e1 \u0026lt;= emails[i].length \u0026lt;= 100\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eemails[i]\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003econsist of lowercase English letters,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e'+'\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e'.'\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eand\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e'@'\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEach\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eemails[i]\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003econtains exactly one\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e'@'\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003echaracter.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAll local and domain names are non-empty.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eLocal names do not start with a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e'+'\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e 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The person who was unlucky enough to draw the short straw would be executed by the other nine members of his group.\r\n\r\nThe bloodthirsty Roman Centurion Carnage Maximus decided to apply this to his prisoners, with a few gruesome differences.  Rather than kill every tenth prisoner and allow the rest to live, he is going to leave only one prisoner alive and kill all of the others.  Instead of killing every tenth prisoner, he chooses a number (kill_every).  If kill_every=3, he kills every third prisoner.  If kill_every=5, he kills every fifth prisoner.  He always chooses a number between 2 and the number of prisoners he has, and this process will be repeated until there is only one prisoner left.  For example, if there are 10 prisoners, and kill_every=3\r\n\r\nFirst iteration: 1 2 3 4 5 6 7 8 9 10\r\n\r\n1-2-3 4-5-6 7-8-9 10\r\n\r\nPrisoners 3, 6 and 9 will be killed.\r\n\r\nSecond iteration: 1 2 4 5 7 8 10\r\n\r\nBecause Prisoner 10 was counted during the first iteration, the executions\r\nwill proceed as such: 10-1-2 4-5-7 8-10, so prisoners 2 and 7 will be killed\r\n\r\nThird iteration: 1 4 5 8 10\r\n8-10-1 4-5-8 10, so prisoners 1 and 8 executed.\r\n\r\nFourth Iteration:  10-4-5 10\r\nPrisoner 5 is executed.\r\n\r\nFifth iteration:  10-4 10\r\nPrisoner 10 is executed\r\n\r\nSince the sole survivor is prisoner 4, he is released.\r\n\r\nYou are an unlucky prisoner caught by Carnage Maximum.  Prior to lining up the prisoners, he reveals the number of prisoners he has and his value of kill_every for the day.  Your job is to figure out which prisoner you need to be in order to survive.  Write a MATLAB script that takes the values of num_prisoners and kill_every.  The output will be survivor, which is the position of the person who survives.  If you write your script quickly enough, that person will be you.\r\n\r\nGood luck!","description_html":"\u003cp\u003eWhen dealing to the Roman Army, the term decimate meant that the entire unit would be broken up into groups of ten soldiers, and lots would be drawn.  The person who was unlucky enough to draw the short straw would be executed by the other nine members of his group.\u003c/p\u003e\u003cp\u003eThe bloodthirsty Roman Centurion Carnage Maximus decided to apply this to his prisoners, with a few gruesome differences.  Rather than kill every tenth prisoner and allow the rest to live, he is going to leave only one prisoner alive and kill all of the others.  Instead of killing every tenth prisoner, he chooses a number (kill_every).  If kill_every=3, he kills every third prisoner.  If kill_every=5, he kills every fifth prisoner.  He always chooses a number between 2 and the number of prisoners he has, and this process will be repeated until there is only one prisoner left.  For example, if there are 10 prisoners, and kill_every=3\u003c/p\u003e\u003cp\u003eFirst iteration: 1 2 3 4 5 6 7 8 9 10\u003c/p\u003e\u003cp\u003e1-2-3 4-5-6 7-8-9 10\u003c/p\u003e\u003cp\u003ePrisoners 3, 6 and 9 will be killed.\u003c/p\u003e\u003cp\u003eSecond iteration: 1 2 4 5 7 8 10\u003c/p\u003e\u003cp\u003eBecause Prisoner 10 was counted during the first iteration, the executions\r\nwill proceed as such: 10-1-2 4-5-7 8-10, so prisoners 2 and 7 will be killed\u003c/p\u003e\u003cp\u003eThird iteration: 1 4 5 8 10\r\n8-10-1 4-5-8 10, so prisoners 1 and 8 executed.\u003c/p\u003e\u003cp\u003eFourth Iteration:  10-4-5 10\r\nPrisoner 5 is executed.\u003c/p\u003e\u003cp\u003eFifth iteration:  10-4 10\r\nPrisoner 10 is executed\u003c/p\u003e\u003cp\u003eSince the sole survivor is prisoner 4, he is released.\u003c/p\u003e\u003cp\u003eYou are an unlucky prisoner caught by Carnage Maximum.  Prior to lining up the prisoners, he reveals the number of prisoners he has and his value of kill_every for the day.  Your job is to figure out which prisoner you need to be in order to survive.  Write a MATLAB script that takes the values of num_prisoners and kill_every.  The output will be survivor, which is the position of the person who survives.  If you write your script quickly enough, that person will be you.\u003c/p\u003e\u003cp\u003eGood luck!\u003c/p\u003e","function_template":"function survivor=decimate(num_prisoners,kill_every)\r\nsurvivor=4;\r\nend","test_suite":"%%\r\nassert(isequal(decimate(10,3),4))\r\n%%\r\nassert(isequal(decimate(1024,3),676))\r\n%%\r\nassert(isequal(decimate(2012,50),543))\r\n%%\r\nassert(isequal(decimate(30,5),3))\r\n%%\r\nassert(isequal(decimate(10,10),8))\r\n%%\r\nassert(isequal(decimate(2048,2),1))","published":true,"deleted":false,"likes_count":20,"comments_count":12,"created_by":1615,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":322,"test_suite_updated_at":"2012-12-04T21:28:04.000Z","rescore_all_solutions":false,"group_id":13,"created_at":"2012-12-04T19:47:49.000Z","updated_at":"2026-06-11T14:10:13.000Z","published_at":"2012-12-04T19:53:55.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWhen dealing to the Roman Army, the term decimate meant that the entire unit would be broken up into groups of ten soldiers, and lots would be drawn. The person who was unlucky enough to draw the short straw would be executed by the other nine members of his group.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe bloodthirsty Roman Centurion Carnage Maximus decided to apply this to his prisoners, with a few gruesome differences. Rather than kill every tenth prisoner and allow the rest to live, he is going to leave only one prisoner alive and kill all of the others. Instead of killing every tenth prisoner, he chooses a number (kill_every). If kill_every=3, he kills every third prisoner. If kill_every=5, he kills every fifth prisoner. He always chooses a number between 2 and the number of prisoners he has, and this process will be repeated until there is only one prisoner left. For example, if there are 10 prisoners, and kill_every=3\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFirst iteration: 1 2 3 4 5 6 7 8 9 10\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e1-2-3 4-5-6 7-8-9 10\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrisoners 3, 6 and 9 will be killed.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSecond iteration: 1 2 4 5 7 8 10\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBecause Prisoner 10 was counted during the first iteration, the executions will proceed as such: 10-1-2 4-5-7 8-10, so prisoners 2 and 7 will be killed\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThird iteration: 1 4 5 8 10 8-10-1 4-5-8 10, so prisoners 1 and 8 executed.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFourth Iteration: 10-4-5 10 Prisoner 5 is executed.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFifth iteration: 10-4 10 Prisoner 10 is executed\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSince the sole survivor is prisoner 4, he is released.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou are an unlucky prisoner caught by Carnage Maximum. Prior to lining up the prisoners, he reveals the number of prisoners he has and his value of kill_every for the day. Your job is to figure out which prisoner you need to be in order to survive. Write a MATLAB script that takes the values of num_prisoners and kill_every. The output will be survivor, which is the position of the person who survives. If you write your script quickly enough, that person will be you.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGood luck!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44374,"title":"Tautology","description":"Check if the given expression is always true. For example, the sentence\r\n\r\n  '~(A \u0026 B) == (~A | ~B)'\r\n\r\nis always true.\r\n\r\nCharacters in the input sequences may include *~ \u0026 | == ( )*, whitespace, 0 for false, 1 for true and letters for variables.","description_html":"\u003cp\u003eCheck if the given expression is always true. For example, the sentence\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e'~(A \u0026 B) == (~A | ~B)'\r\n\u003c/pre\u003e\u003cp\u003eis always true.\u003c/p\u003e\u003cp\u003eCharacters in the input sequences may include \u003cb\u003e~ \u0026 | == ( )\u003c/b\u003e, whitespace, 0 for false, 1 for true and letters for variables.\u003c/p\u003e","function_template":"function y = tautology(x)\r\n  y = true;\r\nend","test_suite":"%%\r\nx = '0';\r\ny_correct = false;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = '1';\r\ny_correct = true;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = 'A';\r\ny_correct = false;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = '~A';\r\ny_correct = false;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = 'A|1';\r\ny_correct = true;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = '1\u0026A';\r\ny_correct = false;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = 'A|B';\r\ny_correct = false;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = 'A\u0026B';\r\ny_correct = false;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = 'A|A';\r\ny_correct = false;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = 'A|~A';\r\ny_correct = true;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = '0==0';\r\ny_correct = true;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = '~0';\r\ny_correct = true;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = '~(A \u0026 B) == (~A | ~B)';\r\ny_correct = true;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = '~(Z \u0026 Y) == (~Y | ~Z)';\r\ny_correct = true;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = 'A|B|C|D|E|F|G|H|I|J|K|L|M|N|O|P|Q|R|S|T|U|X|V|W|Y|Z';\r\ny_correct = false;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nx = 'A|B|C|D|E|F|G|H|I|J|K|L|M|~A|O|P|Q|R|S|T|U|X|V|W|Y|Z';\r\ny_correct = true;\r\nassert(isequal(tautology(x),y_correct))\r\n%%\r\nassert(isequal(tautology('(A|B)|C'),false));\r\n%%\r\nassert(isequal(tautology('(A|B)|(C == C)'),true));\r\n%%\r\nassert(isequal(tautology('(A == B)|(B == C)|(C == A)'),true));\r\n%%\r\nassert(isequal(tautology('~(~(~(~(~(~(0))))))'),false)); \r\n%%\r\nassert(isequal(tautology('~(~(~(~(~(~(~0))))))'),true));\r\n% provided by Alfonso:\r\nassert(isequal(tautology('((0\u00261)|~B)\u0026~B'),false)); \r\n%%\r\nassert(isequal(tautology('((0\u0026~B)\u0026~B)'),false)); \r\n%%\r\nassert(isequal(tautology('((0|A)\u0026~A)'),false)); \r\n%%\r\nassert(isequal(tautology('((0|A)|1)'),true)); \r\n%%\r\nassert(isequal(tautology('((0|~B)|1)'),true)); \r\n%%\r\nassert(isequal(tautology('((1\u00260)|B)'),false)); \r\n%%\r\nassert(isequal(tautology('((1\u00261)\u0026A)'),false)); \r\n%%\r\nassert(isequal(tautology('((1|0)|A)'),true)); \r\n%%\r\nassert(isequal(tautology('((1|A)|0)'),true)); \r\n%%\r\nassert(isequal(tautology('((1|~A)\u0026B)'),false)); \r\n%%\r\nassert(isequal(tautology('((A\u00261)|~A)|A'),true)); \r\n%%\r\nassert(isequal(tautology('((A\u0026~A)\u0026~B)|~A'),false)); \r\n%%\r\nassert(isequal(tautology('((A\u0026~B)\u00261)|B'),false)); \r\n%%\r\nassert(isequal(tautology('((A|0)\u00261)\u0026~B'),false)); \r\n%%\r\nassert(isequal(tautology('((A|A)\u0026A)|~A'),true)); \r\n%%\r\nassert(isequal(tautology('((B|0)\u0026B)'),false)); \r\n%%\r\nassert(isequal(tautology('((B|1)\u0026B)\u0026A'),false)); \r\n%%\r\nassert(isequal(tautology('((B|A)|~A)'),true)); \r\n%%\r\nassert(isequal(tautology('((~A\u0026~A)\u00260)\u0026B'),false)); \r\n%%\r\nassert(isequal(tautology('((~A\u0026~A)|0)'),false)); \r\n%%\r\nassert(isequal(tautology('((~A\u0026~A)|~A)|1'),true)); \r\n%%\r\nassert(isequal(tautology('((~A|A)|~B)\u00261'),true)); \r\n%%\r\nassert(isequal(tautology('((~A|B)|A)'),true)); \r\n%%\r\nassert(isequal(tautology('((~A|~A)|1)'),true)); \r\n%%\r\nassert(isequal(tautology('((~A|~B)\u00260)'),false)); \r\n%%\r\nassert(isequal(tautology('((~B\u00260)\u0026A)'),false)); \r\n%%\r\nassert(isequal(tautology('(0\u00261)|1\u00261'),true)); \r\n%%\r\nassert(isequal(tautology('(0|~A\u0026B)'),false)); \r\n%%\r\nassert(isequal(tautology('(1|A\u00260)'),true)); \r\n%%\r\nassert(isequal(tautology('(A\u0026A\u0026~B)'),false)); \r\n%%\r\nassert(isequal(tautology('(A\u0026~A|1)'),true)); \r\n%%\r\nassert(isequal(tautology('(A|1)|B'),true)); \r\n%%\r\nassert(isequal(tautology('(A|A)|A|1'),true)); \r\n%%\r\nassert(isequal(tautology('(B\u00261)|~B'),true)); \r\n%%\r\nassert(isequal(tautology('(B\u0026~B)\u0026~B\u00260'),false)); \r\n%%\r\nassert(isequal(tautology('(B|~B)|B'),true)); \r\n%%\r\nassert(isequal(tautology('(~A\u0026B\u00260)'),false)); \r\n%%\r\nassert(isequal(tautology('(~A|0)|~B\u0026~A'),false)); \r\n%%\r\nassert(isequal(tautology('(~A|1)|1'),true)); \r\n%%\r\nassert(isequal(tautology('(~A|B\u0026B)'),false)); \r\n%%\r\nassert(isequal(tautology('(~A|B)|~B'),true)); \r\n%%\r\nassert(isequal(tautology('(~A|~A)|0'),false)); \r\n%%\r\nassert(isequal(tautology('(~B\u00260)\u00261|1'),true)); \r\n%%\r\nassert(isequal(tautology('1\u0026B|~B|0'),true)); \r\n%%\r\nassert(isequal(tautology('B\u00261\u0026A\u00261'),false)); \r\n%%\r\nassert(isequal(tautology('~A\u00260\u00261|1'),true)); \r\n%%\r\nassert(isequal(tautology('~B\u00260\u0026~A|B'),false)); \r\n%%\r\nassert(isequal(tautology('~B|1|1|~B'),true)); \r\n%%\r\nassert(isequal(tautology('~B|~B\u00261|1'),true));\r\n%%\r\nassert(isequal(tautology('A==~A'),false));\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":3,"comments_count":30,"created_by":14358,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":45,"test_suite_updated_at":"2017-10-31T07:45:16.000Z","rescore_all_solutions":true,"group_id":35,"created_at":"2017-10-10T23:20:08.000Z","updated_at":"2026-05-25T02:36:10.000Z","published_at":"2017-10-16T01:51:01.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCheck if the given expression is always true. For example, the sentence\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA['~(A \u0026 B) == (~A | ~B)']]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eis always true.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCharacters in the input sequences may include\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e~ \u0026amp; | == ( )\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, whitespace, 0 for false, 1 for true and letters for variables.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":60486,"title":"Compute Farey sequences","description":"Problem statement\r\nThe Farey sequence of order  consists of fractions between 0 and 1 expressed in reduced form in increasing order and with no denominator greater than . For example, the Farey sequence of order 3 is {0/1, 1/3, 1/2, 2/3, 1/1}. \r\nWrite a function to compute the Farey sequence of order . Put the numerators in the first row of a two-row matrix and denominators in the second row. \r\nFurther comments\r\nFarey sequences are connected to Stern-Brocot trees, but unlike some other problems of mine (e.g., CP 59791 and 60311), this one was not inspired by a problem of minnolina’s. Instead it arose out of reading The Riemann Hypothesis: The Greatest Unsolved Problem in Mathematics by Karl Sabbagh. Geologist John Farey noted, in a half-page 1816 paper in Philosophical Magazine, a relationship between a fraction in the sequence and the fractions to its left and right. In discussing the connection between the Riemann hypothesis and Farey sequences, Sabbagh writes that, mathematician G.H. Hardy “said, somewhat cruelly:\r\nJust once in his life Mr. Farey rose above mediocrity, and made an original observation. He did not understand very well what he was doing, and he was too weak a mathematician to prove the quite simple theorem he had discovered. It is evident also that he did not consider his discovery…at all important…He had obviously no idea that this casual letter was the one event of real importance in his life. We may be tempted to think that Farey was very lucky; but a man who has made an observation that has escaped Fermat and Euler deserves any luck that comes his way.”  \r\nStunned by this mean-spirited statement—which Hardy read in a 1928 lecture in New York City, I decided to write a problem on the sequences named for Farey.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 474px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 237px; transform-origin: 407px 237px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 63.0083px 8px; transform-origin: 63.0083px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eProblem statement\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 92.1833px 8px; transform-origin: 92.1833px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe Farey sequence of order \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 276.958px 8px; transform-origin: 276.958px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e consists of fractions between 0 and 1 expressed in reduced form in increasing order and with no denominator greater than \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 222.075px 8px; transform-origin: 222.075px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. For example, the Farey sequence of order 3 is {0/1, 1/3, 1/2, 2/3, 1/1}. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 176.842px 8px; transform-origin: 176.842px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to compute the Farey sequence of order \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 185.508px 8px; transform-origin: 185.508px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. Put the numerators in the first row of a two-row matrix and denominators in the second row. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 61.45px 8px; transform-origin: 61.45px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eFurther comments\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 126px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 63px; text-align: left; transform-origin: 384px 63px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 109.692px 8px; transform-origin: 109.692px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFarey sequences are connected to \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/60266\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"text-decoration-line: underline; \"\u003eStern-Brocot\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 175.408px 8px; transform-origin: 175.408px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e trees, but unlike some other problems of mine (e.g., CP \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/59791\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"text-decoration-line: underline; \"\u003e59791\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 15.5583px 8px; transform-origin: 15.5583px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/60311\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"text-decoration-line: underline; \"\u003e60311\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 5.65833px 8px; transform-origin: 5.65833px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e), this one was not inspired by a problem of minnolina’s. Instead it arose out of reading \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.525px 8px; transform-origin: 80.525px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eThe Riemann Hypothesis:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.94167px 8px; transform-origin: 1.94167px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 14px 8px; transform-origin: 14px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eThe Greatest Unsolved Problem in Mathematics \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 234.567px 8px; transform-origin: 234.567px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eby Karl Sabbagh. Geologist John Farey noted, in a half-page 1816 paper in \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 73.5417px 8px; transform-origin: 73.5417px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ePhilosophical Magazine\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 278.492px 8px; transform-origin: 278.492px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, a relationship between a fraction in the sequence and the fractions to its left and right. In discussing the connection between the Riemann hypothesis and Farey sequences, Sabbagh writes that, mathematician G.H. Hardy “said, somewhat cruelly:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 126px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 63px; text-align: left; transform-origin: 384px 63px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 384px 8px; transform-origin: 384px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eJust once in his life Mr. Farey rose above mediocrity, and made an original observation. He did not understand very well what he was doing, and he was too weak a mathematician to prove the quite simple theorem he had discovered. It is evident also that he did not consider his discovery…at all important…He had obviously no idea that this casual letter was the one event of real importance in his life. We may be tempted to think that Farey was very lucky; but a man who has made an observation that has escaped Fermat and Euler deserves any luck that comes his way.” \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.85px 8px; transform-origin: 3.85px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; font-style: italic; \"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 384px 8px; transform-origin: 384px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eStunned by this mean-spirited statement—which Hardy read in a 1928 lecture in New York City, I decided to write a problem on the sequences named for Farey.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = Farey(n)\r\n  y = [0 1:n 1; 1 n:-1:1 1]; % Numerators in the first row, denominators in the second\r\nend","test_suite":"%%\r\nn = 1;\r\ny = Farey(n);\r\ny_correct = [0 1; 1 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 3;\r\ny = Farey(n);\r\ny_correct = [0 1 1 2 1; 1 3 2 3 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 5;\r\ny = Farey(n);\r\ny_correct = [0 1 1 1 2 1 3 2 3 4 1; 1 5 4 3 5 2 5 3 4 5 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 8;\r\ny = Farey(n);\r\ny_correct = [0 1 1 1 1 1 2 1 3 2 3 1 4 3 5 2 5 3 4 5 6 7 1; 1 8 7 6 5 4 7 3 8 5 7 2 7 5 8 3 7 4 5 6 7 8 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%% \r\nn = 18;\r\ny = Farey(n);\r\ny_correct = [0 1 1 1 1 1 1 1 1 1 1 2 1 2 1 2 1 3 2 3 1 3 2 3 4 1 4 3 5 2 5 3 4 5 1 6 5 4 3 5 7 2 7 5 3 7 4 5 6 7 8 1 9 8 7 6 5 9 4 7 10 3 11 8 5 7 9 11 2 11 9 7 12 5 13 8 11 3 13 10 7 11 4 13 9 14 5 11 6 13 7 15 8 9 10 11 12 13 14 15 16 17 1; ...\r\n             1 18 17 16 15 14 13 12 11 10 9 17 8 15 7 13 6 17 11 16 5 14 9 13 17 4 15 11 18 7 17 10 13 16 3 17 14 11 8 13 18 5 17 12 7 16 9 11 13 15 17 2 17 15 13 11 9 16 7 12 17 5 18 13 8 11 14 17 3 16 13 10 17 7 18 11 15 4 17 13 9 14 5 16 11 17 6 13 7 15 8 17 9 10 11 12 13 14 15 16 17 18 1];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 81;\r\ny = Farey(n);\r\nf = y(1,:)./y(2,:);\r\nindx73 = find(y==73)';\r\nindx73_correct = [20 102 162 220 276 332 386 446 502 552 608 670 722 776 828 886 940 1006 1052 1104 1158 1214 1274 1340 1380 1440 1494 1552 1614 1662 1720 1772 1828 1886 1942 2012 2032 2102 2158 2216 2272 2324 2382 2430 2492 2550 2604 2664 2704 2770 2830 2886 2940 2992 3038 3104 3158 3216 3268 3322 3374 3436 3492 3542 3598 3641 3658 3693 3712 3735 3768 3787 3824 3835 3882 3889 3942 3945 4024 4025];\r\nwidth_correct = 2021;\r\nsum_correct = [54882; 109764];\r\nnprime_correct = 1648;\r\nassert(all(diff(f)\u003e0))\r\nassert(isequal(indx73,indx73_correct))\r\nassert(isequal(width(y),width_correct))\r\nassert(isequal(sum(y,2),sum_correct))\r\nassert(isequal(length(find(isprime(y))),nprime_correct))\r\n\r\n%%\r\nn = 188;\r\ny = Farey(n);\r\nf = y(1,:)./y(2,:);\r\nindx173 = find(y==173)';\r\nindx173_correct = [34 222 358 490 618 742 870 996 1124 1242 1370 1494 1624 1752 1876 1996 2124 2250 2378 2496 2618 2746 2876 3000 3120 3244 3374 3498 3606 3746 3872 3998 4118 4246 4364 4498 4624 4742 4868 4986 5116 5244 5388 5486 5614 5742 5868 5996 6118 6240 6366 6484 6618 6746 6870 6990 7124 7210 7360 7488 7616 7738 7852 7988 8100 8236 8358 8486 8630 8736 8860 8994 9114 9250 9362 9488 9602 9738 9862 9984 10112 10232 10366 10492 10618 10782 10814 10978 11104 11230 11364 11484 11612 11734 11858 11994 12108 12234 12346 12482 12602 12736 12860 12966 13110 13238 13360 13496 13608 13744 13858 13980 14108 14236 14386 14472 14606 14726 14850 14978 15112 15230 15356 15478 15600 15728 15854 15982 16110 16208 16352 16480 16610 16728 16854 16972 17098 17232 17350 17478 17598 17724 17850 17990 18098 18222 18352 18476 18596 18720 18850 18978 19100 19218 19346 19472 19600 19720 19844 19867 19972 19975 20089 20102 20195 20226 20309 20354 20417 20472 20527 20600 20643 20726 20761 20854 20877 20978 20997 21106 21117 21238 21245 21374 21377 21562 21563];\r\nwidth_correct = 10797;\r\nsum_correct = [678175; 1356350];\r\nnprime_correct = 7550;\r\nassert(all(diff(f)\u003e0))\r\nassert(isequal(indx173,indx173_correct))\r\nassert(isequal(width(y),width_correct))\r\nassert(isequal(sum(y,2),sum_correct))\r\nassert(isequal(length(find(isprime(y))),nprime_correct))\r\n\r\n%%\r\nn = 811;\r\ny = Farey(n);\r\nf = y(1,:)./y(2,:);\r\nindx773 = find(y==773)';\r\nindx773_correct = [80 892 1458 2014 2538 3078 3588 4124 4644 5160 5682 6200 6724 7254 7768 8266 8792 9298 9844 10358 10880 11388 11918 12434 12950 13470 13986 14504 15018 15540 16062 16592 17106 17628 18142 18656 19176 19700 20204 20720 21238 21772 22264 22802 23320 23846 24364 24888 25400 25916 26438 26956 27476 27992 28514 29028 29542 30046 30584 31098 31632 32136 32656 33174 33688 34202 34724 35240 35766 36286 36794 37318 37838 38360 38874 39396 39920 40428 40942 41452 41986 42500 43026 43554 44062 44534 45098 45618 46134 46652 47142 47692 48196 48722 49236 49754 50258 50778 51308 51826 52342 52860 53410 53900 54420 54940 55462 55980 56508 57032 57528 58054 58570 59092 59606 60104 60646 61164 61646 62196 62708 63256 63744 64270 64786 65310 65828 66354 66792 67376 67904 68406 68932 69454 69976 70492 71008 71544 72050 72566 73078 73586 74122 74632 75128 75678 76196 76718 77232 77738 78268 78784 79300 79836 80308 80848 81378 81892 82406 82924 83466 83968 84484 84992 85522 86040 86566 87100 87602 88118 88638 89138 89670 90188 90714 91228 91748 92266 92786 93306 93820 94330 94858 95394 95890 96412 96936 97456 97964 98486 99004 99532 100146 100532 101062 101586 102110 102626 103140 103664 104174 104702 105220 105732 106256 106786 107294 107812 108326 108848 109350 109878 110396 110916 111436 111946 112462 112996 113512 114030 114480 115056 115570 116094 116616 117132 117648 118168 118698 119202 119726 120196 120754 121278 121796 122314 122836 123342 123870 124384 124904 125422 125940 126470 126978 127480 128014 128536 129054 129576 130090 130616 131120 131646 132172 132694 133246 133570 134214 134744 135270 135788 136300 136820 137350 137862 138378 138894 139416 139934 140454 140968 141488 142014 142536 143080 143566 144092 144600 145124 145678 146154 146674 147192 147712 148236 148752 149262 149792 150250 150826 151348 151858 152382 152898 153420 153936 154450 154966 155490 156006 156524 157044 157564 158082 158602 159130 159644 160248 160668 161194 161712 162226 162762 163262 163792 164304 164826 165344 165860 166374 166930 167414 167934 168456 168974 169508 170004 170526 171040 171578 172068 172586 173118 173620 174160 174668 175194 175708 176226 176748 177254 177780 178288 178812 179332 179854 180368 180894 181406 181932 182438 182958 183476 183996 184512 185026 185552 186066 186588 187100 187622 188142 188660 189188 189708 190222 190742 191254 191768 192306 192816 193330 193860 194366 194886 195402 195924 196438 196958 197482 197998 198528 199054 199592 200282 200362 201052 201590 202116 202646 203162 203686 204206 204720 205242 205758 206278 206784 207314 207828 208338 208876 209390 209902 210422 210936 211456 211984 212502 213022 213544 214056 214578 215092 215618 216132 216648 217168 217686 218206 218712 219238 219750 220276 220790 221312 221832 222356 222864 223390 223896 224418 224936 225450 225976 226484 227024 227526 228058 228576 229066 229604 230118 230640 231136 231670 232188 232710 233230 233714 234270 234784 235300 235818 236340 236852 237382 237882 238418 238932 239450 239976 240396 241000 241514 242042 242562 243080 243600 244120 244638 245154 245678 246194 246708 247224 247746 248262 248786 249296 249818 250394 250852 251382 251892 252408 252932 253452 253970 254490 254966 255520 256044 256552 257078 257564 258108 258630 259156 259676 260190 260710 261228 261750 262266 262782 263294 263824 264344 264856 265374 265900 266430 267074 267398 267950 268472 268998 269524 270028 270554 271068 271590 272108 272630 273164 273666 274174 274704 275222 275740 276260 276774 277302 277808 278330 278848 279366 279890 280448 280918 281442 281946 282476 282996 283512 284028 284550 285074 285588 286164 286614 287132 287648 288182 288698 289208 289728 290248 290766 291294 291796 292318 292832 293350 293858 294388 294912 295424 295942 296470 296980 297504 298018 298534 299058 299582 300112 300498 301112 301640 302158 302680 303188 303708 304232 304754 305250 305786 306314 306824 307338 307858 308378 308896 309416 309930 310456 310974 311506 312006 312526 313042 313544 314078 314604 315122 315652 316160 316676 317178 317720 318238 318752 319266 319796 320336 320808 321344 321860 322376 322906 323412 323926 324448 324966 325516 326012 326522 327058 327566 328078 328594 329100 329636 330152 330668 331190 331712 332238 332740 333268 333852 334290 334816 335334 335858 336374 336900 337388 337936 338448 338998 339480 339998 340540 341038 341552 342074 342590 343116 343612 344136 344664 345182 345704 346224 346744 347234 347784 348302 348818 349336 349866 350386 350890 351408 351922 352448 352952 353502 353992 354510 355026 355546 356110 356582 357090 357618 358144 358658 359192 359702 360216 360724 361248 361770 362284 362806 363326 363850 364358 364878 365404 365920 366442 366956 367470 367988 368508 369012 369546 370060 370598 371102 371616 372130 372652 373168 373688 374206 374728 375244 375756 376280 376798 377324 377842 378380 378872 379406 379924 380440 380944 381468 381865 381988 382349 382502 382813 383016 383281 383538 383759 384052 384239 384582 384715 385104 385211 385626 385683 386140 386171 386645 386658 387125 387174 387609 387694 388101 388210 388579 388726 389073 389256 389561 389764 390045 390286 390541 390800 391039 391346 391549 391852 392043 392378 392537 392876 393019 393390 393517 393920 394035 394444 394535 394962 395045 395484 395547 396000 396055 396520 396563 397056 397091 397566 397589 398106 398125 398630 398641 399186 399193 399752 399755 400564 400565];\r\nwidth_correct = 200321;\r\nsum_correct = [54206832; 108413664];\r\nnprime_correct = 112653;\r\nassert(all(diff(f)\u003e0))\r\nassert(isequal(indx773,indx773_correct))\r\nassert(isequal(width(y),width_correct))\r\nassert(isequal(sum(y,2),sum_correct))\r\nassert(isequal(length(find(isprime(y))),nprime_correct))\r\n\r\n%%\r\nn = randi(134);\r\ny = Farey(n);\r\nm = width(y);\r\nb = y(2,:);\r\nassert(abs(sum(b(1:end-1)./b(2:end))-(3*m-4)/2)\u003c1e-5)\r\nassert(abs(sum(1./(b(1:end-1).*b(2:end)))-1)\u003c1e-5)\r\n\r\n%%\r\nfiletext = fileread('Farey.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'assert'); \r\nassert(~illegal)","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":46909,"edited_by":46909,"edited_at":"2024-06-10T05:07:53.000Z","deleted_by":null,"deleted_at":null,"solvers_count":10,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2024-06-10T05:03:32.000Z","updated_at":"2026-06-05T22:48:45.000Z","published_at":"2024-06-10T05:03:55.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eProblem statement\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Farey sequence of order \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"n\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e consists of fractions between 0 and 1 expressed in reduced form in increasing order and with no denominator greater than \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"n\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. For example, the Farey sequence of order 3 is {0/1, 1/3, 1/2, 2/3, 1/1}. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to compute the Farey sequence of order \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"n\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. Put the numerators in the first row of a two-row matrix and denominators in the second row. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eFurther comments\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFarey sequences are connected to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/60266\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eStern-Brocot\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e trees, but unlike some other problems of mine (e.g., CP \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/59791\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e59791\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/60311\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e60311\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e), this one was not inspired by a problem of minnolina’s. Instead it arose out of reading \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThe Riemann Hypothesis:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThe Greatest Unsolved Problem in Mathematics \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eby Karl Sabbagh. Geologist John Farey noted, in a half-page 1816 paper in \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ePhilosophical Magazine\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, a relationship between a fraction in the sequence and the fractions to its left and right. In discussing the connection between the Riemann hypothesis and Farey sequences, Sabbagh writes that, mathematician G.H. Hardy “said, somewhat cruelly:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eJust once in his life Mr. Farey rose above mediocrity, and made an original observation. He did not understand very well what he was doing, and he was too weak a mathematician to prove the quite simple theorem he had discovered. It is evident also that he did not consider his discovery…at all important…He had obviously no idea that this casual letter was the one event of real importance in his life. We may be tempted to think that Farey was very lucky; but a man who has made an observation that has escaped Fermat and Euler deserves any luck that comes his way.” \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eStunned by this mean-spirited statement—which Hardy read in a 1928 lecture in New York City, I decided to write a problem on the sequences named for Farey.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":364,"title":"Matrix spiral","description":"Make a spiral in a (n*n) matrix. The spiral has to start in the top left, and has to rotate clockwise to the center. The spiral has to have a padding of zeros between itself.\r\n\r\nThe (n*n) matrix is filled with zeros except for the spiral, that has to be made of 11 (elevens, for visual reference).\r\nThe final matrix has to have the same or more zeros than elevens.\r\n\r\nExample:\r\nn=8\r\n\r\nA =\r\n\r\n    11    11    11    11    11    11    11    11\r\n     0     0     0     0     0     0     0    11\r\n     0     0    11    11    11    11     0    11\r\n     0     0    11     0     0    11     0    11\r\n     0     0    11     0    11    11     0    11\r\n     0     0    11     0     0     0     0    11\r\n     0     0    11    11    11    11    11    11\r\n     0     0     0     0     0     0     0     0","description_html":"\u003cp\u003eMake a spiral in a (n*n) matrix. The spiral has to start in the top left, and has to rotate clockwise to the center. The spiral has to have a padding of zeros between itself.\u003c/p\u003e\u003cp\u003eThe (n*n) matrix is filled with zeros except for the spiral, that has to be made of 11 (elevens, for visual reference).\r\nThe final matrix has to have the same or more zeros than elevens.\u003c/p\u003e\u003cp\u003eExample:\r\nn=8\u003c/p\u003e\u003cp\u003eA =\u003c/p\u003e\u003cpre\u003e    11    11    11    11    11    11    11    11\r\n     0     0     0     0     0     0     0    11\r\n     0     0    11    11    11    11     0    11\r\n     0     0    11     0     0    11     0    11\r\n     0     0    11     0    11    11     0    11\r\n     0     0    11     0     0     0     0    11\r\n     0     0    11    11    11    11    11    11\r\n     0     0     0     0     0     0     0     0\u003c/pre\u003e","function_template":"function A = Matrix_Spiral(n)\r\n  A = n;\r\nend","test_suite":"%%\r\nn = 1;\r\ny_correct =[0];\r\nassert(isequal(Matrix_Spiral(n),y_correct))\r\n\r\n%%\r\nn = 2;\r\ny_correct =[11    11\r\n     0     0];\r\nassert(isequal(Matrix_Spiral(n),y_correct))\r\n\r\n%%\r\nn = 3;\r\ny_correct =[11    11    11\r\n     0     0    11\r\n     0     0     0];\r\nassert(isequal(Matrix_Spiral(n),y_correct))\r\n\r\n%%\r\nn = 4;\r\ny_correct =[11    11    11    11\r\n     0     0     0    11\r\n     0     0    11    11\r\n     0     0     0     0];\r\nassert(isequal(Matrix_Spiral(n),y_correct))\r\n\r\n\r\n%%\r\nn = 5;\r\ny_correct =[11    11    11    11    11\r\n     0     0     0     0    11\r\n     0     0    11     0    11\r\n     0     0    11    11    11\r\n     0     0     0     0     0];\r\nassert(isequal(Matrix_Spiral(n),y_correct))\r\n\r\n%%\r\nn = 10;\r\ny_correct =[11    11    11    11    11    11    11    11    11    11\r\n     0     0     0     0     0     0     0     0     0    11\r\n     0     0    11    11    11    11    11    11     0    11\r\n     0     0    11     0     0     0     0    11     0    11\r\n     0     0    11     0    11    11     0    11     0    11\r\n     0     0    11     0    11     0     0    11     0    11\r\n     0     0    11     0    11    11    11    11     0    11\r\n     0     0    11     0     0     0     0     0     0    11\r\n     0     0    11    11    11    11    11    11    11    11\r\n     0     0     0     0     0     0     0     0     0     0];\r\nassert(isequal(Matrix_Spiral(n),y_correct))\r\n\r\n\r\n%%\r\nn = 17;\r\ny_correct =[11    11    11    11    11    11    11    11    11    11    11    11    11    11    11    11    11\r\n     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0    11\r\n     0     0    11    11    11    11    11    11    11    11    11    11    11    11    11     0    11\r\n     0     0    11     0     0     0     0     0     0     0     0     0     0     0    11     0    11\r\n     0     0    11     0    11    11    11    11    11    11    11    11    11     0    11     0    11\r\n     0     0    11     0    11     0     0     0     0     0     0     0    11     0    11     0    11\r\n     0     0    11     0    11     0    11    11    11    11    11     0    11     0    11     0    11\r\n     0     0    11     0    11     0    11     0     0     0    11     0    11     0    11     0    11\r\n     0     0    11     0    11     0    11     0    11     0    11     0    11     0    11     0    11\r\n     0     0    11     0    11     0    11     0    11    11    11     0    11     0    11     0    11\r\n     0     0    11     0    11     0    11     0     0     0     0     0    11     0    11     0    11\r\n     0     0    11     0    11     0    11    11    11    11    11    11    11     0    11     0    11\r\n     0     0    11     0    11     0     0     0     0     0     0     0     0     0    11     0    11\r\n     0     0    11     0    11    11    11    11    11    11    11    11    11    11    11     0    11\r\n     0     0    11     0     0     0     0     0     0     0     0     0     0     0     0     0    11\r\n     0     0    11    11    11    11    11    11    11    11    11    11    11    11    11    11    11\r\n     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0     0];\r\nassert(isequal(Matrix_Spiral(n),y_correct))","published":true,"deleted":false,"likes_count":5,"comments_count":3,"created_by":872,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":98,"test_suite_updated_at":"2012-02-20T12:29:13.000Z","rescore_all_solutions":false,"group_id":18,"created_at":"2012-02-20T12:17:08.000Z","updated_at":"2026-04-28T18:06:26.000Z","published_at":"2012-02-20T12:29:13.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMake a spiral in a (n*n) matrix. The spiral has to start in the top left, and has to rotate clockwise to the center. The spiral has to have a padding of zeros between itself.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe (n*n) matrix is filled with zeros except for the spiral, that has to be made of 11 (elevens, for visual reference). The final matrix has to have the same or more zeros than elevens.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample: n=8\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA =\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[    11    11    11    11    11    11    11    11\\n     0     0     0     0     0     0     0    11\\n     0     0    11    11    11    11     0    11\\n     0     0    11     0     0    11     0    11\\n     0     0    11     0    11    11     0    11\\n     0     0    11     0     0     0     0    11\\n     0     0    11    11    11    11    11    11\\n     0     0     0     0     0     0     0     0]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":52664,"title":"List the Moran numbers","description":"The quotient of a Moran number and its digit sum is prime. For example, 117 and 481 are Moran numbers because 117/(1+1+7) is 13 and 481/(4+8+1) = 37, and both 13 and 37 are prime. \r\nWrite a function to list the Moran numbers less than or equal to the input number. ","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 72px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 36px; transform-origin: 407px 36px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 363px 8px; transform-origin: 363px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe quotient of a Moran number and its digit sum is prime. For example, 117 and 481 are Moran numbers because 117/(1+1+7) is 13 and 481/(4+8+1) = 37, and both 13 and 37 are prime. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 257px 8px; transform-origin: 257px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to list the Moran numbers less than or equal to the input number. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = Moran(n)\r\n  y = f(n);\r\nend","test_suite":"%%\r\nn = 500;\r\ny = Moran(n);\r\ny_correct = [18 21 27 42 45 63 84 111 114 117 133 152 153 156 171 190 195 198 201 207 209 222 228 247 261 266 285 333 370 372 399 402 407 423 444 465 481];\r\nassert(isequal(y,y_correct))\r\n\r\n%% \r\nn = 40332;\r\ny = Moran(n);\r\ny23_correct = [207 1679 3749 4577 8717 14099 18653 19067 22793 24449 25691 26519 26933 29417 29831 32729 33557 35627 37283];\r\nassert(isequal(y(mod(y,23)==0),y23_correct) \u0026\u0026 isequal(y(end),n))\r\n\r\n%%\r\nn = [100000 400000 700000 1e6 4e6 7e6 1e7];\r\ns = [383 1193 1870 2451 8080 12913 17271];\r\nlen_correct = [1915 5967 9352 12259 40403 64567 86356];\r\nsum_correct = [79699686 1044807776 2880495403 5339917218 73480226594 205122929098 389309242207];\r\nsd_correct  = [2.925215086021406e+04 1.171076738381341e+05 2.065163622127620e+05 2.944277010513903e+05 1.177431499460555e+06 2.057551640570258e+06 2.933705654924581e+06];\r\nys_correct  = [11354 28489 48992 71660 99972; 51489 125203 210051 300165 399477; 96325 220734 364473 524186 699739; 129627 308214 513837 741778 999219; 579189 1331117 2176042 3062214 3999644; 1046322 2330397 3782883 5322552 6999255; 1440693 3292137 5341677 7565613 9999882];\r\nfor k = 1:length(n)\r\n    disp(['Test 3.' num2str(k)])\r\n    y = Moran(n(k));\r\n    assert(isequal(length(y),len_correct(k)) \u0026\u0026 isequal(sum(y),sum_correct(k)) \u0026\u0026 abs(std(y)-sd_correct(k))\u003c1e-7 \u0026\u0026 isequal(y(s(k):s(k):end),ys_correct(k,:)));\r\nend\r\n\r\n%%\r\nfiletext = fileread('Moran.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'regexp') || contains(filetext, 'oeis') || contains(filetext, 'persistent'); \r\nassert(~illegal)","published":true,"deleted":false,"likes_count":3,"comments_count":2,"created_by":46909,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":25,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-09-05T13:52:35.000Z","updated_at":"2026-06-04T06:39:54.000Z","published_at":"2021-09-05T14:10:51.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe quotient of a Moran number and its digit sum is prime. For example, 117 and 481 are Moran numbers because 117/(1+1+7) is 13 and 481/(4+8+1) = 37, and both 13 and 37 are prime. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to list the Moran numbers less than or equal to the input number. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":42503,"title":"Generating random matrix with given probability mass function","description":"Inspired by \u003chttp://www.mathworks.com/matlabcentral/cody/problems/2356-simulating-the-selection-of-a-state-with-given-probabilities Problem 2356. Simulating the selection of a state with given probabilities\u003e, let's consider a similar yet more useful problem. Write a function\r\n\r\n                             x = rndsampling(m,n,prob)\r\n\r\nto generate an m-by-n matrix x, whose entries are drawn independently from integer symbols 1:numel(prob) according to the given probability mass function prob. Specifically, symbol k occurs with probability prob(k), k = 1, 2, ..., numel(prob), where all(prob\u003e0) == 1 and sum(prob) == 1.","description_html":"\u003cp\u003eInspired by \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/2356-simulating-the-selection-of-a-state-with-given-probabilities\"\u003eProblem 2356. Simulating the selection of a state with given probabilities\u003c/a\u003e, let's consider a similar yet more useful problem. Write a function\u003c/p\u003e\u003cpre\u003e                             x = rndsampling(m,n,prob)\u003c/pre\u003e\u003cp\u003eto generate an m-by-n matrix x, whose entries are drawn independently from integer symbols 1:numel(prob) according to the given probability mass function prob. Specifically, symbol k occurs with probability prob(k), k = 1, 2, ..., numel(prob), where all(prob\u0026gt;0) == 1 and sum(prob) == 1.\u003c/p\u003e","function_template":"function x = rndsampling(m,n,prob);\r\n  x = rand(m,n)\r\nend","test_suite":"%%\r\nrnd = sort(rand(randi([10,20]),1));\r\nprob = vertcat(rnd(1,:),diff(rnd,1,1),1-rnd(end,:));\r\nsz = [1 1e5;1e5 1;1e3 1e2;randi([100 200], 100, 2)];\r\nsz = sz(randi(size(sz,1)),:);\r\nx = rndsampling(sz(1),sz(2),prob);\r\nprob_est = histcounts(x,1:numel(prob)+1,'Normalization','probability').';\r\nerr = mean(abs(prob_est - prob))\r\nassert(err \u003c 0.005 \u0026\u0026 isequal(size(x),sz) \u0026\u0026 all(~isnan(x(:))));\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":1,"created_by":12569,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":132,"test_suite_updated_at":"2015-08-13T18:44:59.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2015-08-11T19:26:49.000Z","updated_at":"2026-05-14T19:22:17.000Z","published_at":"2015-08-11T19:26:49.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInspired by\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/2356-simulating-the-selection-of-a-state-with-given-probabilities\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 2356. Simulating the selection of a state with given probabilities\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, let's consider a similar yet more useful problem. Write a function\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[                             x = rndsampling(m,n,prob)]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eto generate an m-by-n matrix x, whose entries are drawn independently from integer symbols 1:numel(prob) according to the given probability mass function prob. Specifically, symbol k occurs with probability prob(k), k = 1, 2, ..., numel(prob), where all(prob\u0026gt;0) == 1 and sum(prob) == 1.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":196,"title":"love is an n-letter word","description":"Given a list of *N words*, return the *N-letter word* (choosing one letter from each word) with the property of having the least distance between each pair of two consecutive letters (if there are multiple optimal solutions return any one of them).\r\n\r\nExample: s1 = {'abcd','bcde','cdef','defg'}; should return s2 = 'dddd'; (with total letter-distance = 0)\r\n\r\nExample: s1={'aldfejk','czoa','vwy','abcde'}; should return s2='love'; (with total letter-distance = 27: _|'l'-'o'|_=3 + _|'o'-'v'|_=7 + _|'v'-'e'|_=17 ; compare for example to the possible word 'aave' which has a total letter-distance of 38)\r\n\r\n\r\n\r\n","description_html":"\u003cp\u003eGiven a list of \u003cb\u003eN words\u003c/b\u003e, return the \u003cb\u003eN-letter word\u003c/b\u003e (choosing one letter from each word) with the property of having the least distance between each pair of two consecutive letters (if there are multiple optimal solutions return any one of them).\u003c/p\u003e\u003cp\u003eExample: s1 = {'abcd','bcde','cdef','defg'}; should return s2 = 'dddd'; (with total letter-distance = 0)\u003c/p\u003e\u003cp\u003eExample: s1={'aldfejk','czoa','vwy','abcde'}; should return s2='love'; (with total letter-distance = 27: \u003ci\u003e\u003ctt\u003e'l'-'o'\u003c/tt\u003e\u003c/i\u003e=3 + \u003ci\u003e\u003ctt\u003e'o'-'v'\u003c/tt\u003e\u003c/i\u003e=7 + \u003ci\u003e\u003ctt\u003e'v'-'e'\u003c/tt\u003e\u003c/i\u003e=17 ; compare for example to the possible word 'aave' which has a total letter-distance of 38)\u003c/p\u003e","function_template":"function s2 = gobbledigook(s1)\r\n  s2 = '';\r\nend","test_suite":"%%\r\ns1 = {'abcd','bcde','cdef','defg'}; \r\nassert(isequal(gobbledigook(s1),'dddd'))\r\ns2_correct = 'dddd';\r\n%%\r\ns1 = {'aldfejk','czoa','vwy','abcde'}; \r\nassert(isequal(gobbledigook(s1),'love'))\r\ns2_correct = 'love';\r\n%%\r\ns1 = {'some','help','check','viterbi','algorithm'}; \r\nassert(isequal(gobbledigook(s1),'eeeeg'))\r\ns2_correct = 'eeeeg';\r\n%%\r\ns1 = {'ldjfac','deamv','fka','idlw','pqmfjavs'}; \r\nassert(isequal(gobbledigook(s1),'lmklm')|isequal(gobbledigook(s1),'aaadf'))\r\ns2_correct = 'lmklm';\r\ns2_correct = 'aaadf';\r\n%% \r\n% avoids look-up table hack\r\ns1 = cellfun(@(x)char('a'-1+randi(26,1,5)),cell(1,7),'uniformoutput',false);\r\nassert(all(any(bsxfun(@eq,gobbledigook(s1),cell2mat(cellfun(@(x)x',s1,'uniformoutput',false)))))\u0026all(sum(abs(diff(double(gobbledigook(s1)))))\u003c=sum(abs(diff(double(cell2mat(cellfun(@(x)x(randi(numel(x),1,1000))',s1,'uniformoutput',false))),1,2)),2)));","published":true,"deleted":false,"likes_count":4,"comments_count":5,"created_by":43,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":65,"test_suite_updated_at":"2012-03-08T02:36:04.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2012-01-31T08:36:36.000Z","updated_at":"2026-06-10T20:34:10.000Z","published_at":"2012-03-08T03:14:35.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a list of\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN words\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, return the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN-letter word\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (choosing one letter from each word) with the property of having the least distance between each pair of two consecutive letters (if there are multiple optimal solutions return any one of them).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample: s1 = {'abcd','bcde','cdef','defg'}; should return s2 = 'dddd'; (with total letter-distance = 0)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample: s1={'aldfejk','czoa','vwy','abcde'}; should return s2='love'; (with total letter-distance = 27:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e'l'-'o'\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e=3 +\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e'o'-'v'\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e=7 +\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e'v'-'e'\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e=17 ; compare for example to the possible word 'aave' which has a total letter-distance of 38)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44345,"title":"MATLAB Counter","description":"Write a function f = counter(x0,b) to construct a counter handle f that counts with an initial value x0 and a step size b. \r\n\r\nE.g.,\r\n\r\n  \u003e\u003e f = counter(0,1)  % Initialize a counter f() with initial_count = 0 and step_size = 1\r\n  \u003e\u003e f()\r\n  ans =\r\n       0\r\n  \u003e\u003e f()\r\n  ans =\r\n       1\r\n  \u003e\u003e f()\r\n  ans =\r\n       2\r\n\r\n\r\n","description_html":"\u003cp\u003eWrite a function f = counter(x0,b) to construct a counter handle f that counts with an initial value x0 and a step size b.\u003c/p\u003e\u003cp\u003eE.g.,\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e\u0026gt;\u0026gt; f = counter(0,1)  % Initialize a counter f() with initial_count = 0 and step_size = 1\r\n\u0026gt;\u0026gt; f()\r\nans =\r\n     0\r\n\u0026gt;\u0026gt; f()\r\nans =\r\n     1\r\n\u0026gt;\u0026gt; f()\r\nans =\r\n     2\r\n\u003c/pre\u003e","function_template":"function y = counter(x,b)\r\n  y = x;\r\nend","test_suite":"%%\r\nassessFunctionAbsence({'regexp','regexpi','regexprep','str2num'},'FileName','counter.m')\r\n\r\n%%\r\nf = counter(0,1);\r\nassert(isequal(f(),0))\r\nassert(isequal(f(),1))\r\nassert(isequal(2,f()))\r\nassert(isequal(3,f()))\r\n\r\n%%\r\nf = counter(1,0);\r\nassert(isequal(f(),1))\r\nassert(isequal(f(),1))\r\nassert(isequal(1,f()))\r\nassert(isequal(1,f()))\r\n\r\n%%\r\nf = counter(10,2);\r\nassert(isequal(f(),10))\r\nassert(isequal(f(),12))\r\nassert(isequal(14,f()))\r\nassert(isequal(16,f()))\r\n\r\n%%\r\nf = counter(0,5);\r\ny_correct = [0, 5, 10, 15, 20, 55];\r\nassert(isequal([f() f() f() f() f() f()+f()],y_correct))\r\n\r\n%%\r\nx0 = randi(10);\r\nb = randi(10);\r\nf = counter(x0,b);\r\ny_correct = x0 + (0:1000)*b;\r\nassert(isequal(arrayfun(@(n)f(),0:1000),y_correct))","published":true,"deleted":false,"likes_count":23,"comments_count":9,"created_by":12569,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":305,"test_suite_updated_at":"2017-10-17T00:19:49.000Z","rescore_all_solutions":false,"group_id":34,"created_at":"2017-09-24T01:58:21.000Z","updated_at":"2026-05-28T04:25:58.000Z","published_at":"2017-10-16T01:45:08.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function f = counter(x0,b) to construct a counter handle f that counts with an initial value x0 and a step size b.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eE.g.,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[\u003e\u003e f = counter(0,1)  % Initialize a counter f() with initial_count = 0 and step_size = 1\\n\u003e\u003e f()\\nans =\\n     0\\n\u003e\u003e f()\\nans =\\n     1\\n\u003e\u003e f()\\nans =\\n     2]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":1241,"title":"PACMAT  - Ghosts maximize unique locations; 3 Lives","description":"The Classic PACMAN game brought to Cody.\r\n\r\nPACMAT Ghosts Random requires clearing the board of Yellow Dots while not bumping into the wandering ghosts in 3 lives. Adjacent Ghosts will capture PACMAT.  Ghosts do not use the tunnel. On Ghost capture everyone gets reset.\r\n\r\n\u003c\u003chttps://sites.google.com/site/razapor/matlab_cody/PACMAT_300.jpg\u003e\u003e\r\n\r\nTo aid in development of your routine, a PACMAT_Ghosts_002.m file that creates a video has been posted at \u003chttps://sites.google.com/site/razapor/matlab_cody/PACMAT_Ghosts_002.m PACMAT_Ghosts_002.m\u003e. (Right click, 'save link as'). Using patches thus enable/figure  disable/video for best results.\r\n\r\n\r\n\u003chttps://sites.google.com/site/razapor/matlab_cody/PACMAT_G002_video_ANC.mp4 Alfonso's Enhanced Ghost Avoider\u003e (MP4) Quite an impressive solution\r\n\r\n\r\nThe reference solution uses Alfonso's Local Optimum Algorithm with a Catch.\r\n\r\n*Inputs:* Map   Definitions: -1=Wall, 0=Empty, 1=Dot, 2=PACMAT, \u003e2=Ghost\r\n\r\n*Output:* Direction  Definitions: 1-Up, 2-Right, 3-Down, 4-Left, 0-No move\r\n\r\n*Scoring:* Total # of Moves to Clear the Yellow Dots +(LivesRemaining-3)*100\r\n\r\n\r\n*Near Future:* Ghosts with LOS Tracking.\r\n\r\n*Future:* Player will be Team Ghosts versus PACMAT_BOT","description_html":"\u003cp\u003eThe Classic PACMAN game brought to Cody.\u003c/p\u003e\u003cp\u003ePACMAT Ghosts Random requires clearing the board of Yellow Dots while not bumping into the wandering ghosts in 3 lives. Adjacent Ghosts will capture PACMAT.  Ghosts do not use the tunnel. On Ghost capture everyone gets reset.\u003c/p\u003e\u003cimg src=\"https://sites.google.com/site/razapor/matlab_cody/PACMAT_300.jpg\"\u003e\u003cp\u003eTo aid in development of your routine, a PACMAT_Ghosts_002.m file that creates a video has been posted at \u003ca href=\"https://sites.google.com/site/razapor/matlab_cody/PACMAT_Ghosts_002.m\"\u003ePACMAT_Ghosts_002.m\u003c/a\u003e. (Right click, 'save link as'). Using patches thus enable/figure  disable/video for best results.\u003c/p\u003e\u003cp\u003e\u003ca href=\"https://sites.google.com/site/razapor/matlab_cody/PACMAT_G002_video_ANC.mp4\"\u003eAlfonso's Enhanced Ghost Avoider\u003c/a\u003e (MP4) Quite an impressive solution\u003c/p\u003e\u003cp\u003eThe reference solution uses Alfonso's Local Optimum Algorithm with a Catch.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInputs:\u003c/b\u003e Map   Definitions: -1=Wall, 0=Empty, 1=Dot, 2=PACMAT, \u003e2=Ghost\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e Direction  Definitions: 1-Up, 2-Right, 3-Down, 4-Left, 0-No move\u003c/p\u003e\u003cp\u003e\u003cb\u003eScoring:\u003c/b\u003e Total # of Moves to Clear the Yellow Dots +(LivesRemaining-3)*100\u003c/p\u003e\u003cp\u003e\u003cb\u003eNear Future:\u003c/b\u003e Ghosts with LOS Tracking.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFuture:\u003c/b\u003e Player will be Team Ghosts versus PACMAT_BOT\u003c/p\u003e","function_template":"function  [newdir]=pacmat(map)\r\n% 314 move solver if Ghosts do not move\r\n persistent ptr\r\n if isempty(ptr)\r\n  ptr=['bbbbbbbcccbbbbbcccdddddddddddddddddddddddddaaa'...\r\n      'bbbbbaaaaaaaaaaaaaaaaaaaaaaaaadddddcccccccbbbbddddaaabbbbbbbb'...\r\n      'cccbbbdddaaabbbaaaadddddbbbbbccccbbbbbbbbbbbbbbaaaaddddddddddd'...\r\n      'ccccbbbcccdddbbbaaabbbaaaccccccbbbbbaaccdddddccccccccccccccaabbbbbcccddccc'...\r\n      'dddaaaaaaddddddcccbbbcccdddcccdddaaadddaaaddbbbbbaaadddddddddddcccbbccc'];\r\n  ptr=(ptr-'a')+1;\r\n end\r\n  \r\n newdir=ptr(1);\r\n ptr(1)=[];\r\n\r\n% usage of newdir=randi(4) will barely move\r\nend","test_suite":"%%\r\nfeval(@assignin,'caller','score',2000);\r\n%%\r\nmax_moves=2000; % Fixed path expect to succeed by 600 moves\r\n\r\nmap=[...\r\n      repmat('a',1,28);\r\n      'accccccccccccaacccccccccccca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acccccccccccccccccccccccccca';\r\n      'acaaaacaacaaaaaaaacaacaaaaca';\r\n      'acaaaacaacaaaaaaaacaacaaaaca';\r\n      'accccccaaccccaaccccaacccccca';\r\n      'aaaaaacaaaaabaabaaaaacaaaaaa';\r\n      'aaaaaacaaaaabaabaaaaacaaaaaa';\r\n      'aaaaaacaabbbbbbbbbbaacaaaaaa';\r\n      'aaaaaacaabaaabbaaabaacaaaaaa';\r\n      'aaaaaacaabalbbbblabaacaaaaaa';\r\n      'bbbbbbcbbbabbbbbbabbbcbbbbbb';\r\n      'aaaaaacaabalbbbblabaacaaaaaa';\r\n      'aaaaaacaabaaaaaaaabaacaaaaaa';\r\n      'aaaaaacaabbbbbbbbbbaacaaaaaa';\r\n      'aaaaaacaabaaaaaaaabaacaaaaaa';\r\n      'aaaaaacaabaaaaaaaabaacaaaaaa';\r\n      'accccccccccccaacccccccccccca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acaaaacaaaaacaacaaaaacaaaaca';\r\n      'acccaacccccccbdcccccccaaccca';\r\n      'aaacaacaacaaaaaaaacaacaacaaa';\r\n      'aaacaacaacaaaaaaaacaacaacaaa';\r\n      'accccccaaccccaaccccaacccccca';\r\n      'acaaaaaaaaaacaacaaaaaaaaaaca';\r\n      'acaaaaaaaaaacaacaaaaaaaaaaca';\r\n      'acccccccccccccccccccccccccca';\r\n      repmat('a',1,28);];\r\n  \r\n  map=map-'b';\r\n  [nr, nc]=size(map);\r\n\r\n  gmap=map; % Map used by ghosts to simplify PAC Capture\r\n  gmap(15,6)=Inf; %No tunnel ghosts\r\n  gmap(15,26)=Inf;\r\n  gmap(map==-1)=Inf; % walls to Inf\r\n  gmap(map\u003e2)=Inf; % Elim start points as viable moves, quicker box exit\r\n\r\n\r\n  mapdelta=[-1 nr 1 -nr]; % Valid as long as not on an edge\r\n  gmovxy=[0 -1;1 0;0 1;-1 0];\r\n\r\n  tunnel=find(map(:,1)==0); % tunnelptr\r\n  tunnel=[tunnel tunnel+nr*(nc-1)]; % Entrance/Exit Tunnel\r\n\r\n  [pmr, pmc]=find(map==2); % pi 24 row  pj 15 column of map\r\n   ptrpac=find(map==2);\r\n\r\n  ptrpac=find(map==2);\r\n  ptrpac_start=ptrpac;\r\n  ptrg_start=find(map\u003e2);\r\n  map(ptrg_start)=[10 20 30 40];% use deal?\r\n  [gstartx, gstarty]=find(map\u003e2);\r\n  \r\n  lives=3; % Lives\r\n  movepac=0;\r\n\r\nwhile lives \u0026\u0026 any(mod(map(:),10)==1) \u0026\u0026 movepac\u003cmax_moves\r\n movepac=movepac+1;\r\n\r\n [curdir]=pacmat(map);\r\n% if curdir==0,continue;end % Inf loop error\r\n [pmr, pmc]=find(map==2);\r\nif curdir\u003e0\r\n if map(ptrpac+mapdelta(curdir))==-1\r\n  % Do nothing - Ran into a Wall\r\n elseif map(ptrpac+mapdelta(curdir))\u003e2 % ran into ghost\r\n  map(ptrpac)=0; % remove PAC from the board\r\n  lives=lives-1;\r\n  if lives==0,break;end\r\n  % reset the board\r\n  [ptrgx, ptrgy]=find(map\u003e2);\r\n  ptrg=find(map\u003e2);\r\n  map(ptrg)=mod(map(ptrg),10);\r\n  map(ptrpac_start)=2;\r\n  map(ptrg_start)=[10 20 30 40];\r\n  ptrpac=find(map==2);\r\n  continue;\r\n else % legal move\r\n  map(ptrpac)=0; % Eat Dot and clear PAC\r\n  ptrpac=ptrpac+mapdelta(curdir);\r\n  if ptrpac==tunnel(1),ptrpac=tunnel(2)-nr;end\r\n  if ptrpac==tunnel(2),ptrpac=tunnel(1)+nr;end\r\n  map(ptrpac)=2;\r\n end\r\nend % curdir \u003e0\r\n\r\n% Ghosts\r\n for i=1:4\r\n\r\n   gmapT=gmap;\r\n   ptrg=find(map\u003e2); % Find all ghosts\r\n   gmapT(ptrg)=Inf; % Rule out moving onto a ghost\r\n\r\n\r\n  dot=false;\r\n  [gptrx, gptry]=find(map==10*i);\r\n  gidx=find(map==10*i);\r\n  if isempty(gidx)\r\n   [gptrx, gptry]=find(map==10*i+1); % ghost must be on a dot\r\n   gidx=find(map==10*i+1);\r\n   dot=true;\r\n  end\r\n\r\n% Find valid ghost moves using gmap\r\n% mapdelta=[-1 nr 1 -nr]; \r\n  gmov=find(map(gidx+mapdelta)==2); % adjacent to PACMAT\r\n  if ~isempty(gmov) % PAC adjacent\r\n   lives=lives-1;\r\n   if lives==0,break;end\r\n   % reset the board\r\n   [pmr, pmc]=find(map==2); % PACMAT erase coords\r\n   map(map==2)=0;\r\n      \r\n   [ptrgx, ptrgy]=find(map\u003e2);\r\n   ptrg=find(map\u003e2);\r\n   map(ptrg)=mod(map(ptrg),10);\r\n   map(ptrpac_start)=2;\r\n   map(ptrg_start)=[10 20 30 40];\r\n   ptrpac=find(map==2);     \r\n   break; % Ghost move loop\r\n      \r\n  else % gmap/gmapT avoids tunnel,other ghosts, Walls\r\n \r\n   gmap(gidx)=gmap(gidx)+1;\r\n   ghost_adj=gmapT(gidx+mapdelta);\r\n   if min(ghost_adj)\u003cInf\r\n    if rand\u003c0.5 % Push ghosts away from each other\r\n     gmov=find(ghost_adj==min(ghost_adj),1,'first');\r\n    else\r\n     gmov=find(ghost_adj==min(ghost_adj),1,'last');\r\n    end\r\n   else\r\n    gmov=[];\r\n   end\r\n \r\n   if ~isempty(gmov) % valid g move : ghost may not stand on ghost\r\n    map(gptrx,gptry)=mod(map(gptrx,gptry),10);\r\n    map(gidx+mapdelta(gmov))=map(gidx+mapdelta(gmov))+10*i;     \r\n   end % ~isempty(gmov) standard move - no capture\r\n\r\n  end % ~isempty(gmov) PACMAT adjacent\r\n  \r\n end % i ghost moves\r\nend % while alive\r\n\r\nfprintf('moves %i\\n',movepac)\r\n\r\nassert(lives\u003e0,sprintf('Three Captures\\n'))\r\nassert(~isempty(any(mod(map(:),10)==1)),sprintf('Moves\\n',movepac)) % Test Move Timeout\r\n\r\n\r\nfeval( @assignin,'caller','score',floor(min( 2000,300-100*lives+movepac )) );\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":15,"test_suite_updated_at":"2013-02-02T05:09:50.000Z","rescore_all_solutions":false,"group_id":33,"created_at":"2013-02-02T00:36:11.000Z","updated_at":"2026-04-23T18:03:04.000Z","published_at":"2013-02-02T01:21:05.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.JPEG\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Classic PACMAN game brought to Cody.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePACMAT Ghosts Random requires clearing the board of Yellow Dots while not bumping into the wandering ghosts in 3 lives. Adjacent Ghosts will capture PACMAT. Ghosts do not use the tunnel. On Ghost capture everyone gets reset.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo aid in development of your routine, a PACMAT_Ghosts_002.m file that creates a video has been posted at\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://sites.google.com/site/razapor/matlab_cody/PACMAT_Ghosts_002.m\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ePACMAT_Ghosts_002.m\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. (Right click, 'save link as'). Using patches thus enable/figure disable/video for best results.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://sites.google.com/site/razapor/matlab_cody/PACMAT_G002_video_ANC.mp4\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eAlfonso's Enhanced Ghost Avoider\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e (MP4) Quite an impressive solution\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe reference solution uses Alfonso's Local Optimum Algorithm with a Catch.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInputs:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Map Definitions: -1=Wall, 0=Empty, 1=Dot, 2=PACMAT, \u0026gt;2=Ghost\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Direction Definitions: 1-Up, 2-Right, 3-Down, 4-Left, 0-No move\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eScoring:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Total # of Moves to Clear the Yellow Dots +(LivesRemaining-3)*100\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eNear Future:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Ghosts with LOS Tracking.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eFuture:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Player will be Team Ghosts versus PACMAT_BOT\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" 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\"}]}"},{"id":81,"title":"Mandelbrot Numbers","description":"The \u003chttp://en.wikipedia.org/wiki/Mandelbrot_set Mandelbrot Set\u003e is built around a simple iterative equation.\r\n\r\n z(1)   = c\r\n z(n+1) = z(n)^2 + c\r\n\r\nFor any complex c, we can continue this iteration until either\r\nabs(z(n+1)) \u003e 2 or n == lim, then return the iteration count n.\r\n\r\n* If c = 0   and lim = 3, then z = [0 0 0] and n = 3.\r\n* If c = 1   and lim = 5, then z = [1 2], and n = length(z) or 2.\r\n* If c = 0.5 and lim = 5, then z = [0.5000 0.7500 1.0625 1.6289] and n = 4.\r\n\r\nFor a matrix of complex numbers C, return a corresponding matrix N such\r\nthat each element of N is the iteration count n for each complex number c\r\nin the matrix C, subject to the iteration count limit of lim.\r\n\r\nIf C = [0 0.5; 1 4] and lim = 5, then N = [5 4; 2 1]\r\n\r\nCleve Moler has a whole chapter on the Mandelbrot set in his book Experiments\r\nwith MATLAB: \u003chttp://www.mathworks.com/moler/exm/chapters/mandelbrot.pdf \r\nChapter 10, Mandelbrot Set (PDF)\u003e","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 296.167px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 148.083px; transform-origin: 407px 148.083px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 12.5px 8px; transform-origin: 12.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"http://en.wikipedia.org/wiki/Mandelbrot_set\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eMandelbrot Set\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 133px 8px; transform-origin: 133px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is built around a simple iterative equation.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 40.8667px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 20.4333px; transform-origin: 404px 20.4333px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 44px 8.5px; transform-origin: 44px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e z(1)   = c\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 80px 8.5px; transform-origin: 80px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e z(n+1) = z(n)^2 + c\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 379.5px 8px; transform-origin: 379.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor any complex c, we can continue this iteration until either abs(z(n+1)) \u0026gt; 2 or n == lim, then return the iteration count n.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 61.3px; counter-reset: list-item 0; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 30.65px; transform-origin: 391px 30.65px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 142.5px 8px; transform-origin: 142.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf c = 0 and lim = 3, then z = [0 0 0] and n = 3.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 176.5px 8px; transform-origin: 176.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf c = 1 and lim = 5, then z = [1 2], and n = length(z) or 2.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 226.5px 8px; transform-origin: 226.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf c = 0.5 and lim = 5, then z = [0.5000 0.7500 1.0625 1.6289] and n = 4.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 377px 8px; transform-origin: 377px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor a matrix of complex numbers C, return a corresponding matrix N such that each element of N is the iteration count n for each complex number c in the matrix C, subject to the iteration count limit of lim.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 149.5px 8px; transform-origin: 149.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf C = [0 0.5; 1 4] and lim = 5, then N = [5 4; 2 1]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 127px 8px; transform-origin: 127px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eCleve Moler has a whole chapter on the \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/content/dam/mathworks/mathworks-dot-com/moler/exm/chapters/mandelbrot.pdf\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eMandelbrot set\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 38px 8px; transform-origin: 38px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e in his book \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/moler/exm/chapters.html\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eExperiments with MATLAB\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function N = mandelbrot(C,lim)\r\n  N = ones(size(C));\r\nend","test_suite":"%%\r\nC = 0;\r\nlim = 5;\r\nN_correct = 5;\r\nassert(isequal(mandelbrot(C,lim),N_correct))\r\n\r\n%%\r\nC = [0 0.5; 1 4];\r\nlim = 5;\r\nN_correct = [5 4; 2 1];\r\nassert(isequal(mandelbrot(C,lim),N_correct))\r\n\r\n%%\r\ni = sqrt(-1);\r\nC = [i 1 -2*i -2];\r\nlim = 10;\r\nN_correct = [10 2 1 10];\r\nassert(isequal(mandelbrot(C,lim),N_correct))","published":true,"deleted":false,"likes_count":17,"comments_count":9,"created_by":1,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":1781,"test_suite_updated_at":"2012-01-26T03:21:20.000Z","rescore_all_solutions":false,"group_id":2,"created_at":"2012-01-18T01:00:28.000Z","updated_at":"2026-05-05T05:05:17.000Z","published_at":"2012-01-18T01:00:28.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Mandelbrot_set\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMandelbrot Set\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is built around a simple iterative equation.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ z(1)   = c\\n z(n+1) = z(n)^2 + c]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor any complex c, we can continue this iteration until either abs(z(n+1)) \u0026gt; 2 or n == lim, then return the iteration count n.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf c = 0 and lim = 3, then z = [0 0 0] and n = 3.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf c = 1 and lim = 5, then z = [1 2], and n = length(z) or 2.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf c = 0.5 and lim = 5, then z = [0.5000 0.7500 1.0625 1.6289] and n = 4.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor a matrix of complex numbers C, return a corresponding matrix N such that each element of N is the iteration count n for each complex number c in the matrix C, subject to the iteration count limit of lim.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf C = [0 0.5; 1 4] and lim = 5, then N = [5 4; 2 1]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCleve Moler has a whole chapter on the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/content/dam/mathworks/mathworks-dot-com/moler/exm/chapters/mandelbrot.pdf\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eMandelbrot set\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e in his book \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/moler/exm/chapters.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eExperiments with MATLAB\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":1740,"title":"Find number patterns in rows of a matrix...","description":"So I think this may be a hard one, may be it was only hard for me!  So here is what it is, I will give a matrix, with some number of rows by 5 columns, to make it simpler I will not repeat any number in a *single row*, so all numbers in a *single rows* are unique. So you need to find up to 2, 3 or 4 numbers that are found in a *single row* that repeat in *other rows* and the amount of times they repeat in a given matrix. To make it a little better, you only need to display the rows/numbers that repeat at lease twice or more. Examples are the best way to show what to do:\r\n  \r\n          %p is the given matrix\r\n          p = [1 2 3 4 5;\r\n               2 3 4 5 1;\r\n               3 4 6 7 8;\r\n               1 2 4 3 5;\r\n               4 6 3 2 1];\r\n          %v is the amount of elements per row that are being checked to see if they repeat in another row\r\n          v = 4;\r\n        %Output:\r\n        out =\r\n        \r\n             1     2     3     4     4\r\n             1     2     3     5     3\r\n             1     2     4     5     3\r\n             1     3     4     5     3\r\n             2     3     4     5     3\r\n      %So the first to the fourth column are the four number that were found to repeat in rows of matrix p (any and all combination that can appear in the rows of the matrix p). The fifth column is the amount of times the that particular set of matrix out(?,1:4) appear in matrix p. for for row one in matrix out, 1,2,3 and 4 in any combination appear 4 time in the rows of matrix p.\r\n      \r\n      So more examples...\r\n      \r\n      p = [1 2 3 4 9;\r\n           2 3 1 4 8]\r\n      v = 4;\r\n      \r\n      out =\r\n           1     2     3     4     2\r\n    %so any combination of 1,2,3 and 4 appear 2 times in matrix p\r\n    \r\n    and another:\r\n    \r\n    p = [1 2 3 4 9;\r\n         2 3 1 4 8;\r\n         3 4 2 7 1]\r\n    v = 3;\r\n      \r\n    out =\r\n         1     2     3     3\r\n         1     2     4     3\r\n         1     3     4     3\r\n         2     3     4     3\r\n    %so this uses any combo of 3 values, so in this case out(?,1:3) are the number combos that can be found in matrix p, and the out(?,4) is the amount of times that those combos are found\r\n    \r\n    last example:\r\n    \r\n    p = [1 2 3 4 9;\r\n         2 3 1 4 8]\r\n    v = 2;\r\n    \r\n    out =\r\n         1     2     2\r\n         1     3     2\r\n         1     4     2\r\n         2     3     2\r\n         2     4     2\r\n         3     4     2\r\n    %so in this case only combos of out(?,1:2) are found and out(?,3) is the amounts of times that combo of out(?,1:2) is found in matrix p. so row four of matrix out (so out(4,:)...) shows that the combo of 2 and 3 are found 2 times in matrix p.\r\n\r\nNOTE: only display any row number combos that occur at least 2 times or more!\r\n\r\nI hope I gave enough instructions and explanation, if not please let me know and I will add to it! Thanks and good luck!","description_html":"\u003cp\u003eSo I think this may be a hard one, may be it was only hard for me!  So here is what it is, I will give a matrix, with some number of rows by 5 columns, to make it simpler I will not repeat any number in a \u003cb\u003esingle row\u003c/b\u003e, so all numbers in a \u003cb\u003esingle rows\u003c/b\u003e are unique. So you need to find up to 2, 3 or 4 numbers that are found in a \u003cb\u003esingle row\u003c/b\u003e that repeat in \u003cb\u003eother rows\u003c/b\u003e and the amount of times they repeat in a given matrix. To make it a little better, you only need to display the rows/numbers that repeat at lease twice or more. Examples are the best way to show what to do:\u003c/p\u003e\u003cpre\u003e          %p is the given matrix\r\n          p = [1 2 3 4 5;\r\n               2 3 4 5 1;\r\n               3 4 6 7 8;\r\n               1 2 4 3 5;\r\n               4 6 3 2 1];\r\n          %v is the amount of elements per row that are being checked to see if they repeat in another row\r\n          v = 4;\r\n        %Output:\r\n        out =\u003c/pre\u003e\u003cpre\u003e             1     2     3     4     4\r\n             1     2     3     5     3\r\n             1     2     4     5     3\r\n             1     3     4     5     3\r\n             2     3     4     5     3\r\n      %So the first to the fourth column are the four number that were found to repeat in rows of matrix p (any and all combination that can appear in the rows of the matrix p). The fifth column is the amount of times the that particular set of matrix out(?,1:4) appear in matrix p. for for row one in matrix out, 1,2,3 and 4 in any combination appear 4 time in the rows of matrix p.\u003c/pre\u003e\u003cpre\u003e      So more examples...\u003c/pre\u003e\u003cpre\u003e      p = [1 2 3 4 9;\r\n           2 3 1 4 8]\r\n      v = 4;\u003c/pre\u003e\u003cpre\u003e      out =\r\n           1     2     3     4     2\r\n    %so any combination of 1,2,3 and 4 appear 2 times in matrix p\u003c/pre\u003e\u003cpre\u003e    and another:\u003c/pre\u003e\u003cpre\u003e    p = [1 2 3 4 9;\r\n         2 3 1 4 8;\r\n         3 4 2 7 1]\r\n    v = 3;\u003c/pre\u003e\u003cpre\u003e    out =\r\n         1     2     3     3\r\n         1     2     4     3\r\n         1     3     4     3\r\n         2     3     4     3\r\n    %so this uses any combo of 3 values, so in this case out(?,1:3) are the number combos that can be found in matrix p, and the out(?,4) is the amount of times that those combos are found\u003c/pre\u003e\u003cpre\u003e    last example:\u003c/pre\u003e\u003cpre\u003e    p = [1 2 3 4 9;\r\n         2 3 1 4 8]\r\n    v = 2;\u003c/pre\u003e\u003cpre\u003e    out =\r\n         1     2     2\r\n         1     3     2\r\n         1     4     2\r\n         2     3     2\r\n         2     4     2\r\n         3     4     2\r\n    %so in this case only combos of out(?,1:2) are found and out(?,3) is the amounts of times that combo of out(?,1:2) is found in matrix p. so row four of matrix out (so out(4,:)...) shows that the combo of 2 and 3 are found 2 times in matrix p.\u003c/pre\u003e\u003cp\u003eNOTE: only display any row number combos that occur at least 2 times or more!\u003c/p\u003e\u003cp\u003eI hope I gave enough instructions and explanation, if not please let me know and I will add to it! Thanks and good luck!\u003c/p\u003e","function_template":"function out = findRowPattern(p,v)\r\n  out = [p v];\r\nend","test_suite":"%%\r\np = [1 2 3 4 5;\r\n     2 3 4 5 1;\r\n     3 4 6 7 8;\r\n     1 2 4 3 5;\r\n     4 6 3 2 1];\r\nv = 4;\r\nout = [1 2 3 4 4;\r\n       1 2 3 5 3;\r\n       1 2 4 5 3;\r\n       1 3 4 5 3;\r\n       2 3 4 5 3]\r\nassert(isequal(findRowPattern(p,v),out))\r\n%%\r\np = [1 2 3 4 9;\r\n     2 3 1 4 8]\r\nv = 4;\r\nout = [1 2 3 4 2]\r\nassert(isequal(findRowPattern(p,v),out))\r\n%%\r\np = [1 2 3 4 9;\r\n     2 3 1 4 8;\r\n     3 4 2 7 1]\r\nv = 3;\r\nout = [1 2 3 3;\r\n       1 2 4 3;\r\n       1 3 4 3;\r\n       2 3 4 3]\r\nassert(isequal(findRowPattern(p,v),out))\r\n%%\r\np = [1 2 3 4 9;\r\n     2 3 1 4 8]\r\nv = 2;\r\nout = [1 2 2;\r\n       1 3 2;\r\n       1 4 2;\r\n       2 3 2;\r\n       2 4 2;\r\n       3 4 2]\r\nassert(isequal(findRowPattern(p,v),out))\r\n%%\r\np = [15 23 68 49 88;\r\n     69 58 78 21 35;\r\n     10 23 21 35 88;\r\n     99 58 63 24 10;\r\n     64 28 14 33 58;\r\n     85 69 21 45 55;\r\n     99 24 76 49 33;\r\n     89 69 33 98 21;\r\n     99 10 21 55 58;\r\n     35 68 69 44 21;\r\n     21 69 35 46 33];\r\nv = 3;\r\nout = [21 35 69 3;\r\n     10 58 99 2;\r\n     21 33 69 2]\r\nassert(isequal(findRowPattern(p,v),out))\r\n%%\r\np = [15 23 68 49 88;\r\n     69 58 78 21 35;\r\n     10 23 21 35 88;\r\n     99 58 63 24 10;\r\n     64 28 14 33 58;\r\n     85 69 21 45 55;\r\n     99 24 76 49 33;\r\n     89 69 33 98 21;\r\n     99 10 21 55 58;\r\n     35 68 69 44 21;\r\n     21 69 35 46 33];\r\nv = 2;\r\n\r\nout = [21    69     5;\r\n       21    35     4;\r\n       35    69     3;\r\n       10    21     2;\r\n       10    58     2;\r\n       10    99     2;\r\n       21    33     2;\r\n       21    55     2;\r\n       21    58     2;\r\n       23    88     2;\r\n       24    99     2;\r\n       33    69     2;\r\n       58    99     2]\r\nassert(isequal(findRowPattern(p,v),out))","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":15013,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":12,"test_suite_updated_at":"2013-07-23T17:11:41.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-07-23T16:51:20.000Z","updated_at":"2026-05-27T04:01:28.000Z","published_at":"2013-07-23T17:11:41.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSo I think this may be a hard one, may be it was only hard for me! So here is what it is, I will give a matrix, with some number of rows by 5 columns, to make it simpler I will not repeat any number in a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esingle row\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, so all numbers in a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esingle rows\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e are unique. So you need to find up to 2, 3 or 4 numbers that are found in a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esingle row\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e that repeat in\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eother rows\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and the amount of times they repeat in a given matrix. To make it a little better, you only need to display the rows/numbers that repeat at lease twice or more. Examples are the best way to show what to do:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[          %p is the given matrix\\n          p = [1 2 3 4 5;\\n               2 3 4 5 1;\\n               3 4 6 7 8;\\n               1 2 4 3 5;\\n               4 6 3 2 1];\\n          %v is the amount of elements per row that are being checked to see if they repeat in another row\\n          v = 4;\\n        %Output:\\n        out =\\n\\n             1     2     3     4     4\\n             1     2     3     5     3\\n             1     2     4     5     3\\n             1     3     4     5     3\\n             2     3     4     5     3\\n      %So the first to the fourth column are the four number that were found to repeat in rows of matrix p (any and all combination that can appear in the rows of the matrix p). The fifth column is the amount of times the that particular set of matrix out(?,1:4) appear in matrix p. for for row one in matrix out, 1,2,3 and 4 in any combination appear 4 time in the rows of matrix p.\\n\\n      So more examples...\\n\\n      p = [1 2 3 4 9;\\n           2 3 1 4 8]\\n      v = 4;\\n\\n      out =\\n           1     2     3     4     2\\n    %so any combination of 1,2,3 and 4 appear 2 times in matrix p\\n\\n    and another:\\n\\n    p = [1 2 3 4 9;\\n         2 3 1 4 8;\\n         3 4 2 7 1]\\n    v = 3;\\n\\n    out =\\n         1     2     3     3\\n         1     2     4     3\\n         1     3     4     3\\n         2     3     4     3\\n    %so this uses any combo of 3 values, so in this case out(?,1:3) are the number combos that can be found in matrix p, and the out(?,4) is the amount of times that those combos are found\\n\\n    last example:\\n\\n    p = [1 2 3 4 9;\\n         2 3 1 4 8]\\n    v = 2;\\n\\n    out =\\n         1     2     2\\n         1     3     2\\n         1     4     2\\n         2     3     2\\n         2     4     2\\n         3     4     2\\n    %so in this case only combos of out(?,1:2) are found and out(?,3) is the amounts of times that combo of out(?,1:2) is found in matrix p. so row four of matrix out (so out(4,:)...) shows that the combo of 2 and 3 are found 2 times in matrix p.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNOTE: only display any row number combos that occur at least 2 times or more!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI hope I gave enough instructions and explanation, if not please let me know and I will add to it! Thanks and good luck!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2448,"title":"Back to the future","description":"See test suite\r\n\r\nSee also:\r\nhttp://www.mathworks.com/matlabcentral/cody/problems/2449-back-to-the-future-ii","description_html":"\u003cp\u003eSee test suite\u003c/p\u003e\u003cp\u003eSee also: \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/2449-back-to-the-future-ii\"\u003ehttp://www.mathworks.com/matlabcentral/cody/problems/2449-back-to-the-future-ii\u003c/a\u003e\u003c/p\u003e","function_template":"function y = your_solution(x)\r\n  y = x;\r\nend","test_suite":"%%\r\n% Create necessary files\r\nfid = fopen('myfunction.m', 'w');fprintf(fid, 'function [a, b]=myfunction(), a=rand; b=@()1;');fclose(fid);\r\nrehash;\r\n\r\n% Execute my function 10 times\r\ny=cell(10,1);\r\nfor iter=1:10\r\n[x y{iter}]=myfunction();\r\nend\r\n\r\n% Ouch! I have overwritten the value of \"x\"\r\n% Your task is to recover the value of the 3rd \"x\"\r\n\r\n% Now I am downloading my secret solver\r\nurlwrite('https://github.com/masdeseiscaracteres/files4codygame/blob/master/my_secret_solver.p?raw=true','my_secret_solver.p');\r\nrehash;\r\n\r\n% I compare your solution with my secret solver to see how you have done\r\nif my_secret_solver~=your_solution, fail{-1}, end","published":true,"deleted":false,"likes_count":2,"comments_count":4,"created_by":5925,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":15,"test_suite_updated_at":"2014-07-18T19:16:07.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-07-18T18:11:07.000Z","updated_at":"2026-05-28T03:13:43.000Z","published_at":"2014-07-18T19:12:51.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSee test suite\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSee also:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/2449-back-to-the-future-ii\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehttp://www.mathworks.com/matlabcentral/cody/problems/2449-back-to-the-future-ii\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44243,"title":"Ternary Conditional Operator","description":"Returns one of two expressions depending on a condition.\r\n\r\n  (test) : (expression1) : (expression2)\r\n\r\n*test:* \r\nAny Boolean expression.\r\n\r\n*expression1:* \r\nA function handle called if test is true. \r\n\r\n*expression2:* \r\nA function handle called if test is false. \r\n\r\n*Example*\r\n\r\n  \u003e\u003e a = (2 \u003e 1) : (@() 1) : (@() 2)\r\n     a =\r\n          1\r\n  \u003e\u003e a = (1 \u003e 2) : (@() 1) : (@() 2)\r\n     a =\r\n          2\r\n\r\nThe |colon.m| you submitted will be moved to the class folder |@function_handle|:\r\n  \r\n  mkdir @function_handle\r\n  movefile submission/colon.m @function_handle","description_html":"\u003cp\u003eReturns one of two expressions depending on a condition.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e(test) : (expression1) : (expression2)\r\n\u003c/pre\u003e\u003cp\u003e\u003cb\u003etest:\u003c/b\u003e \r\nAny Boolean expression.\u003c/p\u003e\u003cp\u003e\u003cb\u003eexpression1:\u003c/b\u003e \r\nA function handle called if test is true.\u003c/p\u003e\u003cp\u003e\u003cb\u003eexpression2:\u003c/b\u003e \r\nA function handle called if test is false.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample\u003c/b\u003e\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e\u0026gt;\u0026gt; a = (2 \u0026gt; 1) : (@() 1) : (@() 2)\r\n   a =\r\n        1\r\n\u0026gt;\u0026gt; a = (1 \u0026gt; 2) : (@() 1) : (@() 2)\r\n   a =\r\n        2\r\n\u003c/pre\u003e\u003cp\u003eThe \u003ctt\u003ecolon.m\u003c/tt\u003e you submitted will be moved to the class folder \u003ctt\u003e@function_handle\u003c/tt\u003e:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003emkdir @function_handle\r\nmovefile submission/colon.m @function_handle\r\n\u003c/pre\u003e","function_template":"function y = colon1(test, expr1, expr2)\r\n  y = expr1;\r\nend","test_suite":"%%\r\nmkdir @function_handle\r\nmovefile colon1.m @function_handle/colon.m\r\n\r\n%%\r\nassert(isequal((2 \u003e 1) : (@() 1) : (@() 2), 1))\r\n\r\n%%\r\nassert(isequal((1 \u003e 2) : (@() 1) : (@() 2), 2))\r\n\r\n%%\r\nfib = @(f, n) (n \u003e 2) : (@() f(f, n - 1) + f(f, n - 2)) : (@() 1);\r\nassert(fib(fib, 20) == 6765)\r\n\r\n%%\r\nx = magic(3);\r\n[m,I] = (x(1) \u003e 0) : (@() max(x)) : (@() min(x)) \r\nassert(isequal(m, [8 9 7]) \u0026\u0026 isequal(I, [1 3 2]))\r\n","published":true,"deleted":false,"likes_count":6,"comments_count":5,"created_by":1434,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":13,"test_suite_updated_at":"2020-04-22T14:42:35.000Z","rescore_all_solutions":false,"group_id":52,"created_at":"2017-06-27T15:11:40.000Z","updated_at":"2026-05-29T03:45:36.000Z","published_at":"2017-06-27T15:11:40.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eReturns one of two expressions depending on a condition.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[(test) : (expression1) : (expression2)]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etest:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Any Boolean expression.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eexpression1:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e A function handle called if test is true.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eexpression2:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e A function handle called if test is false.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[\u003e\u003e a = (2 \u003e 1) : (@() 1) : (@() 2)\\n   a =\\n        1\\n\u003e\u003e a = (1 \u003e 2) : (@() 1) : (@() 2)\\n   a =\\n        2]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecolon.m\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e you submitted will be moved to the class folder\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e@function_handle\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[mkdir @function_handle\\nmovefile submission/colon.m @function_handle]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":47453,"title":"Slitherlink I: Trivial","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 540.65px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 270.333px; transform-origin: 407px 270.333px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 80.1333px 7.91667px; transform-origin: 80.1333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to solve \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Slitherlink\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 137.7px 7.91667px; transform-origin: 137.7px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e pencil puzzles. An essential starter guide is \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003eSlitherlink Techniques\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 55.2167px 7.91667px; transform-origin: 55.2167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 78.5333px 7.91667px; transform-origin: 78.5333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eThis Slitherlink I: Trivial\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 258.667px 7.91667px; transform-origin: 258.667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is for the cases of s with a 4 or a pair of adjacent 3s forming a unique solution loop.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 368.367px 7.91667px; transform-origin: 368.367px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink II: Gimmes will use the Starting Techniques from Slitherlink Techniques. Adjacent 3s  yields R 3 R 3 R board values if trivial did not already solve. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 19.4333px 7.91667px; transform-origin: 19.4333px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eInput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 333.35px 7.91667px; transform-origin: 333.35px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 25.2667px 7.91667px; transform-origin: 25.2667px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eOutput:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 334.583px 7.91667px; transform-origin: 334.583px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.1167px 7.91667px; transform-origin: 31.1167px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.95px 7.91667px; transform-origin: 1.95px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 40.8667px; transform-origin: 404px 40.8667px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 358.05px 7.91667px; transform-origin: 358.05px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 365.75px 7.91667px; transform-origin: 365.75px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 250.25px 7.91667px; transform-origin: 250.25px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.833333px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.833333px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.833333px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.833333px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 177.1px 7.91667px; transform-origin: 177.1px 7.91667px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); text-emphasis-color: rgb(2, 128, 9); \"\u003e% 4 8 12 16 20]                       %to path\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 132.917px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 66.4667px; text-align: left; transform-origin: 384px 66.4667px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 66.9px 7.91667px; transform-origin: 66.9px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eRelated Challenges:\u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 241px;height: 127px\" src=\"data:image/png;base64,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\" data-image-state=\"image-loaded\" width=\"241\" height=\"127\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 373.267px 7.91667px; transform-origin: 373.267px 7.91667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSlitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check (large)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n [nr,nc]=size(s);\r\n [nrc,ncc]=size(c);\r\n% p=p'  as a 1-2 seg is also a 2-1 seg. rows/cols are path nodes and c indices\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% sum of p starts as 2 for corners, 3 for edges, and 4 for mid-points\r\n%The display tool, show_pfigs, makes segments Red for p(i,j)=5, Black if 0, grey if 1\r\n% Final nodes of p are either 5 or 0 with sum(p) being 0 or 10\r\n% Nodes in a path have an entry/exit path thus a sum of 10\r\n\r\np=trivial_solve(p,bsegs,s);\r\n\r\n[sv,valid]=pcheck(s,p,bsegs);\r\n\r\n  %show_pfig(s,p,c,emap,pmap,1)\r\n  fprintf('sv trivial solution\\n')\r\n  fprintf('%i ',sv);fprintf('\\n')\r\n \r\nend % sv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n\r\n\r\n\r\nfunction p=trivial_solve(p,bsegs,s)\r\n if nnz(s==4)\r\n  p=p*0;\r\n  %p(?)=5\r\n  p=p+p';\r\n  return\r\n end\r\n \r\n ptr3=find(s==3); % adjacent 3s  check if box around solves\r\n %p(?)=5\r\n p=p+p'; \r\nend %p=trivial_solve(p,bsegs,s)\r\n\r\n\r\n\r\nfunction [v,valid]=pcheck(s,p,bsegs)\r\n%creates the sv vector and tells valid status\r\n valid=0;\r\n v=[];\r\n if nnz(sum(p,2)==10)\u003c4,return;end\r\n  \r\n sv=s(:);\r\n schk=sv*0; % will add seg walls to schk and compare to sv using bsegs while ignore sv==5\r\n p(p\u003c5)=0; % clear non-segments\r\n v=find(sum(p,2)==10,1,'first'); %first index,  indices of corners; valid if v(1)=v(end)\r\n vnext=find(p(v,:)==5,1,'first');\r\n p(v,vnext)=0;\r\n p(vnext,v)=0;\r\n v=[v vnext];\r\n while v(1)~=v(end)\r\n  vnext=find(p(v(end),:)==5);\r\n  if isempty(vnext),return;end % No connector - no solution\r\n  p(v(end),vnext)=0;\r\n  p(vnext,v(end))=0;\r\n  v=[v vnext];\r\n end\r\n % v(1)==v(end)  [1 2 4 3 1]\r\n vsegs=sort([v(1:end-1);v(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(sv) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % bsegs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(sv==5)=5;\r\n if isequal(schk,sv)\r\n  valid=1;\r\n end\r\n \r\nend % pcheck\r\n\r\n\r\n\r\nfunction show_pfig(s,p,c,emap,pmap,fignum)\r\n%Create display of current solution status using p\r\n% p(i,j)=5 is a Red bar, p(i,j)=0 is a Black bar, p(i,j)=1 is a Grey bar\r\n% emap/pmap contain info on what segments are part of the puzzle p(1,end) is not a real segment\r\n [nr,nc]=size(s);\r\n \r\n figure(fignum);plot([0,nc,nc,0,0],[0,0,nr,nr,0],'color',[192 192 192]/255,'LineWidth',5);hold on\r\n axis tight\r\n set (gca,'Ydir','reverse')\r\n set (gca,'Xtick',[]);\r\n set (gca,'Ytick',[]);\r\n for i=0:nr\r\n  plot([0,nc],[i,i],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n for i=0:nc\r\n  plot([i,i],[0,nr],'color',[192 192 192]/255,'LineWidth',5)\r\n end\r\n\r\n for i=1:nr\r\n  for j=1:nc\r\n   txt=num2str(s(i,j));\r\n   t=text(j-.6,i-.5,txt); % reverse i,j  j is y-row, i is col  graph [col,row]\r\n   t.FontSize=20; %https://www.mathworks.com/help/matlab/creating_plots/add-text-to-specific-points-on-graph.html\r\n  end\r\n end\r\n \r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if pv==0\r\n    plot([b,d],[a,c],'k','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n \r\n %Draw RED on top\r\n for i=1:size(pmap,1)\r\n  pr=pmap(i,1);\r\n  pc=pmap(i,2);\r\n  pv=p(pr,pc);\r\n  if pv~=1\r\n   a=emap(pr,1);\r\n   b=emap(pr,2);\r\n   c=emap(pc,1);\r\n   d=emap(pc,2);\r\n   if b==d\r\n    if a\u003cc\r\n     a=max(0,a-.05);\r\n     c=min(nr,c+.05);\r\n    else % a\u003ec\r\n     a=min(nr,a+.05);\r\n     c=max(0,c-.05);\r\n    end\r\n   else %a==c\r\n    if b\u003cd\r\n     b=max(0,b-.05);\r\n     d=min(nc,d+.05);\r\n    else % b\u003ed\r\n     b=min(nc,b+.05);\r\n     d=max(0,d-.05);\r\n    end\r\n   end\r\n   if pv==5\r\n    plot([b,d],[a,c],'r','LineWidth',5);\r\n   end\r\n  end\r\n end\r\n hold off \r\nend %show_pfig(s,p,c,emap,pmap,fignum)\r\n\r\nfunction [c,bsegs,p,pmap]=create_p(nr,nc)\r\n%This is provided by the calling routine.  Included here for reference info\r\n%p is matrix of connections from r2c,c2r\r\n%0 is no connect, 1 is possible, 5 is connected\r\n%p row sums to 0 or 10\r\n%p_row_sum of 1 evolves to 0\r\n%p_row_sum of 6 evolves to 10\r\n%p_row_sum 1:4,6:8 has multiple options\r\n% transpose values always match\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n %[nr*nc,8]  four C segments about each s index\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc];\r\n p=p+p';\r\n \r\n %c\r\n %bsegs\r\n %p\r\n \r\n%1 4 2x1   1 4 7  1 5 9\r\n% A         A C    A D\r\n%2 5       2 5 8  2 6 10\r\n% B         B D    B E\r\n%3 6       3 6 9  3 7 11\r\n%                  C F\r\n%                 4 8 12\r\nend %[c,bsegs,p,pmap]=create_p(nr,nc)\r\n","test_suite":"%%\r\ns = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n\r\n%%\r\ns = [5 5 5;5 4 5;5 5 5];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns = [5 5;4 5;5 5];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns = [3 3];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns = [3;3];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns =[0 5 5;5 3 5;5 3 5;5 5 0];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\ns =[5 1 1 5;1 3 3 1;5 1 1 5];\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid,1))\r\n\r\n%%\r\n% anti-hack case\r\ns=zeros(randi(4,1,2)+2)+5;\r\ns(randi(prod(size(s)))) = 4;\r\n\r\n [nr,nc]=size(s);\r\n nr1=nr+1;\r\n nc1=nc+1;\r\n p=zeros(nr1*nc1);\r\n bsegs=zeros(nr*nc,8); % borders\r\n c=reshape(1:nr1*nc1,nr1,nc1); % corners\r\n [er,ec]=find(c);\r\n emap=[er-1,ec-1]; % used by visualizer\r\n \r\n for i=1:nr\r\n  for j=1:nc\r\n   ptr=i+nr*(j-1);\r\n   bsegs(ptr,:)=[c(i,j) c(i+1,j) c(i,j) c(i,j+1) c(i+1,j) c(i+1,j+1) c(i,j+1) c(i+1,j+1)];\r\n  end\r\n end %i\r\n \r\n for i=1:nr*nc\r\n  for j=1:2:7\r\n   p(bsegs(i,j),bsegs(i,j+1))=1;\r\n  end\r\n end\r\n \r\n [pr,pc]=find(p==1);\r\n pmap=[pr,pc]; %used by visualizer\r\n p=p+p';\r\n\r\nsv=slitherlink(s,c,p,bsegs,emap,pmap)\r\n \r\n schk=s(:)*0;\r\n vsegs=sort([sv(1:end-1);sv(2:end)]',2); % make low to hi to match bsegs pairs\r\n idxvsegs=vsegs*[bsegs(end);1]; %Create an index value for easier comparison\r\n \r\n pvalid=1; %check path is valid, contiguous\r\n for i=1:length(sv)-1\r\n  pvalid=pvalid*p(sv(i),sv(i+1));\r\n end\r\n for i=1:length(s(:)) % Create schk by examining all segments for each s index\r\n  for j=1:2:7 % besgs pairs loop\r\n   if nnz(idxvsegs==(bsegs(i,j)*bsegs(end)+bsegs(i,j+1)))\r\n    schk(i)=schk(i)+1;\r\n   end\r\n  end\r\n end\r\n schk(s(:)==5)=5; % overwrite real values with unknown setting\r\n if isequal(schk,s(:))\r\n  valid=1;\r\n else\r\n  valid=0;\r\n end\r\n\r\nassert(isequal(valid*pvalid,1))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":2,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-11-12T00:38:37.000Z","updated_at":"2026-05-30T19:54:00.000Z","published_at":"2020-11-12T23:27:09.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to solve \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Slitherlink\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e pencil puzzles. An essential starter guide is \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.conceptispuzzles.com/index.aspx?uri=puzzle/slitherlink/techniques\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink Techniques\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. An s matrix with values from 0:5 is provided. An s of 5 means this locations edges are not provided and may be from 0:3. The player will be given the s, c, and initial p matrices. The c matrix is clarified for the creation of the solution path of nodes as given in c.  The p matrix is a [numel,numel] matrix of c indices where p(x,y)=1 is a possible node connection. p(1,2)=1 as well as example's p(1,5)=1. Additional details of p are provided in the function template. Function template also includes visualization code.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eThis Slitherlink I: Trivial\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is for the cases of s with a 4 or a pair of adjacent 3s forming a unique solution loop.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink II: Gimmes will use the Starting Techniques from Slitherlink Techniques. Adjacent 3s  yields R 3 R 3 R board values if trivial did not already solve. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e s,  matrix of edge counts of the unique solution path; (c,p,bsegs,emap,pmap)  are provided but not required\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e sv, a vector of path nodes where sv(1)=sv(end). These nodes correspond to values in the c matrix example.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[%[1 5  9 13 17 % c matrix   [3 1 1 2; % s matrix  [1 2 6 7 8 12 16 20 19 18 17 13 9 5 1] % sv\\n% 2 6 10 14 18 %path nodes   2 1 0 1; %qty edges  % sv matrix is vector of nodes generating the\\n% 3 7 11 15 19 % corners     1 2 1 2] %adjacent   % Red Line path\\n% 4 8 12 16 20]                       %to path]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eRelated Challenges:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"127\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"241\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSlitherlink II: Gimmes, Slitherlink III: Evolve, Slitherlink IV: Recursive (medium), Slitherlink V: Assert/Evolve/Check (large)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"re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function","description":"Given a function handle f, generate a function that evaluates the derivative of f\r\n\r\n\r\nExamples:\r\n    \r\n   f = @sin;\r\n   df = Derivative(f);\r\n   df([0 pi])\r\n   ans =\r\n        1    -1\r\n\r\nDerivative of sin is cos, cos([0 pi]) = [1 -1]\r\n\r\nHint(Added 2015/9/17):\r\n\u003chttps://en.wikipedia.org/wiki/Numerical_differentiation\u003e","description_html":"\u003cp\u003eGiven a function handle f, generate a function that evaluates the derivative of f\u003c/p\u003e\u003cp\u003eExamples:\u003c/p\u003e\u003cpre\u003e   f = @sin;\r\n   df = Derivative(f);\r\n   df([0 pi])\r\n   ans =\r\n        1    -1\u003c/pre\u003e\u003cp\u003eDerivative of sin is cos, cos([0 pi]) = [1 -1]\u003c/p\u003e\u003cp\u003eHint(Added 2015/9/17): \u003ca href = \"https://en.wikipedia.org/wiki/Numerical_differentiation\"\u003ehttps://en.wikipedia.org/wiki/Numerical_differentiation\u003c/a\u003e\u003c/p\u003e","function_template":"function df = Derivative(f)\r\n  df = f;\r\nend","test_suite":"%%\r\nf = @sin;\r\ndf = Derivative(f);\r\nx = 10*rand(10000,1);\r\ndy = cos(x);\r\nassert(max(abs((dy-df(x))./dy))\u003c1e-13)\r\n%% \r\nf = @log;\r\ndf = Derivative(f);\r\nx = exp(500*rand(10000,1));\r\ndy = 1./x;\r\nassert(max(abs((dy-df(x))./dy))\u003c1e-13)\r\n%%\r\nf = @exp;\r\ndf = Derivative(f);\r\nx = 50*(rand(10000,1)-0.5);\r\ndy = exp(x);\r\nassert(max(abs((dy-df(x))./dy))\u003c1e-13)\r\n%%\r\nt = 10*rand;\r\nf = @(x)1./(x+t);\r\ndf = Derivative(f);\r\nx = 100*(rand(10000,1)-0.5); x(x==-t) = 1;\r\ndy = -1./(x+t).^2;\r\nassert(max(abs((dy-df(x))./dy))\u003c1e-13)\r\n%%\r\nt = 10*rand-5;\r\nf = @(x)atan(t*x);\r\ndf = Derivative(f);\r\nx = 100*(rand(10000,1)-0.5);\r\ndy = t./(t^2*x.^2 + 1);\r\nassert(max(abs((dy-df(x))./dy))\u003c1e-13)\r\n%%\r\nfor t = 2:6\r\n    f = @(x)t.^x./(sin(x).^t+cos(x).^t);\r\n    df = Derivative(f);\r\n    x = 3+rand(10000,1);\r\n    dy = (t.^x.*log(t))./(cos(x).^t+sin(x).^t)-...\r\n        (t.^x.*(t.*cos(x).*sin(x).^(t-1)-...\r\n        t.*cos(x).^(t-1).*sin(x)))./(cos(x).^t+sin(x).^t).^2;\r\n    assert(max(abs((dy-df(x))./dy))\u003c1e-13)\r\nend","published":true,"deleted":false,"likes_count":3,"comments_count":0,"created_by":1434,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":12,"test_suite_updated_at":"2015-09-08T18:13:33.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2015-09-07T05:46:00.000Z","updated_at":"2026-05-28T13:39:34.000Z","published_at":"2015-09-07T05:47:35.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a function handle f, generate a function that evaluates the derivative of f\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExamples:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[   f = @sin;\\n   df = Derivative(f);\\n   df([0 pi])\\n   ans =\\n        1    -1]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDerivative of sin is cos, cos([0 pi]) = [1 -1]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHint(Added 2015/9/17):\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Numerical_differentiation\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://en.wikipedia.org/wiki/Numerical_differentiation\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2837,"title":"Modify subscripts","description":"MATLAB supports object-oriented programming. Let's take an advantage of it in cody.\r\n\r\nThis problem starts \u003chttp://uk.mathworks.com/matlabcentral/cody/problems/2836-class-ics-modify-subscripts-easy here\u003e.\r\n\r\nLet's take things a bit more seriously. There are _subsasgn_ and _subsref_ to overload:\r\n\r\n  \u003e\u003e A = sub_double([ 1 4 7\r\n                      2 5 8\r\n                      3 6 9]);\r\n  \u003e\u003e A([1 3],[3 1])\r\n  \r\n  ans = \r\n\r\n    3x3 sub_double:\r\n\r\n    double data:    \r\n       7    3\r\n  \r\n  \u003e\u003e A([1 2 3],[3 2 1]) = 0\r\n  \r\n  A = \r\n\r\n    3x3 sub_double:\r\n\r\n    double data:\r\n       1     2     0\r\n       4     0     6\r\n       0     8     9\r\n\r\n  \u003e\u003e A(:)'\r\n  \r\n  ans = \r\n  \r\n    1x9 sub_double:\r\n  \r\n    double data:\r\n       1     4     0     2     0     8     0     6     9\r\n  \r\n  \u003e\u003e A(:,:)\r\n  \r\n  ans = \r\n  \r\n    1x3 sub_double:\r\n  \r\n    double data:\r\n       1     0     9\r\n\r\n\r\n","description_html":"\u003cp\u003eMATLAB supports object-oriented programming. Let's take an advantage of it in cody.\u003c/p\u003e\u003cp\u003eThis problem starts \u003ca href = \"http://uk.mathworks.com/matlabcentral/cody/problems/2836-class-ics-modify-subscripts-easy\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\u003cp\u003eLet's take things a bit more seriously. There are \u003ci\u003esubsasgn\u003c/i\u003e and \u003ci\u003esubsref\u003c/i\u003e to overload:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e\u0026gt;\u0026gt; A = sub_double([ 1 4 7\r\n                    2 5 8\r\n                    3 6 9]);\r\n\u0026gt;\u0026gt; A([1 3],[3 1])\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eans = \r\n\u003c/pre\u003e\u003cpre\u003e    3x3 sub_double:\u003c/pre\u003e\u003cpre\u003e    double data:    \r\n       7    3\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003e\u0026gt;\u0026gt; A([1 2 3],[3 2 1]) = 0\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eA = \r\n\u003c/pre\u003e\u003cpre\u003e    3x3 sub_double:\u003c/pre\u003e\u003cpre\u003e    double data:\r\n       1     2     0\r\n       4     0     6\r\n       0     8     9\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003e\u0026gt;\u0026gt; A(:)'\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eans = \r\n\u003c/pre\u003e\u003cpre\u003e    1x9 sub_double:\u003c/pre\u003e\u003cpre\u003e    double data:\r\n       1     4     0     2     0     8     0     6     9\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003e\u0026gt;\u0026gt; A(:,:)\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eans = \r\n\u003c/pre\u003e\u003cpre\u003e    1x3 sub_double:\u003c/pre\u003e\u003cpre\u003e    double data:\r\n       1     0     9\u003c/pre\u003e","function_template":"classdef sub_double \u003c double\r\n  methods\r\n    function obj = sub_double(in)\r\n      obj = obj@double(in);\r\n    end\r\n    function out = subsref(obj,S)\r\n        \r\n    end\r\n  end \r\nend","test_suite":"%%\r\n% first some basic tests\r\nA = 1;\r\nB = sub_double(A);\r\ny = B(1);\r\ny_correct = 1;\r\nassert(isequal(y,y_correct))\r\n%%\r\nA = [1 2 3\r\n     4 5 6\r\n     7 8 9];\r\nB = sub_double(A);\r\ny = B([1 3],[3 1]);\r\ny_correct = [3 7];\r\nassert(isequal(y,y_correct))\r\n%%\r\nA = [1 2 3\r\n     4 5 6\r\n     7 8 9];\r\nB = sub_double(A);\r\ny = B([1 3],[3 end]);\r\ny_correct = [3 9];\r\nassert(isequal(y,double(y_correct)))\r\n%%\r\nA = [1 2 3\r\n     4 5 6\r\n     7 8 9];\r\nB = sub_double(A);\r\ny = B(1,[3 1]);\r\ny_correct = [3 1];\r\nassert(isequal(y,y_correct))\r\n%%\r\nA = [1 2 3\r\n     4 5 6\r\n     7 8 9];\r\nB = sub_double(A);\r\ny = B(2:end);\r\ny_correct = [4 7 2 5 8 3 6 9];\r\nassert(isequal(y,y_correct))\r\n%%\r\nA = magic(5);\r\nB = sub_double(A);\r\ny = B([1 5 4 3 2],[3 4 5 1 2]);\r\ny_correct = [1:5];\r\nassert(isequal(y,y_correct))\r\n%%\r\nA = magic(7);\r\nB = sub_double(A);\r\nrows = randi(7,1,10);\r\ncols = randi(7,1,10);\r\ny = B(rows,cols);\r\ny_correct = arrayfun(@(R,C)A(R,C),rows,cols);\r\nassert(isequal(y,y_correct))\r\n%%\r\nA = 1;\r\nB = sub_double(A);\r\nB(1) = 3;\r\nA = 3;\r\nassert(isequal(A,B))\r\n%%\r\nA = [1 2 3\r\n     4 5 6\r\n     7 8 9];\r\nB = sub_double(A);\r\nB([1 2 3],[3 2 1]) = 0;\r\nA = [1 2 0\r\n     4 0 6\r\n     0 8 9];\r\nassert(isequal(A,B))\r\n%%\r\nA = [1 2 3\r\n     4 5 6\r\n     7 8 9];\r\nB = sub_double(A);\r\nB([1 3],[3 end]) = [7 7];\r\nA = [1 2 7\r\n     4 5 6\r\n     7 8 7];\r\nassert(isequal(double(A),double(B)))\r\n%%\r\nA = [1 2 3\r\n     4 5 6\r\n     7 8 9];\r\nB = sub_double(A);\r\ny = B(:,:);\r\ny_correct = [1 5 9];\r\nassert(isequal(y,y_correct))\r\n%%\r\nA = [1 2 3\r\n     4 5 6\r\n     7 8 9];\r\nB = sub_double(A);\r\nB(2:end) = [2:9];\r\nA = A';\r\nassert(isequal(B,A))\r\n%%\r\nA = magic(5);\r\nB = sub_double(A);\r\nB([1 5 4 3 2],[3 4 5 1 2])=0;\r\nassert(isequal(sum(~[B B']),ones(1,10)))\r\n%%\r\nA = magic(7);\r\nB = sub_double(A);\r\nrows = randi(7,1,10);\r\ncols = randi(7,1,10);\r\nvals = randi(7,1,10);\r\nB(rows,cols) = vals;\r\nfor k = 1:10\r\n  A(rows(k),cols(k))=vals(k);\r\nend\r\nassert(isequal(A,B))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":14358,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":10,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2015-01-18T00:53:33.000Z","updated_at":"2026-05-28T04:22:20.000Z","published_at":"2015-01-18T01:36:53.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMATLAB supports object-oriented programming. Let's take an advantage of it in cody.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem starts\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://uk.mathworks.com/matlabcentral/cody/problems/2836-class-ics-modify-subscripts-easy\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehere\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eLet's take things a bit more seriously. There are\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esubsasgn\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esubsref\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to overload:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[\u003e\u003e A = sub_double([ 1 4 7\\n                    2 5 8\\n                    3 6 9]);\\n\u003e\u003e A([1 3],[3 1])\\n\\nans = \\n\\n    3x3 sub_double:\\n\\n    double data:    \\n       7    3\\n\\n\u003e\u003e A([1 2 3],[3 2 1]) = 0\\n\\nA = \\n\\n    3x3 sub_double:\\n\\n    double data:\\n       1     2     0\\n       4     0     6\\n       0     8     9\\n\\n\u003e\u003e A(:)'\\n\\nans = \\n\\n    1x9 sub_double:\\n\\n    double data:\\n       1     4     0     2     0     8     0     6     9\\n\\n\u003e\u003e A(:,:)\\n\\nans = \\n\\n    1x3 sub_double:\\n\\n    double data:\\n       1     0     9]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":46938,"title":"Numerical computation of the optimal shooting angle of a catapult","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 879.667px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 406.5px 439.833px; transform-origin: 406.5px 439.833px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 64.3333px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 32.1667px; text-align: left; transform-origin: 383.5px 32.1667px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eConsider a capapult that fires a projects into the air with an initial velocity\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"56.5\" height=\"20\" style=\"width: 56.5px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e. The free-flying projectile is subjected to air friction and a gravitional force. Given a desired target \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"109\" height=\"21\" style=\"width: 109px; height: 21px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e and an initial velocity \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"56.5\" height=\"20\" style=\"width: 56.5px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e, find the optimal shooting angle \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"16\" height=\"18.5\" style=\"width: 16px; height: 18.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eof the catapult that minimizes the distance between the target and the trajectory of the fired projectile. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 21.25px; text-align: left; transform-origin: 383.5px 21.25px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003etip 1:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e Consider the states \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAB0AAAAoCAYAAAACJPERAAABwElEQVRYR+3VS6hNcRTH8c9VKDKSiTKRiYzlGUaSYsRAMbkD14SSooiUt4nEwMDUoxhI6d6ukRtKMWBmrESUwkheLa20O23249zTGdy964zO/7++a/1+a63/iCF8I0Ng6qADVb2Tt5N3WhToGmlaZPxXkJkt7ywswVZsxzrcx3584s8jsQbnsQL7cKeJH2XyzsVSvMV63MICbMZDbMRBfMMWHMXVfqHF+4txA5sy+D0cwjG8bwIqnq1qpDkpY1T2IKs7iVdtgXGvChpnxnAtIXtxHb8GDV2NSXzFNryoAC7KRCOxM2Vn61QaTXUbK7E7PS6LNR978kx0/AmcagONTo5so3niO4fj+FESbHbKvgqP20JDhV1Yiw+IBprIaj7+R+KosjV0OY7kbxnG8Q47snvDu1gOU/hZSKIRNMYjfPmChbiMK3iC4ryGrzGv4ddNPO+pujY0gBHkMF5n5qdzG0UXhrcXcQAv8Sb/i23VOz61oeHfzgz8Of2Laoqyxb69hHk4i7v4XuJtbWg/s957t4P+VaTORmoq/VDk3YBHbTdS0wrjoY8qR3MSnuECnva+vYOQtzLZDlopUT8HZo68vwGa/l8pXtmErgAAAABJRU5ErkJggg==\" width=\"14.5\" height=\"20\" style=\"width: 14.5px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAB0AAAAoCAYAAAACJPERAAACPklEQVRYR+3VS6hOYRQG4OfIJSTlklImIsmY3IoMJOUyIAmlFCYIhQ5K7ilJJMVEuRUDkYgZmYhESsZyi1IuyV3r9P3atv8//22fzsC/p9+31vu973rftdt0w9fWDZhaoF2qekvelryFKNAyUiEyVmryf8vbAyMwG3MxBZexBu/o+ElMwn6Mw2pcqGce5eTtg5F4gak4hwGYiZuYhvX4illox9FmQbP1w3EG01PzS9iIrXhdD1D2bjUj9U4yBrOrid0OPGoUMOqqgcadVTieQFbiJH51NehE3MBHzMH9CoCDEI+alwx2ByeSCb/XI2/cDVOdx3gsTTPO4w7FMbxKY5iAdeiV3B1m/KNONXnDyXuSeQJoH7bjRw51BYbgIEqsZuA0HmIZ3pZqOgONs8WYjDcIA13PN0BfbE5SPs88ZmCa/2gswtNaQMemZtFwFK4l+RYk94aksRxuIxT5nDNYPOYQxmBJyn0HbpZpxKM/PmAwDuMIwhDZvMZcI6+7cBb3KhirxDSWzCZ8yTMNwGgShyHDT+xO2ygMEEwOYG2a0bN09pdBcuChwilswK1y7g3GC1Pj92l+wSbAS1/s25CrH/biYsY0ebI9sQXfcub6R95m8p6vjf0ceQ2nf8ofVotMIw8JA3a6n4sGHZZyHXl9UunFRYJGhLalvD7OAMZ8l+NK6c9UFGhELdwdUXuQYxgZj9jsLG2yIkAjThG1aFrue4n5uFvLRmrERDXVFMG0JqByy6HuwmYKWkybUa9q7W9V624prHV7AQAAAABJRU5ErkJggg==\" width=\"14.5\" height=\"20\" style=\"width: 14.5px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e as the x- and y-position of the projectile, and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"14.5\" height=\"20\" style=\"width: 14.5px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"14.5\" height=\"20\" style=\"width: 14.5px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e as the x- and y-velocity. Then, the trajectory of the projectile can be found by solving the following ordinary differential equation (ODE):\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 22px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 11px; text-align: left; transform-origin: 383.5px 11px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e        \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"45\" height=\"22\" style=\"width: 45px; height: 22px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e,     \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 22px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 11px; text-align: left; transform-origin: 383.5px 11px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e        \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"45.5\" height=\"22\" style=\"width: 45.5px; height: 22px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e      \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 22px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 11px; text-align: left; transform-origin: 383.5px 11px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; 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height: 19.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e, and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"47.5\" height=\"17.5\" style=\"width: 47.5px; height: 17.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eis the friction coefficient between the air and the projectile. Use the ode45.m function to compute the trajectory of the projectile with initial conditions \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" 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Plotting \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"14.5\" height=\"20\" style=\"width: 14.5px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e vs. \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"14.5\" height=\"20\" style=\"width: 14.5px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e will result in the x-y trajectory of the projectile, as shown in the figure below.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.8333px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.9167px; text-align: left; transform-origin: 383.5px 10.9167px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003etip 2: \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eUse the following update law, to incrementally update the shooting angle \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"15\" height=\"20\" style=\"width: 15px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.8333px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.9167px; text-align: left; transform-origin: 383.5px 10.9167px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e        \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" 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width=\"44\" height=\"20\" style=\"width: 44px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e the smallest Euclidean distance between the trajectory of the projectile and the target \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg 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\" width=\"298\" height=\"20\" style=\"width: 298px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eis a difference angle, and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"55.5\" height=\"17.5\" style=\"width: 55.5px; height: 17.5px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003ean update parameter.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.6667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.3333px; text-align: left; transform-origin: 383.5px 10.3333px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.6667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.3333px; text-align: left; transform-origin: 383.5px 10.3333px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20.6667px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.5px 10.3333px; text-align: left; transform-origin: 383.5px 10.3333px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eExample of algorithm's numerical result:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 80px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 403.5px 40px; transform-origin: 403.5px 40px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 403.5px 10px; transform-origin: 403.5px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003etheta = catapult(25,3,25)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 403.5px 10px; transform-origin: 403.5px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003etheta = \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 403.5px 10px; transform-origin: 403.5px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e    0.8431\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 403.5px 10px; transform-origin: 403.5px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e    \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 264.333px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 383.5px 132.167px; text-align: left; transform-origin: 383.5px 132.167px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" width=\"570\" height=\"259\" style=\"vertical-align: baseline;width: 570px;height: 259px\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function theta = catapult(xd,yd,v0) \r\n  \r\n    global g nu;\r\n    \r\n    g   = -9.81;  % grav. acceleration\r\n    nu  = 0.5;    % air friction coeff.\r\n    k   = 0;      % solver increments\r\n    dt  = 1e-2;   % timesteps\r\n    T   = 10;     % simulation time\r\n    TOL = 1e-2;   % absolute tolerance\r\n    \r\n    [~,y] = ode45(@ODECatapult,0:dt:T,[v0,0,0]); \r\n    \r\n    % solver for optimal angle\r\n    while (e \u003e= TOL) \u0026\u0026 (k \u003e 150)        \r\n        \r\n        %theta = theta + beta;\r\n        \r\n        k = k+1;    % add increment\r\n    end\r\n  \r\n    function dx = ODECatapult(t,x)\r\n        global g nu;\r\n        %% fill in ordinary differential equation %%\r\n    end\r\n    \r\n    function e = EuclideanDistance(y,xd,yd)\r\n        %% fill in computation of smallest euclidean distance %%\r\n    end\r\n    \r\n    function beta = UpdateLaw(y,e,lambda)\r\n        %% fill in update law to update the shooting angle %%\r\n    end\r\nend","test_suite":"xd = 8;\r\nyd = 2;\r\nv0 = 35;\r\ny_correct = 1.446;\r\n\r\nassert(isequal(round(catapult(xd,yd,v0),3),y_correct))\r\n\r\n%%\r\nxd = 15;\r\nyd = 5;\r\nv0 = 35;\r\ny_correct = 1.33;\r\n\r\nassert(isequal(round(catapult(xd,yd,v0),2),y_correct))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":2,"created_by":636373,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-10-19T12:41:43.000Z","updated_at":"2026-05-30T17:02:13.000Z","published_at":"2020-10-19T13:39:36.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eConsider a capapult that fires a projects into the air with an initial velocity\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ev_0 \\\\in \\\\mathbb{R}_{\\\\ge 0}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. The free-flying projectile is subjected to air friction and a gravitional force. Given a desired target \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e$z_d = [x_d, y_d] \\\\in \\\\mathbb{R}^2$\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e and an initial velocity \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ev_0 \\\\in \\\\mathbb{R}_{\\\\ge 0}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, find the optimal shooting angle \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\theta^*\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003eof the catapult that minimizes the distance between the target and the trajectory of the fired projectile. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etip 1:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Consider the states \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex_1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex_2\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e as the x- and y-position of the projectile, and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex_3\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex_4\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e as the x- and y-velocity. Then, the trajectory of the projectile can be found by solving the following ordinary differential equation (ODE):\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e        \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\dot{x_1} = x_3\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e,     \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e        \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\dot{x}_2 = x_4\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e      \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e        \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\dot{x}_3 = -\\\\nu x_3\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e,  \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e    \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e        \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\dot{x}_4 = -g - \\\\nu x_4\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e.   \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhere \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eg = 9.81\\\\; (\\\\text{m/s}^2)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\nu = 0.5\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003eis the friction coefficient between the air and the projectile. Use the ode45.m function to compute the trajectory of the projectile with initial conditions \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex(t = 0) = (0,0,v_0 \\\\cos(\\\\theta_k), v_0 \\\\sin(\\\\theta_k))\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. Plotting \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex_1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e vs. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex_2\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e will result in the x-y trajectory of the projectile, as shown in the figure below.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003etip 2: \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eUse the following update law, to incrementally update the shooting angle \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\theta_k\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e        \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\theta_{k+1} = \\\\theta_k + \\\\lambda \\\\, \\\\text{sign}(\\\\theta_{e,k})\\\\,e_k\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhere \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ee_k \\\\in \\\\mathbb{R}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e the smallest Euclidean distance between the trajectory of the projectile and the target \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ez_d\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\theta_{e,k} = \\\\text{atan2}(d_y,d_x) - \\\\text{atan2}(v_0\\\\sin(\\\\theta_k),v_0\\\\cos(\\\\theta_k))\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003eis a difference angle, and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e$\\\\lambda = 0.01$\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003ean update parameter.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample of algorithm's numerical result:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[theta = catapult(25,3,25)\\ntheta = \\n    0.8431\\n    ]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"259\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"570\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":52911,"title":"Easy Sequences 41: Boxes with Integer Edges","description":"For this problem, we are asked to write a function that will count the number of boxes with integer edges, that has the same given volume .\r\nFor example for , the possible boxes are shown in the figure below:\r\n                                               \r\nTherefore, in this case, the function should return .","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.440000534057617px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: normal; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 344px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003eFor this problem,\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"font-weight: bold; \"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003ewe are asked to \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"font-weight: bold; \"\u003ewrite a function that will count the number of boxes with integer edges, that has the same given volume \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: normal; color: rgb(0, 0, 0);\"\u003ev\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"font-weight: bold; \"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003eFor example for \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"36\" height=\"18\" style=\"width: 36px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e, the possible boxes are shown in the figure below:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 233px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e                                               \u003c/span\u003e\u003c/span\u003e\u003cimg class=\"imageNode\" width=\"367\" height=\"227\" style=\"vertical-align: baseline;width: 367px;height: 227px\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003eTherefore, in this case, the function should return \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: normal; font-weight: normal; color: rgb(0, 0, 0);\"\u003e3\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function n = numPosBoxes(v)\r\n    n = floor(log(v)/log(2));\r\nend","test_suite":"%%\r\nv = 8;\r\nn_correct = 3;\r\nassert(isequal(numPosBoxes(v),n_correct))\r\n%%\r\nv = 1234;\r\nn_correct = 2;\r\nassert(isequal(numPosBoxes(v),n_correct))\r\n%%\r\nv = 10000;\r\nn_correct = 42;\r\nassert(isequal(numPosBoxes(v),n_correct))\r\n%%\r\nv = 9999999;\r\nn_correct = 10;\r\nassert(isequal(numPosBoxes(v),n_correct))\r\n%%\r\nv = 9876543210;\r\nn_correct = 164;\r\nassert(isequal(numPosBoxes(v),n_correct))\r\n%%\r\nv = 123123123123;\r\nn_correct = 365;\r\nassert(isequal(numPosBoxes(v),n_correct))\r\n%%\r\nv = 100000000000000;\r\nn_correct = 2432;\r\nassert(isequal(numPosBoxes(v),n_correct))\r\n%%\r\nv = double(intmax('uint32'))*12345\r\nn_correct = 488;\r\nassert(isequal(numPosBoxes(v),n_correct))\r\n%%\r\nvs = floor(flintmax ./ (1:100));\r\nns = arrayfun(@(v) numPosBoxes(v),vs);\r\nss = floor([sum(ns) mean(ns) mode(ns) median(ns) std(ns)])\r\nss_correct = [389499 3894 5 89 7993];\r\nassert(isequal(ss,ss_correct))\r\n%%\r\nfiletext = fileread('numPosBoxes.m');\r\nnot_allowed = contains(filetext, 'persistent') || contains(filetext, 'global') || contains(filetext, 'BigInteger') || contains(filetext, 'java'); \r\nassert(~not_allowed)","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":255988,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2021-10-11T18:45:59.000Z","updated_at":"2026-05-24T22:04:42.000Z","published_at":"2021-10-17T18:48:44.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor this problem,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003ewe are asked to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ewrite a function that will count the number of boxes with integer edges, that has the same given volume \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ev\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example for \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ev=8\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, the possible boxes are shown in the figure below:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e                                               \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"227\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"367\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTherefore, in this case, the function should return \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e3\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":42580,"title":"Conic equation","description":"A conic of revolution (around the |z| axis) can be defined by the equation\r\n\r\n   s^2 – 2*R*z + (k+1)*z^2 = 0\r\n\r\nwhere |s^2=x^2+y^2|, |R| is the vertex radius of curvature, and |k| is the conic constant: |k\u003c-1| for a hyperbola, |k=-1| for a parabola, |-1\u003ck\u003c0| for a tall ellipse, |k=0| for a sphere, and |k\u003e0| for a short ellipse.\r\n\r\nWrite a function |z=conic(s,R,k)| to calculate height |z| as a function of radius |s| for given |R| and |k|.  Choose the branch of the solution that gives |z=s^2/(2*R)+...| for small values of |s|.  This defines a concave surface for |R\u003e0| and a convex surface for |R\u003c0|.  \r\n\r\nThe trick is to get full machine precision for all values of |s| and |R|.  The test suite will require a relative error less than |4*eps|, where |eps| is the machine precision.\r\n\r\nHint (added 2015/09/03): the straightforward solution is \r\n\r\n   z = (R-sqrt(R^2-(k+1)*s^2))/(k+1), \r\n\r\nbut this does not work if |k=-1|, gives the wrong branch of the solution if |R\u003c0|, and is subject to severe roundoff error if |s^2| is small compared to |R^2|.  It is possible, however, to find a mathematically equivalent form of the solution that solves all three problems at once.\r\n","description_html":"\u003cp\u003eA conic of revolution (around the \u003ctt\u003ez\u003c/tt\u003e axis) can be defined by the equation\u003c/p\u003e\u003cpre\u003e   s^2 – 2*R*z + (k+1)*z^2 = 0\u003c/pre\u003e\u003cp\u003ewhere \u003ctt\u003es^2=x^2+y^2\u003c/tt\u003e, \u003ctt\u003eR\u003c/tt\u003e is the vertex radius of curvature, and \u003ctt\u003ek\u003c/tt\u003e is the conic constant: \u003ctt\u003ek\u0026lt;-1\u003c/tt\u003e for a hyperbola, \u003ctt\u003ek=-1\u003c/tt\u003e for a parabola, \u003ctt\u003e-1\u0026lt;k\u0026lt;0\u003c/tt\u003e for a tall ellipse, \u003ctt\u003ek=0\u003c/tt\u003e for a sphere, and \u003ctt\u003ek\u0026gt;0\u003c/tt\u003e for a short ellipse.\u003c/p\u003e\u003cp\u003eWrite a function \u003ctt\u003ez=conic(s,R,k)\u003c/tt\u003e to calculate height \u003ctt\u003ez\u003c/tt\u003e as a function of radius \u003ctt\u003es\u003c/tt\u003e for given \u003ctt\u003eR\u003c/tt\u003e and \u003ctt\u003ek\u003c/tt\u003e.  Choose the branch of the solution that gives \u003ctt\u003ez=s^2/(2*R)+...\u003c/tt\u003e for small values of \u003ctt\u003es\u003c/tt\u003e.  This defines a concave surface for \u003ctt\u003eR\u0026gt;0\u003c/tt\u003e and a convex surface for \u003ctt\u003eR\u0026lt;0\u003c/tt\u003e.\u003c/p\u003e\u003cp\u003eThe trick is to get full machine precision for all values of \u003ctt\u003es\u003c/tt\u003e and \u003ctt\u003eR\u003c/tt\u003e.  The test suite will require a relative error less than \u003ctt\u003e4*eps\u003c/tt\u003e, where \u003ctt\u003eeps\u003c/tt\u003e is the machine precision.\u003c/p\u003e\u003cp\u003eHint (added 2015/09/03): the straightforward solution is\u003c/p\u003e\u003cpre\u003e   z = (R-sqrt(R^2-(k+1)*s^2))/(k+1), \u003c/pre\u003e\u003cp\u003ebut this does not work if \u003ctt\u003ek=-1\u003c/tt\u003e, gives the wrong branch of the solution if \u003ctt\u003eR\u0026lt;0\u003c/tt\u003e, and is subject to severe roundoff error if \u003ctt\u003es^2\u003c/tt\u003e is small compared to \u003ctt\u003eR^2\u003c/tt\u003e.  It is possible, however, to find a mathematically equivalent form of the solution that solves all three problems at once.\u003c/p\u003e","function_template":"function z=conic(s,R,k)\r\nz=0;\r\nend","test_suite":"%%\r\nR=5;\r\nk=-1;\r\ns=-5:5;\r\nz=[25 16 9 4 1 0 1 4 9 16 25]/10;\r\nt=arrayfun(@(x)conic(x,R,k),s);\r\nassert(all(abs(t-z)\u003c=4*eps*abs(z)))\r\n%%\r\nR=-5;\r\nk=-1;\r\ns=-5:5;\r\nz=-[25 16 9 4 1 0 1 4 9 16 25]/10;\r\nt=arrayfun(@(x)conic(x,R,k),s);\r\nassert(all(abs(t-z)\u003c=4*eps*abs(z)))\r\n%%\r\nR=6;\r\nk=0;\r\ns=0:0.125:2;\r\nz=[0 0.001302224649086391 0.005210595859100573 ...\r\n   0.01173021649825800 0.02086962844930099 ...\r\n   0.03264086885999461 0.04705955010467117 ...\r\n   0.06414496470811713 0.08392021690038396 ...\r\n   0.1064123829368584 0.1316527028472488 ...\r\n   0.1596768068881667 0.1905249806888747 ...\r\n   0.2242424739260392 0.2608798583755018 ...\r\n   0.3004934424110011 0.3431457505076198];\r\nt=arrayfun(@(x)conic(x,R,k),s);\r\nassert(all(abs(t-z)\u003c=4*eps*abs(z)))\r\n%%\r\nR=6800;\r\nk=-2;\r\ns=10.^(-9:9);\r\nz=[7.352941176470588e-23 7.352941176470588e-21 ...\r\n   7.352941176470588e-19 7.352941176470588e-17 ...\r\n   7.352941176470588e-15 7.352941176470588e-13 ...\r\n   7.352941176470548e-11 7.352941176466613e-9 ...\r\n   7.352941176073046e-7 0.00007352941136716365 ...\r\n   0.007352937201052538 0.7352543677216725 ...\r\n   73.13611097583313 5292.973166264779 93430.93334894173 ...\r\n   993223.1197327390 9.993202311999733e6 9.99932002312e7 ...\r\n   9.9999320002312e8];\r\nt=arrayfun(@(x)conic(x,R,k),s);\r\nassert(all(abs(t-z)\u003c=4*eps*abs(z)))\r\n%%\r\nR=exp(1);\r\nk=pi;\r\ns=10.^(-7:0);\r\nz=[1.839397205857214e-15 1.839397205857469e-13 ...\r\n   1.839397205882986e-11 1.839397208434684e-09 ...\r\n   1.839397463604480e-07 0.00001839422981299153 ...\r\n   0.001841981926630790 0.2212216213343403];\r\nt=arrayfun(@(x)conic(x,R,k),s);\r\nassert(all(abs(t-z)\u003c=4*eps*abs(z)))\r\n%%\r\nt=fileread('conic.m');\r\nassert(isempty(findstr(t,'roots')))\r\nassert(isempty(findstr(t,'fzero')))\r\n\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":1,"created_by":245,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":25,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":37,"created_at":"2015-08-26T21:39:35.000Z","updated_at":"2026-05-25T04:09:40.000Z","published_at":"2015-08-26T22:21:10.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA conic of revolution (around the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ez\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e axis) can be defined by the equation\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[   s^2 – 2*R*z + (k+1)*z^2 = 0]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhere\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es^2=x^2+y^2\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eR\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is the vertex radius of curvature, and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is the conic constant:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u0026lt;-1\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e for a hyperbola,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek=-1\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e for a parabola,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e-1\u0026lt;k\u0026lt;0\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e for a tall ellipse,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek=0\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e for a sphere, and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u0026gt;0\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e for a short ellipse.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ez=conic(s,R,k)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e to calculate height\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ez\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e as a function of radius\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e for given\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eR\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. Choose the branch of the solution that gives\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ez=s^2/(2*R)+...\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e for small values of\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. This defines a concave surface for\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eR\u0026gt;0\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and a convex surface for\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eR\u0026lt;0\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe trick is to get full machine precision for all values of\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eR\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. The test suite will require a relative error less than\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e4*eps\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, where\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eeps\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is the machine precision.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHint (added 2015/09/03): the straightforward solution is\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[   z = (R-sqrt(R^2-(k+1)*s^2))/(k+1),]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ebut this does not work if\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ek=-1\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, gives the wrong branch of the solution if\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eR\u0026lt;0\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and is subject to severe roundoff error if\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003es^2\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is small compared to\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eR^2\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. It is possible, however, to find a mathematically equivalent form of the solution that solves all three problems at once.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":46696,"title":"Number Theoretic Transform (NTT)","description":null,"description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 63px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 31.5px; transform-origin: 407px 31.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eGiven an input polynomial (r) of length \u0026lt;=n and a prime number (p) such that an nth root of unity exists and the modular inverse of (n) modulus p exists. Convert the polynominal coefficents by Number Theoretic Transform (NTT) using the primitive nth root of unity as the generator mod p.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function rhat = numberTheoreticTransform(r,n,p)\r\n  rhat=r;\r\nend","test_suite":"%%\r\nn=210;\r\np=5881;\r\nr=[8669,57047,39514,17386,56675,3808,29999,47330,22216,26294,34536,58604,51010,4546,18270,24862,...\r\n    56667,27522,15720,39167,31418,58887,61257,53601,46459,48707,58963,4275,22014,284,54270,33255,...\r\n    23996,14853,35050,18971,4480,5568,4478,26857,8085,29033,58912,23176,7875,37297,57346,22844,...\r\n    2747,9328,5019,48531,29918,43794,45825,37444,41202,57525,43407,57371,30639,9262,4465,46808,...\r\n    20184,43985,42757,34802,46865,33083,31981,32626,61340,25511,7677,15756,44886,55001,63579,14101,...\r\n    49829,38279,26407,33426,32482,42688,48739,19788,5872,54130,25531,50810,11755,7167,59320,57432,...\r\n    65522,56639,2416,35696,65379,33489,57246,4602,64719,60470,36979,28276,22140,47233,894,24514,...\r\n    60469,35814,31056,32541,20248,62314,64355,33656,65050,29874,27921,13973,12664,54575,47620,34717,...\r\n    54334,33546,36173,13977,38523,9356,3422,44781,39882,14395,26625,41281,36392,8361,11088,65,...\r\n    27404,32013,10477,43701,1174,7843,62398,63953,2026,32367,56539,15917,54674,53319,41220,146,...\r\n    24885,59271,44587,24826,41415,15942,37448,64338,55684,18575,44725,23470,64679,5504,16404,53172,...\r\n    5532,34816,52469,48419,9284,28697,22962,31358,38496,9555,59331,41955,10678,37087,61054,51321,...\r\n    44937,30554,17060,37307,16303,20925,59690,58013];\r\nrhat=[4391        3275        5259        1238        4797        2486         919        3786        5067        4389\r\n        2255        3368         968        3027        4274        3358        2953        3646        2967        4281\r\n        5348         229        5498        5448        3419        3720         100        5376        2968        3437\r\n         404        3686        3576        4743        4840        4942        4237        1414         102        2806\r\n        3832        5075        5084        1139        2483        2021        5541        1332        5022        5155\r\n        5235        4306         797        2739         911        3669        4133         547        5678        5162\r\n        1905        4772        2478        1657        1905        2539        4053        3185        3567         113\r\n        1985         674        1359         182        1645        1249        1752         922        1178        5427\r\n        2476        1241        2496        1703        3691        5044         802         304         905        2280\r\n        2211        4163        4845         690        4246        3016        3159        1555        3762        1416\r\n         105        4332        2706         308        2230        2117         787        1189        5549          72\r\n        4092        4428        4103        3397        5700        5630        1803         840        4153        4385\r\n        5537        1564        5437        5553         377        3468        2885        4827        3523        2618\r\n        2238        3633        2254        4028        3660        1909        5874        1850        3153        2253\r\n           3        2286        1953          81        5161        5764        5063        2887        4983        3481\r\n        5044         770        1225        4832        1673        5556        2434          21        1735        5422\r\n        1003        2672        3262         982        2344         269        2638        3485        3278        4882\r\n        4604        5118        4587        2136        3361         478        5289         808        3170        3752\r\n        2862        4604        4813        3077        5849         813        1214         135        3691        4774\r\n        1668        4361        5564        3949        5019          71        2441        4481         722        5462\r\n        4925        2693          41        3294        3971         361        1224         445        5386         186];\r\nassert(isequal(numberTheoreticTransform(r,n,p),rhat(:)'))\r\n%%\r\nn=1024\r\np=12289;\r\nr=[52074       13446       46646        7881       15396       46708        6135       41514       35944       46632\r\n       60673       28779       56867       40989       26142       30972       42638       64624       10831       31518\r\n       11720        1785        7753       22717       17571       46438       14101       13576       32367       47787\r\n       33917       57421        2557       21929       54559       62787       15982       49616       35069       61443\r\n       41091       39983       39203       37658       65232       33146       22261       58086       13029       33898\r\n       59846       13342       39604       56619       42582       19991       12967       30948       40839       59183\r\n       43513       34073       33844       13013       46134       51761       33215       10414        1724       14299\r\n       25506        3527         492       44069       61099       15491       62308       53144       20892       57227\r\n       48497       56504       45149       59102       45065       15355       25860       31228       34930        5419\r\n       53584       29028       61998       13051       37247       30454       38303        7621       21415       30500\r\n       39344       35914       57248       19548       24959       40592       39750       57391       39465        1437\r\n        5570       37149        7423       32539       41587       40326       46834       41627       23719       52971\r\n       60447       44590       23237       58320       23804        8036       26315        6375        8842       11744\r\n        3512       24338       15855       32860       26713        8112       56275       59535       59887       10839\r\n       34539        5126       36722       18153       24163       18642       60324        2294       41979       11901\r\n        7789       29907       40155       34993       30696       48216       49206        2605       43173       45313\r\n       24913        3135       19713       37634       32991       26955       18716       64786       44258       14009\r\n       53269       48382       52307       27053       59672       54328       52220       44969       48795       19536\r\n       15997        2490       52143         967       13528       61283        9356       24686       55192       50353\r\n       57961       62537       51189       46056       22190       26153       33066       33051       33859       32843\r\n       46704       48652       23009       33210       37625        3421       40022       50036        9952       59602\r\n       24782       61436        3558       24986       31911       37433       46124        3203       24947        3791\r\n       16313       33643       46445        4255       17184       48999       25122       47574       53806       28622\r\n       16571       15787       65072       23499       37984       20987       47754       45962       11230       37503\r\n       50282       17037       10648       15351       57562       32304       58149       30073       21625       37032\r\n        3267       49740        7442       13336        3994       14526        3660       38161       63338       53989\r\n       44911       65099       59826       53331       28893       61556        9058       22222       52841        8264\r\n       40650       23377       31565       25784        5521       31608       56561       11182       14561       19668\r\n       48934       49339       55823        3511       36912       35389       27639       26161       65521         139\r\n       64045        7212       53078       24579       35344       14487       26955       60278        4177       62331\r\n       25160       39127       12239       50790       50335        6287       62858       14814       27884       50220\r\n       17052       28219       16200       10832       15275        3943       49168       23658       26498       49237\r\n       57505       47888        3551       59783       38493       53707       64290       21270       26233        9100\r\n       52828       17116       39908       20919       30079       50559       15303        5477        7334       22893\r\n       30220        6213       50936       21612       56425       12825        6306       33598       27807        9918\r\n        5961       29554       33493       13384       43308       58662       25203       54582       40209       32553\r\n       36979       41947        1818       50280       23191       44846       32785       59284       64753       52995\r\n       12280        8653       64905        4585       22753       43047       37372       47421       14411       41475\r\n       34844       29676       32829       62261       16627       64905       64004       25100       23205       45115\r\n       23267       42742       21757       10368       62424        2208       32299       19530       17448       41914\r\n       20629       54198       11395       18772       19542       27803       26272       45332       19103       47796\r\n       47627       20190       41001       45031       10381       32111       65207       57701       12346       56350\r\n       33801       26369       37692        9250       23677       38240       17104       60591        1498       41088\r\n       51815       57948       49216       33560       48603        5457       43602        5324       29452       11835\r\n       13401       45913       10061       47272       46261       43263       63193       31632       15967       37572\r\n       44440       15851       23382       60872       45933        3427       43984        8405       56932       10719\r\n        3439       49796        9433       47979         408       36492       19606       16574       34643       59379\r\n       52505       19066       55745       49142       24533       46663       34807       57931       59908        5068\r\n       44470       18182       22142       26694       59080       31975          95       12863       63827       22186\r\n       61997         400       18035       15695       20863       40475       57919        7953       38366       38051\r\n        6000       24557         393       34134       39130       14010       26501       35631        7797       31145\r\n       59535       28634       52554       14357       19516       42313       19739       20618       60721       52777\r\n       33420       19942       32598       55206        8192       24945       62297       25037       38899       34785\r\n       40298       19061       35248       43445       25451        6796       30189       51874       57908       14897\r\n       20714       15893       57076       53492       53587       24740       18851       54996       27818       46496\r\n        5078       61386       47372       52027       64302       17226        5546       44579       39797        9740\r\n       55745       56373       43783       30743       56491       15812       38153       27323        4637       43130\r\n        9471       26032       11719       20285        5493       40823       10031       42132       60605       41548\r\n       24280       31419       36077       45061       22132       34270        4790       14030       42079       15027\r\n       40789       37027       62906       64674       15474       27081       38047       40453        6848       11942\r\n       65375       32087       39060       50458       20827       14273       18809       44249       45889       10902\r\n       33904       17682       52990       54367       64516       56266       23718       39388       25939        9804\r\n       64914       64863       64522       46273       35930       56427       47502       22695        5564       13287\r\n       14846       12037       58059       39015       49102       18608       56250       23881       14056       62584\r\n       26083       56469       14014       49340       55171       40330       22801       11238       16305        1042\r\n       45650        2138        2269       32553       10937       51084       63028       52124       14853       62751\r\n        4236       21755       29564       56697       59185       62576       62493       32287       46072        1683\r\n       48998       49069         904        4458        6889       60267       13502       23240       49424       63642\r\n       27551       42229       31045       63474       48830       25219       50347       50794       35866       19503\r\n       53170       11091       62337        6472       47800       10658       40339       15519       36273       34411\r\n       24877       62403       16315       35846       47020       52215       60222       55367       41325       56514\r\n       20910       35603       25324       26409        8744        7459       39487       53511       64582       58746\r\n       64621       16476       28274        7014       29215       10408       46015       55458       41568       12386\r\n       47066       37917       54452       47458       33343       23319       48737       24260       39351       43300\r\n       27078       59996       54044       40218       34766       55558       25238       25115       59584       61684\r\n        6463       58693       29687       51312       56342       38193       16482       56448       37410       63943\r\n       48140       31621       24940       37134       44415       38415        2409       30402       21982        7073\r\n       41766       29015       60677       53170       52811       60675       30941       37391       62727       11724\r\n        4839       20431       48551       37799       34815       37688       42275       45567       28830       48925\r\n        7897        3625       48341       61867       62645         653       18282       62974       39422        3241\r\n       64329       49400       62057       57111        4369       53043       33938       35803       47203        4671\r\n       32558        8647       33429       33266       35488       39898       16100       41718       44484       32055\r\n        1468       23325       51896       51696       18458       31451       19497       37414       13943       55698\r\n        3527       25943       29633       31000       31516       17592       42629       60759        5349       65342\r\n        9232       58033       55653       54316       44883       16914       58418       56607       17988         287\r\n       58554        1391       25587       21134       13648       31523       56433       11130       56853       35560\r\n       30527       55317       48390       63972       39856       14899       13756       11711       36658       56449\r\n       36756       18878       63991       18232       21376        3185       26155       15958       30449       59581\r\n       32404       16406       34294        4773       57727       11091       58188       49268       28200       55400\r\n        4442       32006       28174       49232        8742       16937       16811       13050       50723       57597\r\n       58828       47778       13576       54472        6711       12970       63360       64417       42855       48901\r\n       18911       13278       21194       60446       62856       39694       40577       46506       43104        7699\r\n       17632       14173        7265       21431       10020       53982       10836       11497       10552       33359\r\n       38941       63985       24589       52695        9996       53124       54145       56249       28336       11064\r\n       31187       38878       21620       35274       10194       52575       42971       59599       33101       54467\r\n       24137       19949       22420       30362        5870       46406       35812       63023       24597       60818\r\n       42966       63419       53550       53788       29781       56320       16471       37394       31481       11107\r\n       61485       58718       34844       62384       43836       51189        2631       36888       22440       57916\r\n       40660       12453       34152        4998       54480       13356       15294       11577       50931       25418\r\n       18536         117       50745       46443       51788       65099       23665       33664       25162       25072];\r\nrhat=[8149       10593         825        1418        8962        1559        3161         152\r\n        5086        9561       11949        5217        5798       11589        3150        5329\r\n        8984        3998        7095         390        2183        9925        6985       11431\r\n        3249        1053        7833        4160          13        1692        1976        8920\r\n        9291        9284        9074        2974        5068       10375          80        4023\r\n       11492        5804        6645        1044        7322          19        7192        7839\r\n        9217        4466       10449        3187        2137         119        9226        6202\r\n        7603        5162        8758       12104        7195        7646        6527        5506\r\n        6225       12166        3612        9329         470        7699        9996       11300\r\n       10722        1986        5330       10948         843        4540       11096        7298\r\n         446        7352        2390        5148        9522       11654        1156        8749\r\n        3736       11720       11276        6342        9275       11013         782        1253\r\n        3336        5994       12271       10025        4567        8321        1619        6205\r\n        2010        3243        4122        2079        9240         989       12020        7584\r\n        9610        6632        9036       11327       11665        9187        1417        4616\r\n        3768        1590       10149        3400        4707        5141        3973        5828\r\n        9623        3436         997        6446       10458        5845        8785        4056\r\n       10876        4226        7775        4853        8562        9793        8662        8391\r\n        6182        3663       10851        2565        5271        5953       11059         715\r\n        7529        4284        2244        8402       12053        7689         541        5153\r\n        8110        5951        6609         335        3517        6766        6683        3012\r\n        4955        3196        8713        6749         246        5847        2747        7607\r\n        8485        2656        4214         985        4827        2550       11484       11457\r\n        3124        1611        3563         382        1919        3058        8499        5773\r\n        1423        2757        1327       11084        6349         455        1929         417\r\n        9081       10604         613       10502        7325        8929        7125        8483\r\n        9465       10411        2134        3556         986         530        9950        1448\r\n       10951        8652        3309        7995        6767         315        3390         507\r\n       11937        8868       12228        5740        6625        1837         388       11209\r\n        3858         829        1248       12057        7320        3536        2756        5663\r\n       10974        3976        8957       11721        4338        9767       11429        8609\r\n        6564        1713        9831       10924         912        9766        6373        7233\r\n        3700        1269        4315        8607        4546        8196        9941        7644\r\n         878       11980       10491        2706        3912        9513        2567        8190\r\n         643         379        4709        9262       11112        1860        4737       11022\r\n        1323        3739        8110        8196       11511        2236        8007        9557\r\n       12024       10027       11760       11831        2394         500        6894         389\r\n        8560        4490         168        8370        1272        5988        6261        8753\r\n        4943        2775        2362        2464       10956       12172        2558        5757\r\n        1707        7697        6861        2490        6732        4772        9887        8195\r\n        4315        3792        1287         580        3928        6061        9571        1812\r\n         783        5756        2086        9790        5118        7639        6449        7058\r\n       11898        3862        2787       12173        4520        5695         212        1507\r\n        5346        9561         654        9830        5535        5850        4702        3448\r\n        5173        7945         740        8601        2757        3408        1153        7755\r\n        8707        6923        4891       11539        7076       10827       10657        1702\r\n       11981        8090        5301         856       10035        6307       10356       11679\r\n        9453        2222        1223        9948       10545        6152        6122        5565\r\n       10032         113        4049        4053        8376       11269       10270        1305\r\n        2126        1286        6659         881        3528        8590       10609        9754\r\n       11887        3876        1784        6936        1034        6381        1763       12219\r\n        5357        1336        7692        2667        7716        9060        8531        5530\r\n        5945       11985        2654        2560        6865        4738       11695         801\r\n        5824        6645        6710        6940        2131        2231        1940        4950\r\n         746        3214        6012        8276        8247        3992        9384        5457\r\n        7807        4112        9317        2273        1130       10350        8548        8193\r\n        3642        8624        8082        4908       10678        1835        7296         848\r\n       10867       11073        9696        2669        5746        1260        7347        4877\r\n       11495        5170         991       10986        8479        5253        2646       10197\r\n        5326        2942       11788        1621        2984         294       10203        9804\r\n        4494        2383         294        5111        1339       10478        8541        9356\r\n        2187         172        5127        8875        8915         789        2201        5140\r\n        5160       10441        4096        9010       10106       11593        6362        9775\r\n        4295       10907        1824        1894        5510        5337        9922        3371\r\n        8127        5345        4006        5209       10033        2859        4601        5742\r\n        3793        8908        6314        7883        8653       11031       10085        5403\r\n        3859        7385        4404        8611        5490        3923        8187         480\r\n        9766       12182         901        3569       10257        6668        3709        6931\r\n        2481        9841        9271        2472       11437         391       12053        4980\r\n       11992        3545       10994        9307       11791         322        3678        9815\r\n        1904        6151       10952        4868       10935        8320        9856        9570\r\n        6752         254        9868        7270        9019        3920        1414        1270\r\n       10364        4598       11037        3621        3234        9263       10271       11024\r\n        5000        1675        5510        7650         440        5417       10452        1974\r\n        5922        4450        6056        5245        1126        2867        2645        7398\r\n        1290        5398        5692        8986        3751        7506        2269         488\r\n        1909        5213        4327        8275        5794         534       11630       11908\r\n          53        6833        5782        5954        9629        5503        2239        5699\r\n       10980       10804        4193       10615         236        6096        2895        8953\r\n        5824       11122        7883        2053        6154        4548        5701        5575\r\n        1747        6519        6826        4063       11574        7423        9720        4636\r\n       11375        6605        1810       10176        6039         587       10520       10310\r\n        1100        3029        3862        8091       10427        4614       10870        2274\r\n        8785       11559        6971        4077         925        4907        5804        7863\r\n        3296       12221        8683        3445        3022        7680       11201        3591\r\n         773        3117        2404         968        4600        2668       10518         468\r\n         215        6056        7487       11731        2328        6050        2762        8744\r\n         677        1468        5443        8970        9622        8609         391        9362\r\n         850        9053        2676       10194        5487       10187        1917        5679\r\n        2586         415        8841        4002        8329        9123        1706        6923\r\n        4346        8013       11694        7230        4849        5346         958        9474\r\n        5828       10768        3592       11256       12214        6209        5772        4820\r\n        8719        3551        5014         187        8099        3338        9425        8455\r\n       11193        3088        2025        7193        3726        6393        4237       10983\r\n        7306        3335        9019        6401       11140        9163        2214        8141\r\n        2901        2598        7927        9127        3635        7468        2931        9271\r\n        6188          83        8923        5051         213        3750       10987       11744\r\n        3529         829        6394        6310        8428       11624         392       10014\r\n       10407        6190        4717       10647        8578        4959        8400        9476\r\n       11730        3977        3410       10130        8954        3171        6364        1588\r\n        9146        6763       11717        2289       12045         329        1489        4830\r\n        6135        7211         568       10450        9554        3037        5529           9\r\n        3995        1662       12272       11074        5671        1469        7367        4854\r\n        4549        8081        2025        3242        8687        5009        9142        2004\r\n       10161        2020        4083        1333        4743        5254         194        6885\r\n         226       11966        6259        3939        5004       11828        3961       10006\r\n        3966        8223        9013        2152        7274       11391         120        3271\r\n       12189        1703        2227        5125        5269        7245        8442        8137\r\n        4036        7892        8282        9848        5536        4409        7306        8365\r\n        4788       10993       10891        2650        5180        9379        5748        3115\r\n        1578       10933        4035        6497        8116        7574        4788        9184\r\n        7292        2637        2829        2197        6402        5607        8814        3297\r\n       12163        6864        3905        4982        6710        7802       10831        1012\r\n        8629        6883        3876        2358        3998        1465        7663        5577\r\n        1940        6499        1233       11847        8131        8283         577        8690\r\n        8536        1709       11555        7614        1606        7021        2479       10991\r\n        7673        6724        9074        5707        3131       11008       10835        5567\r\n        6706         321       12276        3407        4790       11852        3340        4209\r\n         887        6216         706        7716        9370        2905        8846         170\r\n        9563        7171        5219        7938        3769        7095          68        2976\r\n        3914        3867       11656         347        7395        8967        5517         894\r\n        2589        4130       10205        5493        3040        4057       10259        5751\r\n         248       10770        3157        1900        1428       11496        3608        7411\r\n       11099        5378        8023        1291       11427        1273       10747        9407\r\n        1600         586        9694        4493        2681        3073        2814        9289\r\n       11724       11813        7932        7385        3639        1582         135        5276\r\n        8990       10675       10619       10670        6193       10730        4885       10384\r\n        5511        2999        4965        9346         562        3131       11572        6417];\r\nassert(isequal(numberTheoreticTransform(r(:)',n,p),rhat(:)'))\r\n%%\r\nn=256;\r\np=3329;\r\nr=[17789       14064       31398        7235        4452       54042       17029        9244       41506       19221       25101       62219       54529       40064       62923       42789\r\n       56877       51841       65136       14198       13625       50288       57544       47986       60999        3380       64958       16802       49403        3404       61808       25001\r\n       48595       42905       39615       53151        2595       65348       12338       45289       64079       33038       18797       64871       40754       37730        7385       19662\r\n       29351        1713       61925        9087       30759       14919       49754        2260        6133       50356       46280       22925       25827       55203       42486       22291\r\n       46506       51496       32141       57796        9836       60263        2076       32037       43367       18545       35075       13665       23545       32750       31509       60222\r\n       61887       60461       28701       60526       64966       42074       42096       63661       39503       14769       12662       43635        5823       28771        4359       29901\r\n       11410       32264       50636         835       27987        6902       37150        7369       31052       21711       45182       63789       22392        9768       58836       28999\r\n       16029       54657       48763       24717       62611       17574       24668       48707       23347       29704        3306       40809       35957        1853       32586       29765\r\n       42003        8608       29026       10997       47464       50059       13929       41847       31167       48325       12087        4164       30182       49589       50548       61950\r\n       52993       49793        3473       35404       38069       52789       51914       38940       43976       33415        2992       24478       42300       52173        3955       14360\r\n       55926       60669        5755        6662       35406        6832        9531       32677       62891       25068       58002       10895       33654       19238       17200       57829\r\n       26091       54572       52296        2573       46231       30786       32056       37214        5838       59341       55036       15157       53374        7550       42668        1302\r\n        7569       17000       42964       61160         329       14356         841       27951       52280       63259        7743        3421        6368       24582        8755       22397\r\n        5261       13960        2119       63674       51282       60470       12229        4996       38717       41174       26896       59097       30389       54322       41847       50202\r\n       23623       34230       36507       23653       60742       20992       31800       19043       59781        8652        7879       51989       38654       55166       25227       22465\r\n       54323       26041       47172       42218         543       56199       54933       36787        6627       40521       37492       24445       12266       43597       50180       40554];\r\nrhat=[1004        2562          12        2074         386         769        3081         923         234         324        3276        3310        1457        2786        1538        1203\r\n        2725         438        1145         992        1049        2056          79        1027        2483        2538           4        2926        2235        1721        1323        1176\r\n         545         183        3074         501        2640         855        1161        2419        1203         813        3106        2589        2852        1456        1682        1723\r\n          69         623        2891         274         813        1126        3161         654        3073         693        2162        3089        2845         612        3031         293\r\n         592        1068        1284         749         404        1253         426        1999        2639        2516        1092        2077         453        1649        2336        3163\r\n        2039         495          51        1711        1959        1829         200        3273        2348         141        1326         760        3201         400        2955        3279\r\n        1452        1853        1912         973         666        1696        2042         965        1095         210        3132         160         766         740        1804        2075\r\n        2315        2977        2197        3084        1811         340         140        1425        1279        3194         758        2224        2692        1839        2400        1477\r\n        2429        1148        2851        2684        3141        1255        2870        3295        2652         899         716        1549         406         517        1155        2856\r\n        3116        1361        1678        2120        1646         915        1518        1631        1685        2535        2169        1417        1796         828        1899         911\r\n         309         738         754        2490        1194        2757        1105        1086        1929        1892        1032        1186        1805        1880        2454         621\r\n        1514        1448        2271        2505         856          47        1949        2456        1700        1041         112        2844        2723        1705         981         781\r\n        3026        1470         145        2240         171        2116        3322        2831        2726        1800        1228         873        2341        2557         246         887\r\n        1275        2019        1990        1422        1937         820         605         925        2552          93        1790        1408        1989        2718        2353        3141\r\n        1249         376         831        1305        2941        1750        2254        2190        2703         774        1527        1776        1829        2916         661        2171\r\n        1503        1689        2004         368         643        2582        2079        2419         999        2909        3317         764        1712        2256        2638        3065];\r\nassert(isequal(numberTheoreticTransform(r(:)',n,p),rhat(:)'))\r\n\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":145982,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-10-06T20:08:10.000Z","updated_at":"2026-05-25T03:12:04.000Z","published_at":"2020-10-06T20:17:36.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven an input polynomial (r) of length \u0026lt;=n and a prime number (p) such that an nth root of unity exists and the modular inverse of (n) modulus p exists. Convert the polynominal coefficents by Number Theoretic Transform (NTT) using the primitive nth root of unity as the generator mod p.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44961,"title":"RSA decryption","description":"Decrypt a large integer string using RSA decryption given the public key (n) and private key (d). Convert the large integer decryption into an output message string with UTF-8 representation.\r\n\r\nExample:\r\n\r\n encrypted_message = '158037161019988039882393476857386648994978438821991287680442802412825849535544067751541256843540494019';%input\r\nn='418336393847020647250825879743341651032293545176800777981294580200903315345456262337972725306797613061';%input\r\nd='8444986024072025211908427894173383040354675378319105204646840203847580180874615752845913488969020869';%input\r\ndecrypted_message = 'I like to swim!';%output\r\n\r\n\r\n","description_html":"\u003cp\u003eDecrypt a large integer string using RSA decryption given the public key (n) and private key (d). Convert the large integer decryption into an output message string with UTF-8 representation.\u003c/p\u003e\u003cp\u003eExample:\u003c/p\u003e\u003cpre\u003e encrypted_message = '158037161019988039882393476857386648994978438821991287680442802412825849535544067751541256843540494019';%input\r\nn='418336393847020647250825879743341651032293545176800777981294580200903315345456262337972725306797613061';%input\r\nd='8444986024072025211908427894173383040354675378319105204646840203847580180874615752845913488969020869';%input\r\ndecrypted_message = 'I like to swim!';%output\u003c/pre\u003e","function_template":"function decrypted_message = RSA_Decrypt(encrypted_message,d,n)\r\n  decrypted_message = 'I like to swim!';\r\nend","test_suite":"%%\r\nm='158037161019988039882393476857386648994978438821991287680442802412825849535544067751541256843540494019';\r\nn='418336393847020647250825879743341651032293545176800777981294580200903315345456262337972725306797613061';\r\nd='8444986024072025211908427894173383040354675378319105204646840203847580180874615752845913488969020869';\r\nmessage = 'I like to swim!';\r\nassert(isequal(RSA_Decrypt(m,d,n),message))\r\n%%\r\nm='99761240327251194937668282784881225946299704960682605372368300120848092908591455333889649815054891832920433772438140697229743047905660648016064750862552787515723449901585029566883857331537912668878918706229773460108043130645834201085405226084010413433457258525704626839479467337463012332940952234541291286602023412313079939518264986360860440514106228995186076871512459300140814669747884371890194124';\r\nn='2044371069952561243871813747701535503388267616657953475148898181142012590397809234167373308955772082860082985954286137615597515257087506574051530104475374974920093127841789408014496870693507622812332673504654584870100580476794800708440785082437228308551107726064054828640053321250498545183042994878498928173976370185712833904492317580152665428272199317847097773542066059565512439224992672101163367819';\r\nd='131358569595346680469722564224349085322306406056832660693226883146757023027681686453130430199929142940992255578581548217026735077095312421736286269892515739496597527524021166625708322359741224036234988705082882699895245449017415856831226734459122764117157172961113990706104659539690141481103935528273973382371183154438463086325519326121228923730136467766036183357672350586091588640276470994058874793';\r\nmessage = 'First, have a definite, clear practical ideal; a goal, an objective. Second, have the necessary means to achieve your ends; wisdom, money, materials, and methods.';\r\nout=RSA_Decrypt(m,n,d);\r\nassert(isequal(RSA_Decrypt(m,d,n),message))\r\n%%\r\nm='23055330962704323769878549529115024711681463755869375992447358448257597289666397997393945478297323372847937346015110864832142828615195632399869833059964395947034849804307978652470092440225822549097020437344501330227622390881049097656181781980124142659454562923055085607225529541369345564097544873012395850084756321027482195140106724706102857159831541577386517426252851135917933602432485339455137853480425755360413522537180403768732770990453717801905433194599201771294559164174484366213507079896512750859909040228686293902666244968559905065091421969981123917913807285202549025830873320919273470513619143052127674709353494599047667391749001044180280253098309127910967801244777547067797257847396027373615105278074126621948502699483945442597921239032762535839481741386676784123570880434889025648597593711911259097408384527597630246903648267481820555872340696607290773027177673329986283293168575528258578575033034179306629426942699054620008867304832513427178319015491321861291086200676826846740503213061372327825278234127270730394825241275398344604061667061892125034848354745243512927194104643800196788262228848172689053393410972717679060779374106225778531311917434399927265718394014531619654762559111657295163989321031700962115070401533540746801357628868609255941469541707984326602981168016264720519340003693219403163278843126513912057794102645161814569893320900148891952732032827189210372803453777673309727629845360314416264926243414888253428149538146304399700653423118791724424837155323935319171814705469113037576054893140065594406360362408167462563925199225169055130480445976569760198217276692195730775524801021060293503580957364539210146595506177369243830063105693396808209631409860229448190041996912370291877581261016834769083953407597967437841733812344230896457097759403613339974363380556576775192238298546579676296359906794868505900966056565863994904406875527092603782089107591766239203672780399852597928423220774857864301715176837587724591257928112610743311445186658958007825888475800717425136849205837547536117562642680';\r\nn='44842008376687803367401704855862174057751658874412319214254366772513580664834338103711248053800336314605433363839051280089244765499067546768593375603554906634041005882836398715141891796137918851678901692978242458027064144613729242304549721699241688731628849316717564354846304644497408201649639245333007226999680183209765262168096747256549932760714835163829498814356014506466022681051474161134416048209239076204778228261950266231966892099194904195589855692846886804440024833800891898474520523721428667143505915995217245366095168045283186412220277535584022415882288464175935131208288146822299727657938865855853054174274144036081729604573534323903573564345990066095029031133576597298513883268061041004553754062342541175017746399640362184228727837219293246060058323563065371426108553463224921179093569734439828568435658070810483844709529476015493154462587917722806971578721201741643550136095893915598053953011321347017522943202801820158823344592288227452688958887872572522782675644714289349442555731461112275849176682816627205790162163242534462778708459909915826953853537650009043468930450287584181263070727575137424307931682948818963610116434419852585517456098152109913486868606969385133300794635427715727859479612757775192862801547635779921771426233897718528292667112148507798722127322607462037572360649156150337532940752245966765201596748120586383127825681873289878451591957019880886587602576426267683951317718936847177641679183717321862429721190212389237395890951104472937261743929446841800523276218816808066432194473964294708564511969507018959711475230849760902049071415838600456901108854663197095849833602808088094456341330276320302662577163562657750920481798540333412112880539002713127873625438212479154166226846436038698572615482646490520911334607730484144343834476612603467400722323562384577281087071440008144012534847478593412147312743274523095010025887231371129897419306984829625295595332434377173711414555784685658774002600022998291909172576630780759729269867715816511099791566815282862210319017784751883446582453959';\r\nd='6565816445407878925089441991498747612160839198603241148969054176252198301108538816900577327681584864655900309129187116952811278662878254707896639032786256375181309679291058212467790617143590175026484896254317631677185521639888090836542092702228103896557828982761215001435908561096741216458335569193212038242350596427680660630865867932219252556141104387324837429582683296520255026128293117631961432452139374326884841820676484348718346835892309697741432400454075190738155214690226263908349465883864526754491093121291860880617807231984031261908018115438062149668224668541927056762969514272957080528641551440449912383177971011340773567381595669197227397095749281691989236351340479846140946699426488082757798251098448588056674770754671643802109836061405587518383808732642252532232749119323532411226969424472963287039513173436339822906642733903667734412972727748722945805719038892729473535651253460092450459024621811281873600606895132005494130257832946742084673535377347237875132451694131873556170785954511701761479583203082125987308962572083138475191433479413530168163642321800669575367485309537509082240852273955663933547032858727060003363699057579114677450927657082252183166216411898446363400905797006665507108575986344278988745322935954278566001664060256959781130851422164231215979527541890717097899466088129536122753240075629363188908753526503257284825826946057815889269456925910606842810401392503996936381736547569711529540423604489529836981870198914784848089842823822656591932437295439032050213520000125621624734133380868707883468507784350355251698659006842220673722867592264770652693364873913361956132623339827358272992906883218249723187904101702943978747751691915678242891407493339828656260188711974558580485287404900248093520605466868886511014909072710668999090169680693653605673085931199903788603401848851956524927526592113342563256817891019122155938256416456381627581783190684277329685396205824988569434587069719626936811661218907123975873968219302538615272967013401476757929690613750478379916399826582503908605565505';\r\nmessage = 'People who succeed have momentum. The more they succeed, the more they want to succeed and the more they find a way to succeed. Similarly, when someone is failing, the tendency is to get on a downward spiral that can even become a self-fulfilling prophecy.';\r\nout=RSA_Decrypt(m,n,d);\r\nassert(isequal(RSA_Decrypt(m,d,n),message))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":3,"created_by":145982,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":"2019-09-08T23:48:25.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2019-08-29T18:26:28.000Z","updated_at":"2026-05-25T03:04:28.000Z","published_at":"2019-09-05T14:41:54.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDecrypt a large integer string using RSA decryption given the public key (n) and private key (d). Convert the large integer decryption into an output message string with UTF-8 representation.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ encrypted_message = '158037161019988039882393476857386648994978438821991287680442802412825849535544067751541256843540494019';%input\\nn='418336393847020647250825879743341651032293545176800777981294580200903315345456262337972725306797613061';%input\\nd='8444986024072025211908427894173383040354675378319105204646840203847580180874615752845913488969020869';%input\\ndecrypted_message = 'I like to swim!';%output]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":44963,"title":"Mask Generation Function (MGF1) for PKCS #1 Standard utilizing Optimal Asymmetric Encryption Padding for RSA Cryptography","description":"Create Mask Generation Function (MGF1) from PKCS #1 v2.2 standard (B.2.1 page 50) at below link. Input will be character array (mgfSeed) which will need to be converted to octet array using UTF-8 representation. Output must be octet array (uint8) of length, maskLen (input). Hashing must use SHA-1 (160 - 20 byte).\r\n\r\nFor example:\r\n\r\n  mgfSeed = 'I like to swim.';%input\r\n  maskLen = 20;%input\r\n  mask = [170,251,101,210,23,101,10,242,193,163,174,148,104,138,228,245,52,234,0,195];%output\r\n\r\n\r\n\u003chttps://www.emc.com/collateral/white-papers/h11300-pkcs-1v2-2-rsa-cryptography-standard-wp.pdf\u003e","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 194px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 97px; transform-origin: 407px 97px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eCreate Mask Generation Function (MGF1) from PKCS #1 v2.2 standard (B.2.1) at below link. Input will be character array (mgfSeed) which will need to be converted to octet array using UTF-8 representation. Output must be octet array (uint8) of length, maskLen (input). Hashing must use SHA-1 (160 - 20 byte).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eFor example:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 60px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 30px; transform-origin: 404px 30px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003emgfSeed = \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); \"\u003e'I like to swim.'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e;\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); \"\u003e%input\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003emaskLen = 20;\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); \"\u003e%input\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10px; transform-origin: 404px 10px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003emask = [170,251,101,210,23,101,10,242,193,163,174,148,104,138,228,245,52,234,0,195];\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(2, 128, 9); border-block-start-color: rgb(2, 128, 9); border-bottom-color: rgb(2, 128, 9); border-inline-end-color: rgb(2, 128, 9); border-inline-start-color: rgb(2, 128, 9); border-left-color: rgb(2, 128, 9); border-right-color: rgb(2, 128, 9); border-top-color: rgb(2, 128, 9); caret-color: rgb(2, 128, 9); color: rgb(2, 128, 9); column-rule-color: rgb(2, 128, 9); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(2, 128, 9); text-decoration: none; text-decoration-color: rgb(2, 128, 9); \"\u003e%output\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eSee: https://www.foo.be/docs/opensst/ref/pkcs/pkcs-1/pkcs-1v2-1d1.pdf\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function mask = mgf(mgfSeed,maskLen)\r\n    mask = []%octet array of length maskLen\r\nend","test_suite":"%%\r\nmgfSeed = 'I like to swim.';\r\nmaskLen = 20;\r\nmask = [170,251,101,210,23,101,10,242,193,163,174,148,104,138,228,245,52,234,0,195];\r\nassert(isequal(mgf(mgfSeed,maskLen),mask))\r\n%%\r\nmgfSeed = 'People who succeed have momentum. The more they succeed, the more they want to succeed and the more they find a way to succeed. Similarly, when someone is failing, the tendency is to get on a downward spiral that can even become a self-fulfilling prophecy.';\r\nmaskLen = 825;\r\nmask = [40    55   251    83   143   211    49    16    87    56   109   116    17   188    72    54   231    67   125   182   223    53   149    36   246   233    71   113   113    29   176    61    74    77   205   167   179    99   172   172   212   249    53   148    13    89   253     3    85     0   155   181    94    15     8   122   120    92    29   229   229    46   192   162   252   119   243    94    52    12   133    98   161   249   152    68    69    22 156   155   229   132    30   163   102    90   217   160    36    53   185   128    74   224   105   214   134    90   188   101    98   220    43   162   251   183    72    54   219     4    44   185   199   193    31    42   129   112    74    25    81   133    70   156    38   225   243    24    50   132    79   130    42    72   145   158   105   253   190    62    73   107    95   133    78   238   214    39    88   245    40    46   166   201   173   209   234   194     3   117   226    32   176    12   139    83   211    67   101    99   174   126   103   130    83    56   157    52    76    21   189    99   217    43   150    92   219   148   173   138   184   183   203    62   231   125    42    79    29   248    30    26   127    50     9    31   219   212   160   157   243    15    52   217     7   144    43   241    70    98   247   231   174   111    96    91   108    59   164   146    77   180   212   155   165    18   150   198   100   233   255   129    44   170   150   243   244   126   203   255   231    28    94    77    58   198   147    84   212   248    36    34   107   210    77    79    80   224    24   126    88   125   255    11   124   227    44   253   242   234    59   176   108   146   170   132    20   128   230   141   187    81   139    26   119    47   159     8   182    40    94   213   107   223    21   187    35     1    40   145   154    92   218   252    46   201   203    65    48    94   118   110     5   158   212    19   129     9    59   124    19   190   201    55   141   124   210    85   240    34   150    81   154    11     3   116   204    37    73    39   169   234    50   241   131    94     9   133     0   209   190   171   206    93   165   145   142   117   230    34   245   223   205   153   227   202     3   218    56   163   203   137    46   114    78   248   196   136    19   177   126    95   231   213   251   130    39    57    59   228   251    73    48     9   109    11   172   152   223   120   153   108   233    76   117   219     8     6   124    44     7   249   116    43   198    98    55   235    52    47   146   239    76   142    88    19    64   169   209   206   255   101    91   197    36   180   253    52    54    63   198    63   113    58    43   229   141    59     4   147    34   200     7   184   108   176   153    95    94    18   235   178   160    51    63   154   227    39   231    71   246   109    13   199   185   220   180    50   146    85   194   176   175   159   103   177    47   149   252   182    33   224   226     1   232   167    94    93   202    55    60   201   228   107   135   142    22   165   168   124   219   216    34   230   174   179    66    67    91    92   186    30    48     9   177   192   254   190   236    52    84   114   255     3    81   150   112   131    49   157    48   243   218    11   132    40    13    68   214   159    36   109   152   219    32   219   193   182    69    37   104   217   115    54   158    79    90     3   223   116   127     8    28   233   223   194    30   241   180    45   209    77   180   184    52   132   164    49   206    50    57   149   118   105    44    25   212   141   253   150    45   244    73   203    10   137   161   118    49   165   248   105   215    58    90    67   180    78    22   135   102   173   139    62    31    63    82   187    78    47     2   176   144    83    22   143    31   155     8   160    12   252   164    52   159   102   113    95   132    49   168   194   137    32   131   189   139    77   143   248    36    30   154   239   163   149    86     9   178   122     6   248    25   208   211   142    59    23    72    43   158   195   244   232   229   245   148    49   163    28    81    93   106    33   239   249    26   220    73    65   112   249   254   251    91   106   204   113    10    59    34   114   189   185   188   181   100   185    22   188   221   187   109   170    35    57   181   234   157   230   206   169    97   140    51   170   128   215   188    14    50   190   167    41   173    19    10    98   165    12    77    49    21    33   147    93    84   163   106   151   234    76   252    91   165   248   223   136    85   124   174    90   188   130   174    83    92   181   134    65    91   105    15   103    91    15    27   201   162    58    84   164    33   137    63     0    84     0    78   180    31   218    47    84    43    13    35   122   117   205    59    81   146    97    14];\r\nassert(isequal(mgf(mgfSeed,maskLen),mask))\r\n%%\r\nmgfSeed = 'First, have a definite, clear practical ideal; a goal, an objective. Second, have the necessary means to achieve your ends; wisdom, money, materials, and methods.';\r\nmaskLen = 300;\r\nmask = [96   185   223   209   149    51   224   128   249   139    57   249   190    69   199   132    37    24    75   127    98    59   231   206    13    83    79   111   181   220   204   120    27   178   155   116   155    11   157   112    10   195   106     4   127   146   101   112   197    29    54    45    14    78    16   100     1   111   156    44   138   141   219   101   171     1   126   254    60    82   214    63    13    94   227   238    64   173    93   142     3   224    63    76     8   190   105     8    77   113   250   243   249    82    56   124   129   116   121   131   207   116    80   185    72   184   244   232   236   127    37   195   236   163   176   245    65    48   169   131    19    36   208   178   184   150   188    50   221    83   132   241    73   205    23     9    41    74    65   251    10    21   133   150   101    15    42   153   164   197   136    19   113   134   153   247    10   161   221   184   195   215   253   149   223    83   180   148   198     4    53   112   114    39    18   132   254   170   130    61     6   158   224   166    76   187   190   128    14   251   158   218   192    68    46   210   199   231   120    57   212    10   107     2    50    37   110    30    87    48     2   134    81   165   107    95   250   206    98    26    45   102   173    46    85   103   137     4   140   154   236   142   125   211    28   164    94    41     8    70   215    10    13   140   139    97   205    60   212   205    35   175   235   158    88   165    40    41   187   215    72   172    97   159   119    79    80    31   128   121   108    94   219   216    77   209   184   244    90   238   182   207   244    52   248     6   220   175   117   122   236   228   219    42    49   196   186    12    19    95];\r\nassert(isequal(mgf(mgfSeed,maskLen),mask))","published":true,"deleted":false,"likes_count":1,"comments_count":4,"created_by":145982,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":5,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2019-09-06T22:10:21.000Z","updated_at":"2026-05-25T02:42:38.000Z","published_at":"2019-09-06T22:31:17.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCreate Mask Generation Function (MGF1) from PKCS #1 v2.2 standard (B.2.1) at below link. Input will be character array (mgfSeed) which will need to be converted to octet array using UTF-8 representation. Output must be octet array (uint8) of length, maskLen (input). Hashing must use SHA-1 (160 - 20 byte).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[mgfSeed = 'I like to swim.';%input\\nmaskLen = 20;%input\\nmask = [170,251,101,210,23,101,10,242,193,163,174,148,104,138,228,245,52,234,0,195];%output]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSee: https://www.foo.be/docs/opensst/ref/pkcs/pkcs-1/pkcs-1v2-1d1.pdf\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44765,"title":"Lights Out 11 - 5x5, with wrapping, x moves","description":"\u003chttps://en.wikipedia.org/wiki/Lights_Out_(game) Lights Out\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves the first problem in the series\u003e for an introduction.\r\n\r\nThis problem contains boards that each require any number of moves to solve. However, now wrapping of the lights occurs. For example, if \r\n\r\n board = [1 0 0 0 1  \r\n          1 1 1 0 1  \r\n          0 1 1 1 0  \r\n          1 1 1 0 0  \r\n          0 0 0 1 1]\r\n\r\nthe answer is:\r\n\r\n moves = [2 4 13 25]\r\n\r\nPrev.: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44764 5x5, wrapping, 6 moves\u003e — \r\nNext: \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44766 5x5, 3 stages, \u003c7 moves\u003e","description_html":"\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Lights_Out_(game)\"\u003eLights Out\u003c/a\u003e is a logic game wherein all lights need to be turned off to complete each board. See \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\"\u003ethe first problem in the series\u003c/a\u003e for an introduction.\u003c/p\u003e\u003cp\u003eThis problem contains boards that each require any number of moves to solve. However, now wrapping of the lights occurs. For example, if\u003c/p\u003e\u003cpre\u003e board = [1 0 0 0 1  \r\n          1 1 1 0 1  \r\n          0 1 1 1 0  \r\n          1 1 1 0 0  \r\n          0 0 0 1 1]\u003c/pre\u003e\u003cp\u003ethe answer is:\u003c/p\u003e\u003cpre\u003e moves = [2 4 13 25]\u003c/pre\u003e\u003cp\u003ePrev.: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44764\"\u003e5x5, wrapping, 6 moves\u003c/a\u003e — \r\nNext: \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44766\"\u003e5x5, 3 stages, \u0026lt;7 moves\u003c/a\u003e\u003c/p\u003e","function_template":"function moves = lights_out_11(board) % 5x5 board, with wrapping, any number of moves\r\n moves = board;\r\nend","test_suite":"%% \r\n board = [1 0 0 0 1  \r\n          1 1 1 0 1  \r\n          0 1 1 1 0  \r\n          1 1 1 0 0  \r\n          0 0 0 1 1];\r\nmoves = lights_out_11(board); % [2 4 13 25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 0 1 1  \r\n          1 0 0 0 1  \r\n          0 0 0 0 0  \r\n          1 0 0 0 1  \r\n          1 1 0 1 1];\r\nmoves = lights_out_11(board); % [1 5 21 25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 0 1 0  \r\n          1 0 1 0 1  \r\n          0 1 0 1 0  \r\n          1 0 1 0 1  \r\n          0 1 0 1 0];\r\nmoves = lights_out_11(board); % [2 6 8 12 14 18 20 24]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 0 0 0  \r\n          0 1 0 1 1  \r\n          0 0 0 0 1  \r\n          0 0 0 1 1  \r\n          1 0 0 1 1];\r\nmoves = lights_out_11(board); % [3 7 17 21 23:25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          1 1 0 1 1  \r\n          1 0 1 0 1  \r\n          1 1 0 1 1  \r\n          0 1 1 1 0];\r\nmoves = lights_out_11(board); % [7:9 12:14 17:19]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 1 1 1 0  \r\n          0 1 1 1 0  \r\n          0 1 1 1 0  \r\n          0 1 1 1 0  \r\n          0 1 1 1 0];\r\nmoves = lights_out_11(board); % [11:15]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 1 0 0  \r\n          0 0 1 0 0  \r\n          1 1 1 1 1  \r\n          1 0 0 1 0  \r\n          1 1 0 1 0];\r\nmoves = lights_out_11(board); % [9 11 14 21 23 25]\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 1 0 1 1  \r\n          1 1 0 1 1  \r\n          0 0 1 0 0  \r\n          1 1 0 1 1  \r\n          1 1 0 1 1];\r\nmoves = lights_out_11(board); % on your own\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [0 0 1 0 1  \r\n          0 0 1 0 1  \r\n          0 0 1 0 1  \r\n          0 0 1 0 1  \r\n          0 0 1 0 1];\r\nmoves = lights_out_11(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n\r\n%% \r\n board = [1 0 0 0 1  \r\n          0 1 1 1 0  \r\n          0 1 1 1 0  \r\n          0 1 1 1 0  \r\n          1 0 0 0 1];\r\nmoves = lights_out_11(board);\r\nb1 = diag(ones(1,5),0) + diag(ones(1,4),1) + diag(ones(1,4),-1);\r\nb1(1,5) = 1; b1(5,1) = 1; b2 = eye(5); b3 = zeros(5);\r\nb_map = [b1,b2,b3,b3,b2; b2,b1,b2,b3,b3; b3,b2,b1,b2,b3; b3,b3,b2,b1,b2; b2,b3,b3,b2,b1];\r\nfor i = 1:numel(moves)\r\n\tboard = mod(board + reshape(b_map(moves(i),:),[5,5]),2); %remove semicolon to display progress\r\nend\r\nassert(sum(abs(board(:)))==0)\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":26769,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":13,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":624,"created_at":"2018-10-31T16:37:46.000Z","updated_at":"2026-05-25T02:30:59.000Z","published_at":"2019-05-02T12:20:39.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Lights_Out_(game)\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eLights Out\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a logic game wherein all lights need to be turned off to complete each board. See\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44751-lights-out-1-5x5-3-moves\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ethe first problem in the series\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e for an introduction.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem contains boards that each require any number of moves to solve. However, now wrapping of the lights occurs. For example, if\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ board = [1 0 0 0 1  \\n          1 1 1 0 1  \\n          0 1 1 1 0  \\n          1 1 1 0 0  \\n          0 0 0 1 1]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ethe answer is:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ moves = [2 4 13 25]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrev.:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44764\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, wrapping, 6 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e — Next:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44766\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e5x5, 3 stages, \u0026lt;7 moves\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":57477,"title":"Solve an equation involving primes and fractions","description":"Write a function to find pairs of primes  and  satisfying the equation\r\n\r\nwhere  is an integer. The function should take a number  as input and produce the triples , , and  such that . If there are no solutions, the function should return three empty vectors.\r\nThis problem is adapted from one in the 2012 European Girls’ Math Olympiad. ","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 146px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 343px 73px; transform-origin: 343px 73px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 320px 10.5px; text-align: left; transform-origin: 320px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eWrite a function to find pairs of primes \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ep\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eq\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e satisfying the equation\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 35px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 320px 17.5px; text-align: left; transform-origin: 320px 17.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"vertical-align:-15px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"136\" height=\"35\" alt=\"p/(p+1) + (q+1)/q = 2n/(n+2)\" style=\"width: 136px; height: 35px;\"\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 320px 21px; text-align: left; transform-origin: 320px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003ewhere \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e is an integer. The function should take a number \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ex\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e as input and produce the triples \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ep\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003eq\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e, and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e such that \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"38\" height=\"18\" alt=\"p \u003c= x\" style=\"width: 38px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e. If there are no solutions, the function should return three empty vectors.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 320px 10.5px; text-align: left; transform-origin: 320px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThis problem is adapted from one in the \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.egmo2012.org.uk/\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"text-decoration: underline; text-decoration-line: underline; \"\u003e2012 European Girls’ Math Olympiad\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [p,q,n] = EGMO2012no5(x)\r\n  p = primes(x); q = primes(x); n = p./(p+1) + (q+1)./q; \r\nend","test_suite":"%%\r\nx = 1;\r\n[p,q,n] = EGMO2012no5(x);\r\nassert(isempty(p) \u0026\u0026 isempty(q) \u0026\u0026 isempty(n))\r\n\r\n%%\r\nx = 2;\r\n[p,q,n] = EGMO2012no5(x);\r\nassert(all(p==2) \u0026\u0026 isequal(q,[5 7]) \u0026\u0026 isequal(n,[28 19]))\r\n\r\n%%\r\nx = 20;\r\n[p,q,n] = EGMO2012no5(x);\r\ns_correct = [35 28 86 178 646 1402];\r\nassert(isequal(p+q+n,s_correct))\r\n\r\n%%\r\nx = 200;\r\n[p,q,n] = EGMO2012no5(x);\r\ns_correct = [35 28 86 178 646 1402 3778 7306 14758 21166 42226 47302 77002 90898 130678 148606 158002];\r\nassert(isequal(p+q+n,s_correct))\r\n\r\n%%\r\nx = 2000;\r\n[p,q,n] = EGMO2012no5(x);\r\nlen_correct = 63;\r\nsum_correct = 265170305;\r\nassert(isequal(length(p),len_correct) \u0026\u0026 isequal(sum(p+q+n),sum_correct))\r\n\r\n%%\r\nx = 20000;\r\n[p,q,n] = EGMO2012no5(x);\r\nlen_correct = 344;\r\nsum_correct = 150118037395;\r\nassert(isequal(length(p),len_correct) \u0026\u0026 isequal(sum(p+q+n),sum_correct))\r\nassert(all(isprime(p)) \u0026\u0026 all(isprime(q)))\r\n\r\n%%\r\nx = 2000000;\r\n[p,q,n] = EGMO2012no5(x);\r\nlen_correct = 14873;\r\nsum_correct = 27402595128;\r\nassert(isequal(length(p),len_correct) \u0026\u0026 isequal(sum(p+q),sum_correct))\r\nassert(all(isprime(p)) \u0026\u0026 all(isprime(q)))\r\n\r\n%%\r\nx = 2e8;\r\n[p,q,n] = EGMO2012no5(x);\r\nlen_correct = 813373;\r\nsum_correct = 152663390088360;\r\nassert(isequal(length(p),len_correct) \u0026\u0026 isequal(sum(p+q),sum_correct))\r\n\r\n%%\r\nfiletext = fileread('EGMO2012no5.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'regexp'); \r\nassert(~illegal)","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":46909,"edited_by":46909,"edited_at":"2022-12-30T13:15:49.000Z","deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-12-30T05:04:32.000Z","updated_at":"2026-05-25T01:33:07.000Z","published_at":"2022-12-30T05:05:56.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to find pairs of primes \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"p\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ep\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"q\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eq\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e satisfying the equation\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"p/(p+1) + (q+1)/q = 2n/(n+2)\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\frac{p}{p+1} + \\\\frac{q+1}{q} = \\\\frac{2n}{n+2}\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhere \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e is an integer. The function should take a number \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"x\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e as input and produce the triples \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"p\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ep\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"q\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003eq\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"n\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e such that \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"p \u0026lt;= x\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ep \\\\le x\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. If there are no solutions, the function should return three empty vectors.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem is adapted from one in the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.egmo2012.org.uk/\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e2012 European Girls’ Math Olympiad\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":44263,"title":"Multivariate polynomials - emulate symbolic form","description":"In \u003chttps://www.mathworks.com/matlabcentral/cody/problems/44262-multivariate-polynomials-overload-multiplication Problem 44262\u003e I asked you to create a class |mPoly| with overloaded multiplication, so a product of two polynomials can be expressed in the form |p = p1*p2|. However, the method of constructing these polynomials is still somewhat unintuitive. In the \u003chttps://www.mathworks.com/products/symbolic.html Symbolic Math Toolbox\u003e, one can simply define some variables,\r\n\r\n  syms x y z\r\n\r\nand then create a polynomial:\r\n\r\n  p = 2*x*y + 3*x^5*z;\r\n\r\nWe would like to do something like that here. As a start, create a class |mPolySym| with properties |exponents| and |coefficients|, and |varnames|,  where the first two properties are the same as in previous problems and |varnames| is a \u003chttps://www.mathworks.com/help/matlab/characters-and-strings.html string array\u003e. The constructor should accept a numeric, char or string input, e.g.,\r\n\r\n  x = mPolySym('x')\r\n\r\n  x = \r\n\r\n  mPolySym with properties:\r\n\r\n        varnames: \"x\"\r\n       exponents: 1\r\n    coefficients: 1\r\n\r\n  r = mPolySym(pi)\r\n\r\n  r = \r\n\r\n  mPolySym with properties:\r\n\r\n        varnames: [0×0 string]\r\n       exponents: 1\r\n    coefficients: 3.1416\r\n\r\nAlso modify the method |mtimes| from the previous problem so it can multiply polynomials with different variable names.","description_html":"\u003cp\u003eIn \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/44262-multivariate-polynomials-overload-multiplication\"\u003eProblem 44262\u003c/a\u003e I asked you to create a class \u003ctt\u003emPoly\u003c/tt\u003e with overloaded multiplication, so a product of two polynomials can be expressed in the form \u003ctt\u003ep = p1*p2\u003c/tt\u003e. However, the method of constructing these polynomials is still somewhat unintuitive. In the \u003ca href = \"https://www.mathworks.com/products/symbolic.html\"\u003eSymbolic Math Toolbox\u003c/a\u003e, one can simply define some variables,\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003esyms x y z\r\n\u003c/pre\u003e\u003cp\u003eand then create a polynomial:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003ep = 2*x*y + 3*x^5*z;\r\n\u003c/pre\u003e\u003cp\u003eWe would like to do something like that here. As a start, create a class \u003ctt\u003emPolySym\u003c/tt\u003e with properties \u003ctt\u003eexponents\u003c/tt\u003e and \u003ctt\u003ecoefficients\u003c/tt\u003e, and \u003ctt\u003evarnames\u003c/tt\u003e,  where the first two properties are the same as in previous problems and \u003ctt\u003evarnames\u003c/tt\u003e is a \u003ca href = \"https://www.mathworks.com/help/matlab/characters-and-strings.html\"\u003estring array\u003c/a\u003e. The constructor should accept a numeric, char or string input, e.g.,\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003ex = mPolySym('x')\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003ex = \r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003emPolySym with properties:\r\n\u003c/pre\u003e\u003cpre\u003e        varnames: \"x\"\r\n       exponents: 1\r\n    coefficients: 1\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003er = mPolySym(pi)\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003er = \r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003emPolySym with properties:\r\n\u003c/pre\u003e\u003cpre\u003e        varnames: [0×0 string]\r\n       exponents: 1\r\n    coefficients: 3.1416\u003c/pre\u003e\u003cp\u003eAlso modify the method \u003ctt\u003emtimes\u003c/tt\u003e from the previous problem so it can multiply polynomials with different variable names.\u003c/p\u003e","function_template":"classdef mPolySym\r\n    properties\r\n        varnames\r\n        exponents\r\n        coefficients\r\n    end\r\n    \r\n    methods\r\n        function p = mPolySym(s)\r\n        end\r\n        \r\n        function p = mtimes(p1,p2)\r\n        end            \r\n    end\r\n    \r\nend\r\n\r\n","test_suite":"%% Test mPolySym\r\nfiletext = fileread('mPolySym.m');\r\nassert(~contains(filetext,'regexp'))\r\n\r\n\r\n%%\r\nr = randi(1000);\r\nx = mPolySym(r);\r\nassert(isempty(x.varnames))\r\nassert(isequal(x.exponents,0))\r\nassert(isequal(x.coefficients,r))\r\n\r\n%%\r\nr = randi(1000);\r\nx = mPolySym('x');\r\ny = r*x;\r\nassert(isequal(y.varnames,\"x\"))\r\nassert(isequal(y.exponents,1))\r\nassert(isequal(y.coefficients,r))\r\nassert(isequal(r*x,x*r))\r\n\r\n%%\r\nx = mPolySym('x');\r\ny = mPolySym(\"y\");\r\nz = mPolySym('z');\r\nw = x*y*z;\r\nassert(isequal(w.varnames,[\"x\" \"y\" \"z\"]))\r\nassert(isequal(w.exponents,[1 1 1]))\r\nassert(isequal(w.coefficients,1))\r\n\r\n%%\r\nm = randi(5);\r\nn = randi(4);\r\nx = mPolySym(\"x\");\r\ny = mPolySym(\"y\");\r\np = [repmat(x,1,m) repmat(y,1,n)];\r\np = p(randperm(length(p)));\r\nr = randi(1000);\r\np_prod = r;\r\nfor ii=1:length(p)\r\n    p_prod = p_prod*p(ii);\r\nend\r\ns = randi(1000);\r\np_prod = p_prod*s;\r\nassert(isequal(p_prod.varnames,[\"x\" \"y\"]))\r\nassert(isequal(p_prod.exponents,[m n]))\r\nassert(isequal(p_prod.coefficients,r*s))","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":1011,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":9,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2017-07-14T23:13:17.000Z","updated_at":"2026-05-25T00:58:31.000Z","published_at":"2017-07-14T23:13:34.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/44262-multivariate-polynomials-overload-multiplication\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 44262\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e I asked you to create a class\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003emPoly\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e with overloaded multiplication, so a product of two polynomials can be expressed in the form\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ep = p1*p2\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e. However, the method of constructing these polynomials is still somewhat unintuitive. In the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/products/symbolic.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eSymbolic Math Toolbox\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, one can simply define some variables,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[syms x y z]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eand then create a polynomial:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[p = 2*x*y + 3*x^5*z;]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWe would like to do something like that here. As a start, create a class\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003emPolySym\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e with properties\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eexponents\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ecoefficients\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003evarnames\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, where the first two properties are the same as in previous problems and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003evarnames\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/help/matlab/characters-and-strings.html\\\"\u003e\u003cw:r\u003e\u003cw:t\u003estring array\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. The constructor should accept a numeric, char or string input, e.g.,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[x = mPolySym('x')\\n\\nx = \\n\\nmPolySym with properties:\\n\\n        varnames: \\\"x\\\"\\n       exponents: 1\\n    coefficients: 1\\n\\nr = mPolySym(pi)\\n\\nr = \\n\\nmPolySym with properties:\\n\\n        varnames: [0×0 string]\\n       exponents: 1\\n    coefficients: 3.1416]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAlso modify the method\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003emtimes\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e from the previous problem so it can multiply polynomials with different variable names.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2511,"title":" BLOCK x3 (Version 4) ","description":"Always in this series ( \u003chttp://www.mathworks.com/matlabcentral/cody/problems/2451-block-x3-version-1/ 2451\u003e, \u003chttp://www.mathworks.com/matlabcentral/cody/problems/2484-block-x3-version-3/ 2484\u003e, and \u003chttp://www.mathworks.com/matlabcentral/cody/problems/2478-block-x3-version-2 2478\u003e ).\r\n\r\nNow we are in color (1 for red  and 2 for white).\r\nYour task is always to count the  *minimum number* of movements required to align the 3 blocks in each colors (vertically or horizontally).\r\n\r\n\r\n\r\n\u003c\u003chttp://4.bp.blogspot.com/-iveYgRwOmLs/Udk9ZctxnUI/AAAAAAAA3Uw/Z9XNHw6f-f4/s400/pack+2+level+2-1.png\u003e\u003e\r\n\r\n* [0 0 0 0 0 0 0;\r\n*  0 0 1 2 0 0 0;\r\n*  0 0 0 0 0 0 0;\r\n*  0 0 1 2 0 0 0;\r\n*  0 0 1 2 0 0 0;\r\n*  0 0 0 0 0 0 0;\r\n*  0 0 0 0 0 0 0]\r\n\r\nIn this example you can move down the first two blocks ( *in two moves* ).\r\n\r\n\r\nNote that blocks can be *swapped* . \r\n\r\n\r\nIn this second example :\r\n\r\n\u003c\u003chttp://1.bp.blogspot.com/-XJP6Mn4X9Ws/Udk9bHEKx_I/AAAAAAAA3Vo/V_Ar3oF9gYw/s400/pack+2+level+2-7.png\u003e\u003e\r\n\r\n* [0 0 0 0 0 0 0;\r\n*  0 0 0 0 0 0 0;\r\n*  0 0 1 2 0 0 0;\r\n*  0 0 2 1 0 0 0;\r\n*  0 0 1 2 0 0 0;\r\n*  0 0 0 0 0 0 0;\r\n*  0 0 0 0 0 0 0]\r\n\r\nHere you win in only *one move* by swapping the two central blocks.\r\n\r\nGood luck !","description_html":"\u003cp\u003eAlways in this series ( \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/2451-block-x3-version-1/\"\u003e2451\u003c/a\u003e, \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/2484-block-x3-version-3/\"\u003e2484\u003c/a\u003e, and \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/2478-block-x3-version-2\"\u003e2478\u003c/a\u003e ).\u003c/p\u003e\u003cp\u003eNow we are in color (1 for red  and 2 for white).\r\nYour task is always to count the  \u003cb\u003eminimum number\u003c/b\u003e of movements required to align the 3 blocks in each colors (vertically or horizontally).\u003c/p\u003e\u003cimg src = \"http://4.bp.blogspot.com/-iveYgRwOmLs/Udk9ZctxnUI/AAAAAAAA3Uw/Z9XNHw6f-f4/s400/pack+2+level+2-1.png\"\u003e\u003cul\u003e\u003cli\u003e[0 0 0 0 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 1 2 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 0 0 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 1 2 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 1 2 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 0 0 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 0 0 0 0 0]\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eIn this example you can move down the first two blocks ( \u003cb\u003ein two moves\u003c/b\u003e ).\u003c/p\u003e\u003cp\u003eNote that blocks can be \u003cb\u003eswapped\u003c/b\u003e .\u003c/p\u003e\u003cp\u003eIn this second example :\u003c/p\u003e\u003cimg src = \"http://1.bp.blogspot.com/-XJP6Mn4X9Ws/Udk9bHEKx_I/AAAAAAAA3Vo/V_Ar3oF9gYw/s400/pack+2+level+2-7.png\"\u003e\u003cul\u003e\u003cli\u003e[0 0 0 0 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 0 0 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 1 2 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 2 1 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 1 2 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 0 0 0 0 0;\u003c/li\u003e\u003cli\u003e0 0 0 0 0 0 0]\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eHere you win in only \u003cb\u003eone move\u003c/b\u003e by swapping the two central blocks.\u003c/p\u003e\u003cp\u003eGood luck !\u003c/p\u003e","function_template":"function y = block3_4(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = [0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 1 2 0 0 0;0 0 0 0 0 0 0;0 0 1 2 0 0 0;0 0 1 2 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3_4(x),y_correct))\r\n\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 0 2 1 0 0;0 0 0 1 2 0 0;0 0 0 2 1 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 1;\r\nassert(isequal(block3_4(x),y_correct))\r\n\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 2 1 2 0 0;0 0 1 2 1 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 1;\r\nassert(isequal(block3_4(x),y_correct))\r\n\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 1 0 1 1 0;0 0 2 2 0 2 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3_4(x),y_correct))\r\n\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 1 1 2 0 0;0 0 2 2 1 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 1;\r\nassert(isequal(block3_4(x),y_correct))\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 1 1 0 0 0;0 0 2 2 0 0 0;0 0 2 1 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3_4(x),y_correct))\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 1 0 0 0;0 0 2 0 1 0 0;0 0 1 0 2 0 0;0 0 0 2 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 3;\r\nassert(isequal(block3_4(x),y_correct))\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 1 0 0 0;0 0 1 2 1 0 0;0 0 2 0 2 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3_4(x),y_correct))\r\n\r\n%%\r\nx = [0 0 0 0 0 0 0;0 0 0 1 0 0 0;0 0 0 1 0 0 0;0 0 1 2 0 0 0;0 0 2 0 0 0 0;0 0 2 0 0 0 0;0 0 0 0 0 0 0];\r\ny_correct = 1;\r\nassert(isequal(block3_4(x),y_correct))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":5390,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2014-08-15T07:15:35.000Z","updated_at":"2026-05-25T00:45:42.000Z","published_at":"2014-08-15T07:17:45.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.png\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/media/image2.png\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAlways in this series (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/2451-block-x3-version-1/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e2451\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/2484-block-x3-version-3/\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e2484\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/2478-block-x3-version-2\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e2478\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e ).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNow we are in color (1 for red and 2 for white). Your task is always to count the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eminimum number\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e of movements required to align the 3 blocks in each colors (vertically or horizontally).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[0 0 0 0 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 1 2 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 0 0 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 1 2 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 1 2 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 0 0 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 0 0 0 0 0]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this example you can move down the first two blocks (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ein two moves\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e ).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNote that blocks can be\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eswapped\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e .\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this second example :\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId2\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[0 0 0 0 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 0 0 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 1 2 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 2 1 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 1 2 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 0 0 0 0 0;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 0 0 0 0 0 0]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHere you win in only\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eone move\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e by swapping the two central blocks.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGood luck !\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray 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\"},{\"partUri\":\"/media/image2.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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\"}]}"},{"id":2451,"title":"BLOCK x3 (Version 1)","description":"\r\n\u003chttps://play.google.com/store/apps/details?id=com.noodlecake.blockblock BLOCK x3\u003e is a simple, fun and relaxing puzzle game.\r\n\r\nThe basics are easy. Solve the puzzles by getting *3 blocks* in a row or column. \r\n\r\nThe puzzle is coded a 6x6 matrix and the 3 blocks are indicated by 1 (and 0 for free space).\r\n\r\nIn this example :\r\n\r\n\u003c\u003chttp://1.bp.blogspot.com/-GbZ-AMjylSs/Udk6kRyp4uI/AAAAAAAA3Pk/3jdWrVSqQpc/s400/pack+1+level+1-1.png\u003e\u003e\r\n \r\n \r\n\r\n* [0 0 0 0 0 0\r\n* \r\n* 0 0 1 0 0 0\r\n* \r\n* 0 0 1 0 0 0\r\n* \r\n* 0 0 0 0 0 0\r\n* \r\n* 0 0 1 0 0 0\r\n* \r\n* 0 0 0 0 0 0]\r\n\r\nYou line up 3 blocks in one move by dragging the last block to the top (you can move a block in the free space only horizontally or vertically.)\r\n\r\n\r\n\r\nThe goal in this first problem is to give the *smallest number* of moves to solve the puzzle.\r\n\r\n\r\n\r\n","description_html":"\u003cp\u003e\u003ca href = \"https://play.google.com/store/apps/details?id=com.noodlecake.blockblock\"\u003eBLOCK x3\u003c/a\u003e is a simple, fun and relaxing puzzle game.\u003c/p\u003e\u003cp\u003eThe basics are easy. Solve the puzzles by getting \u003cb\u003e3 blocks\u003c/b\u003e in a row or column.\u003c/p\u003e\u003cp\u003eThe puzzle is coded a 6x6 matrix and the 3 blocks are indicated by 1 (and 0 for free space).\u003c/p\u003e\u003cp\u003eIn this example :\u003c/p\u003e\u003cimg src = \"http://1.bp.blogspot.com/-GbZ-AMjylSs/Udk6kRyp4uI/AAAAAAAA3Pk/3jdWrVSqQpc/s400/pack+1+level+1-1.png\"\u003e\u003cul\u003e\u003cli\u003e[0 0 0 0 0 0\u003c/li\u003e\u003cli\u003e\u003c/li\u003e\u003cli\u003e0 0 1 0 0 0\u003c/li\u003e\u003cli\u003e\u003c/li\u003e\u003cli\u003e0 0 1 0 0 0\u003c/li\u003e\u003cli\u003e\u003c/li\u003e\u003cli\u003e0 0 0 0 0 0\u003c/li\u003e\u003cli\u003e\u003c/li\u003e\u003cli\u003e0 0 1 0 0 0\u003c/li\u003e\u003cli\u003e\u003c/li\u003e\u003cli\u003e0 0 0 0 0 0]\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eYou line up 3 blocks in one move by dragging the last block to the top (you can move a block in the free space only horizontally or vertically.)\u003c/p\u003e\u003cp\u003eThe goal in this first problem is to give the \u003cb\u003esmallest number\u003c/b\u003e of moves to solve the puzzle.\u003c/p\u003e","function_template":"function y = block3(x)\r\n  y = x;\r\nend","test_suite":"%%\r\nx = [0 0 0 0 0 0;0 0 0 0 0 0;0 0 1 0 0 0;0 0 1 0 0 0;0 0 0 1 0 0;0 0 0 0 0 0];\r\ny_correct = 1;\r\nassert(isequal(block3(x),y_correct))\r\n%%\r\nx = [0 0 0 0 0 0;0 0 0 0 0 0;0 0 1 0 0 0;0 1 0 0 0 0;0 0 1 0 0 0;0 0 0 0 0 0];\r\ny_correct = 1;\r\nassert(isequal(block3(x),y_correct))\r\n%%\r\nx = [0 0 0 0 0 0;0 0 0 0 0 0;0 0 1 0 0 0;0 0 0 1 0 0;0 0 0 0 1 0;0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3(x),y_correct))\r\n%%\r\nx = [0 0 0 0 0 0;0 0 1 0 0 0;0 0 0 0 0 0;0 0 1 0 0 0;0 0 0 0 0 0;0 0 1 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3(x),y_correct))\r\n%%\r\nx = [0 0 0 0 0 0;0 0 0 0 0 0;0 1 0 1 1 0;0 0 0 0 0 0;0 0 0 0 0 0;0 0 0 0 0 0];\r\ny_correct = 1;\r\nassert(isequal(block3(x),y_correct))\r\n%%\r\nx = [0 0 0 0 0 0;0 0 0 0 0 0;0 0 0 1 1 0;0 0 0 0 0 0;0 0 1 0 0 0;0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3(x),y_correct))\r\n%%\r\nx = [0 0 0 0 0 0;0 0 0 0 0 0;0 0 0 1 1 0;0 0 0 1 0 0;0 0 0 0 0 0;0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3(x),y_correct))\r\n%%\r\nx = [0 0 0 0 0 0;0 0 0 0 0 0;0 0 0 1 0 0;0 0 0 1 1 0;0 0 0 0 0 0;0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3(x),y_correct))\r\n%%\r\nx = [0 0 0 0 0 0;0 0 1 0 1 0;0 0 0 0 0 0;0 0 0 0 0 0;0 0 0 1 0 0;0 0 0 0 0 0];\r\ny_correct = 3;\r\nassert(isequal(block3(x),y_correct))\r\n%%\r\nx = [0 0 0 0 0 0;0 0 1 0 1 0;0 0 1 0 0 0;0 0 0 0 0 0;0 0 0 0 0 0;0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(block3(x),y_correct))","published":true,"deleted":false,"likes_count":0,"comments_count":1,"created_by":5390,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":11,"test_suite_updated_at":"2014-07-20T00:15:18.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-07-19T23:58:55.000Z","updated_at":"2026-05-25T00:43:28.000Z","published_at":"2014-07-20T00:15:18.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/media/image1.png\"}],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://play.google.com/store/apps/details?id=com.noodlecake.blockblock\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eBLOCK x3\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is a simple, fun and relaxing puzzle game.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe basics are easy. 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vBu5978v3PP/n+ry584zMIr7v1zoJ3cGTBe59/4vNfdEt1C669ofSu0A2bpRG4WL3/hLz3aFFC3/W6uuqpMlUOx1M4wzOT/FvSWFmG1IuTQu2uEuIX0nvOsH3a8p6q0vQS4R7uW5yfgXs7H/PzTYjpK3nOJ3kKu6bhKRQRHURVRcrIXT2Fl3YfldjieM83PV372Sb3UwG33kAi2f10I7hLbcTtrnfrATEQ2fxcE91zE3WdF1rdSHsJTjf40PEUPogz60saXBt3lcfQWdwl567Ky9frzzW7F9RWHThBklU694p0Xoxrpe2lm9LaHdLzLVJdhEdUbBUT36XL0Rf1QhuHjkzwFNbfk6dQBFZMUp1v4ym8fuictLj+AWT+iX2YaYNlByeOGV3I8RQ+kDPTeRY3VJy5OjUpvHgdpoaGhP58o9K/Urp0Y8foikODIwcxRo3t0OihkfEj6otHltQcWbJMjMNLlp1+euniZW3qpOsSv3Kt3lpx/mpR+Qagpef1pwN6dZ3SPlgKTtLqEb3zlB8eeIuERmB0/yp92nYL6dbxq5LQaeacp9AhhTOYvtiidQwXw9/KnBfnX9EyA4ueCRApdG3dJ128cTbXfiOevJFIiXGppu7C8y9ekqTLPF51Lbmlf/KW/vyr+vPvqUsW17QpU5LRSqiLFop5qXr7/qptL1dv3V91+BRY45nLFWcuVe86TAertu2v3n2w5M48c6Vq/1G92nMP4aNvnr4hub2POWdySOEDPDNJQdfyjSamy0X1uStVh89WHTpdefYKKRin84XryfS1VIbGuZq673zpSz/4+jd+8JWv/eCrX/vTr/3+m3rNDdczN/VP0nhHe4YBfRs97fnmChLDp82rGCDmI8V46NOXBbhLGVn0hmPn3Oq95chIb597TVI9jzlnckjhAz1zWHf7lJF1lay/TuCIFeeucM7IRQL0mSKg09lT+tN//Z0//Oef/uyffvzTf/rRj3/5y1++/7Tn+hOLTEA/a5XQE69LJOrF1soT5yd50cszVsqDWisPntQXN+g1oXvLKXz30uuSXPuYcyaHFD7wCyFlNdRReeyc8NWVwGQkyV4yJHQqfS2bux5P3vxk3WFJ2itJeyRpmyQRoN9TlloB/VSZhJ503ZqgrvkR00d30RnL5SbnFJ6B0UNeu8Nd7dXvPfdbeu/SG5Kr7nHnTA4pfBgXWtqmy161d2XVoVOoKHDhFTz0icCdxqNfunrrVKH7RiR+vcb/atWSk5L0//3j//2l5e9dtWaShL7LdZGB2pKt3nWw4swVRvaVyrOXkemNK15BMYPTV1xb9mueKGeYz6jQTDmgHVJov0IzL7bopKd6YnLHkGvtDnnbfnnTbnntNqlj5PxTn3q1cslN7TmC7HVJ+ZW25Oei+c+GMp+WdJbQtRZAP/PUbUjhRI5Y0waz3fMtarrXtXabvGWva+t+16Y98urNWqJbX9KABDBDMM+o0MwkCe2QQltWH10W1F9q1Z9pxLWeDujPNUpLg+cXvfAq45XGJUn64Av//k++9rXvf+Urf/y7vwdA63VWQN+WFN7um3ypDT6KZxtx3SUNyIRd2vYAqo++e/E1SZ4jpPDVdypuMKZffafy9BWHFD6EBRp3rE7IqIucX/SiCegXzkrSX3/zW//8078EKfzxT//5Jz9996X665VP06s39BfevjMpvMNULPDBLUF658JrklL3uJNCt6/68JmF199c+MobFTSuvVl54iLCFx1S+HAWKAB9VX+elOO3ebzjfu4am5+vmHbot57QxEtvac99Tln8dE2r8jhUH33rzE1J8z7+nEl/JqC7vDTc2HqgaTnVRx/mAhfWhaMvNadfakq91Ewjyds0Rku6dMR4Nfup5vJU0UfXkuLNk9ck3TdHONOkdolO9dGHucCK2vACY4QWlPannmqPSUuKm4cvSov9DmdyFjhPWlJc3HW8GG3ncCZngXO+JcWJ9XuRU+hwJmeBj9cCg3gJ4x5J4bG1O6RPOn0KnQU+fgucGtB3I4Un1u+yANrhTM4CH58F0qv3TgpPrN9ZTMFyKIWzwLnfp3DttqKEdiiFs8C536dw3XaLhHY4k7PAOd6n8AQA7ZBCZ4HzZIHSSejQDil0FjhPFkikcLdDCp0Fzh9SeGKmhWYczuQs8PH0FBKgHU+hs8B5QwonqhwOZ3IWOJdJYTmgHc7kLNAhhQ5nchb4OJHCPQ4pdBY4f0jhyQ37OHzUqo4EZzgtoxT3d6ppTutE1nfwoV/IWeAcWSAD+oUWd12UQ/Wilp17n9Jd0pQ2Uv3u81TTnHqj7kDSjDOMPvTrOgt87Bcondy4XyKBX594AMMfd7flsX0gZ5vOoO+iOTN7l3MW+NgvkADNKkep2Vtx3OOU+8QY9aGtdaPv/8x3mBYpxcO+kLPAObJAltBmCpZDKZwFzn1SaAG042dyFjjnPYUnN+2zANpxpDkLnOvhowC04yl0FjhfPIWnJkpox5HmLHAuewqtEtqhFI/FApcFxUCbs7qwQwrvjRSe2rxfes4hhY/LAvXakNaa1VoyxmjLuf0xhxTeAyk8tcUKaIczPeIFEqBVADp9e0A7pPCOpJAB7ZDCx6W8KkvoTFFCq21Z3QC0QwqnSQonSmiHMz3iBRZVDvW2EtohhbcnhVZAO5Ti0S5QEzp0W44wrfLQgjm9HNAOKbwbKdzikMLHaoFhty+m+aK0xaDV1Tuk0CGF84MzLbOqmA4pnB4pPL3VIYXOAucRKWRAO6TQWeB8IYVWQDuUwlng3CeFW1+WnmtwKIWzwPkSPrrVIYXOAudT+KjjKXQWOK88hVA5HFLoLHDeeAq3veyQwhkukB6X4tW6sI4c+lBpWhspSQ68SvsR4/erQ8CG8b/c4kkv7d95gSGHFE6DFG5zSOE0LmQ81ILuplSpQRPQyfueqPFhDNSGuEwEHQ+7PRG3N2K80yPeXOxWFiwi3nizURAjYoI7zGUrEqXFTtZBHVI40VO47YBDCqeYGniNlF71hI0F1pjPSvGGIkCBXd4KHIvjnqjxqqiEQlOP+C/GK6Z8GxjnCZtvCBvyng6iDoupbhaxXmfKeIcUTiKFBybFctibMxnKg4lXA6w8pQs1pxh/dCTGCI4Cgt6I7hHYjfJxlqwYMbcvqnt5Kt7gw3/peENcx34M8hv/GBZbXUh08e900GsCWkh9A+vm2osy3iGFFlJ40CGFlhOWi0DxlK+LljDdnDFwJjDniwCynpjmi2l+Y0tI1bxR2mH40sGEALfui2v+uOaLaPX8Th/OYMYhxXXcCXH8F1AeY0DjfkDlF0/YuE/qTKFufLCIedc5pLBICi2AtjWlMIhdUamN4NWS5sAymJANQMd0AjSwG+UtYRS1s3R/3O1PqvUEyqRWnwS+SbjSEX+CQKnVx9X6pJu3+Be8FFMDKTqu0zv9CTUQ42JWUd28Aei2oeN6Q4qn4mOwCC+CGPzydoTSrqTw9LaDDinkHQt1Ewq0odqGjWc95DEBOg2Z6k9AymILRGoBYBdT2hJ2sU1pjSktkNAaE9hvSPORNL+Uxg69rVEcp8H/WJ/g/y2WaYtyjbkkdABf2NBMvKZeLnZqI8aTxPOAOOI8IIWntx1ySGHpqW1wNYYLMMQqr5/kcZxgpHuialuOZCfhTyVANzBAAdykSkBvYqTStjmlYitGVmtKGa+2iFfTpVdbkniVztCYxP/W405QSXjX8/lJPItahl7oKrrHVMQ9YYN31lnMfw+EI86H8NEyQNuTUpgEq/goN+wVgu3RNkEqL8lLlXSDYDthSyXR28BSFtDMWACaNlKnWtNIPGlBuqvalkFWFU3bOA8FO/RSjnMH0ypvMZr5VM0Q5DhhE86vEtDphFBO4oA1KSG+qKlGFyvJRgw0exxSOIWEthulKBrgTAuxYGNeY18l2lcfJX2AleOEFi4AZM0ZgT9A0EjPzgOvwTzhVQ12aG0dWqhdo2mwQw3Tfp6mKv0vbUPiJQzsC3y3ZQnfnO+dIgGPMzexmG9K4mag+yeQUIlTIoclAeJIGrYwCHqK1LDcyGjfQjMWO7QtKUURDUQKo6ZpmWWzF1qs7osRqzPUXNKDwx1qgwloIXcZmlqoQw0V8CqhNoKtFunEDm2jNApqtEtMNWzpVQI3DYZ7MO/Gv3eodB5CNu4NEuEEaBbedDOQHtLAhNIPlknskz6bxiZCXXzaIpGtczyF2w9Jz9qeFBquECZbHvb8kQj0R5FxDc6XAGMT6nKsAGWANGmSr0HehjqVSIcC1HYAuLFOhUa8S010qnEaXUqiU0nyNEHbLiXOr8YLOBLtVKJdSozvAYJ4uEMJFRSS4m15N1JlWXIH8yStod4QphvAQd2wirBN0BsWhm185pKL0eaeQgDaxqSwzjxP0RxGLBAkjEFDQpFkM0gbqRZge1qsC5pxqF0JMQQjQK2B0WSPmuhWUz1KqltJ9ajpATXdo2b6FBrpXjXTL6f71BRN++VML70N7092KwkawD3+N17AzRAGsiHvgwRoUlTygkrSZ9ACdHel3MJQ6E+4hcvGx1bqWg4XEQ8ZW5PCHYctgJ6blOI+T+UJl5zPnqjO5jm2ysVZLkI6qs0Zg7eletW2doVEKQtjyGCAsktN9ynZfgKrkh1QsoNKblDODcj5QYVGdpB2ZByh6RAdkWmb68f76Z2AO/0jYb1fTZI4hwhnFQViWyHlJNYNitlimkdY9xDWazxDvHGTI4YsOrTNfkErKTxDgF4SMCv6By2V/u99ajQ0aHsAp5rmlJZBv2tNcKanspyn1nAK6t6YShoqbHOsbDSaNjhhtUj0ai05KLtE7GKdhDYl0aOmWOKmBgiaJInV7JCaHVBzhNphNT+g5kfU/LDaTlAeUnODam6E3wDQ422pPpX+C2juhZAm+MZ71EiXFiHVvAP8MtpJWjU9H4xHRAC2anhkhJfRiAYxLY/Cw2KjX3DiVDqz65hEz6nGNA2dt8Vxz9OGlDtUwPb+TzXNaVNGJ3Wz4T5O1ZSlk2DbnNVb8m6CbGvW3QbrhEbELtpFeFJJzYj3upN9WrJP7l/55IbNT67HWLB+y4INmxds3Prkxm1PbtoqxoJNtL/9yc07ntxM2+0LjJ0dPLZZptswpXdi8L9v3GqcasPWJzfwmddvfmLTVrl3hZbspwGs0yBaCbMJGKS7NS8+MwatgpZAC7HbL1g+FRK63gS7GG0znNKd3RC33N/3cappTnF/R/mKMz5VCFPxzPIYtmcEWtRDEAovCT3uFVBAUmfb5Z5haftWaesWaavY0ths7vDYslnassUy7ja1/u/WSdOdO6oLI3ggwCDYjkdEY9LgiD6uQeMTXkNTYYAeHLbZL1g2lU7vOmqxcswtUhg2KMWMT1XypEB1NiKKSENtEM4/1ptJfw11QGmOdamxgtw3Jm1YL61dO0tj08bqrlGYREhfp49ByG7NGyZw0g1Mb4thljZyDsI2+gWnIIW7jkwy29nGz2QJENVJMHvhC1TZQidcdCQRhWAmcgZbRKTTtXaH9OH3pG9/i8e3LduHMf2W9Effc42spRuJ9A2iobBJt+bYhS5iRTgQygezdCkeUEShTLRt2cdTWAZou/mZTL+xJ8aAjmgcGMSuEzYstMJvAiMaQYpoX7yret026cMPpW9/wOPb5nho048+co2sUxO9MKdEO5Vwu0aKB7zlHAGCJ0kccSY+9h2Wodm2hWYsgLadp9AIrwOgSd+A1lGPqDe1UQRmoJotPH+xDjXRraV6lWQfA/p7Fvw95PHRR/LIeiXZo6b71EQBTkdihK2IA1GbUiKeCQHZvqjmiXEuI8cG2tlTeIZ06GcCdvUUWmKLvSIcNG7EebZk3K1Z9p50wPmX7IFDJNXn2rBrdgH9oWt0g5zuUwjQ9BlITos4kJYc2GpDGo5DBJmI2EDTPWRkstjSU8iAbrCpp7AoodFFHQH77N9mMwKxrlaEGcHBkehW0r1KdkjODLg27Z5tQK/YRNeVs/1wOsY74dAhFQjUMIO4vIYUXIb+mJEMVswis62n8MxuK6BtRQqDxUw+QoPui7gD8MCBbDWn1ZaMGspDPNNTPkWA7pfZ5+faskf63iwDerOSG4CXEYBmPyJpQSSkEWuaptsPiTCkRnvDlnxyG5PCs7uPlUtoO5HCulAxYVsTUXUE6KY0DHatWTgvCM3xbsRmZAeVjkGlfbhqy95ZBfSHH1at3qLkhuUce9QTPYhwQoBeQRXpAghX4oBSf7Fewn2kGM4DUmgFtP1IIWcxeTm4B/EbCfZ1s5MZYXTtCD9CnEYvyUi1MCK3j1Zt3TurKseHH1av2qp0jhCg4VRP9qnxLjgLg3lV9BZqRMYXEsgR1BHTy6Oi7UgKGdB2JYX4+YNG+QEvx6+RviGikNtyYIQxVqBTfQgqKozKnWPV2/fNNqDXbselO0YV0jro1iKVI9YB73dbFvldAHRCr09wHGnUqHYwWRDahxSe23PcAmi7kcJigRguQoA0QXZYcAaKQuI5DvOzjHC5IblrOQC944D04XdnEdDfq1q7Q+4ao0sD0JkBmPBIEQoWOFiKjXcNSQDaH4UpvbyqmB1J4bk9J2xNCg1AR3U/vCps4hD+lCwDGuJZhgI9JHePEaZnHdAfVq3fIXevAKDBSgnQ3XhucJ6LCgmdgtXcl9AEr62zVCSzJyk8t+dkuYS2FSlkQHPeB9fK4LQUw8TB2VOkQKd6YWHoGJG7xuXu8aqdB6Xvza6EXrdL7l1B9xLUHhFuGkPAHeIBhQ+cCycgSqmYpmBnT6EV0HYkhXVshPaQeI4yoNlmR3wrVCgyQgC6MCz3jBOwKnceml1Af1i9Ybfcs5xuJ7qpYOhIsRs8wpa7lgxHKaVIWeLUlbheZ3tPIQO63q6ewhAUDxE16mNPoSiggfxtThNMdMrpfjzrC2OunhVyz4rqPbMM6O9Vr98LCd1DgB5CTkC6h+M6CsgwIEDDcpdW/RFUpWHvN8f729hTeHaiymErUigyPuD3dtdHVVH3qIUrCoTaDR0604tkqs5RoKpvTfVsS+jvVa3fI/etJG0Hag99ElI5EpxAHurA5xQ5LPCtxCzFOmxMCs/sPTnJbGcfUgiVQ+fKcfCqBDiJsCmjtuWUcEGJdhB05Mygkh9Qupa7GNBVew7PNqA3EaDX0O2kFEaRj5gRgC4ohGlY7tIiKUvzccU9T5nKYUtPIQF6ScDunkIULYgZ1eWIEbbllXC7Gu3UUj1yhlWOzjEG9Iqq3YceBaBXA9AdADQ9MZAiHkXNGsSRQkfKIEgQJR5jpboz9vUU7j1VBLQdC80YNewiqHUrCnwB0Oz3jhe0BAF6QMmRhB6Te1cSoCv3Hp5tUrhpLy5NOnTnKJ4VmX4NpLCTVQ4Rc8fFTkXl6WIB9pn1r5gPnkIA2sak0Cz2bJSSaWKvSjCvhDqUWCciNrMcllRglaN/TdXu2TbbVW/cK/evknsZ0DlSOfqZFHLhDmSvZETlUpBCb9TM/Y7Y2FO471S5ymE3UigSY2PIvwpwJVwEcgAuSpRrx2QGDDdhD6kcq6v2PiIdmiR0gUjhEIrUJHpY5SAJneWAu5QmAu6KRdHtXGimHNA2I4W1xVqjEc6NTYrQfrUtAwlNoEl2Q+XIDyld465+AvSqR0AKN+6RB1iHLoxAQqd71WSX4SlszbLKwUGC3hh6X1iLkdrUUzhRQtuKFApGiMgkzReGHRqBHGk1mIOnMNalJbpRDAmewlFWOVY/ClK4j1Qdej4owvud7mNSKMx2WZQnRRsAGmaNXVG40b6ewn22J4Wozh/RUC6fC3FAh85xYmwXEmMzvXL7kNI5TOJZ7l39KEjhPly6mwC9nACNIFKugmfo0M0isxCuew4fjVp1aDuSwvP7T9ucFOp1YQ3B0HEOTuLoiNYMqoASKSToZPrk/DDs0H0r5f6Vj8BTuJEADYshXN+Q0AxoJGJ1QkI3Zzn3O2EmrYTtHj7KgLYtKTSstjpaUcWNCtAEkbasGjZIIULt2oflDuEpHK/aPfs69G7YoXtWwrGS47KOpHIgxp9zv1s4gJu+BC8X2K0rtjC0KyksB7QdPYVG+8D6lGmHRiyHwsVlYOVIE6aHlc5RF1SOVdWzD+gNuyGhe1YpHcNsh+6BlSPSJRpccKH/rDuQQQqWx3R625kUXnj5jPR0vV0LzZiN27hroNHDqjlDpBApWKRDx0mH7oHKgWg7qByVe2bZDv1h1cYdpEO7etYA0LkhlCqNcwnJcIcZnJQ0VQ6ricOupPDigbNFQNsyfNTsolkfRVmtJkBEJbIV5nho2KH7UM65c0zuWe0iUjjrgK7cuEvpJUCPKyILK9OjkQ4dKZieQnRgcaMDS4zbaETsnlPIgPbbtyWFcBSLvq6BpJFQSI/ySKcS6cLDPd2n5Efkwhh0aAL0rlkH9Iadcu9KV+9yuWMMCn2ml24zFI0OdTEpTIu0QrcvhLTIOkvvZHuSQquEtqWnEF4V3Zfgxq9ZzljJMaBFPHSPku6mB72ra9TVtYLU6Kpds23lqNywhwBd3b1SaR+RswPI2I13ozMGegvl0VgoIBqvJABoUbXRzuGjDGh7ksJiy000B1K5ORAqIHJPIDWc44yVbjXV78oPuQqjru7lrp6Vlbtm2w5dsWGnq28FbqeOUfS1QJE7Ujm64Z9H+GiGSKGG7lhcK9oTmaq5m51I4SQJbbOcQhHdzz2vREIhIIIK/tzAKtlDACJSyGa75UjB2rV/lgvNVK/fIZMO3T0mI8B/EAH+8W6YFIWnsDXrDgiVI8LFz2Omzc4hhfYkhRzNo3mien2KkOEWTVVaRRGwLnS4yvTLuX4AuocAvbxytqPtPnSt3yX3rHD1oIgC6dBorhXj3ofhgtZGFBaWOx2xHLBy6KVgaLuSwksHz0mL/eXqSHCG0zJKcX+nmuYUGliMrzjTU4lYDi4FhgjMRnasEKBDOS1SkBOdCiT0kFwYQbRd9/KqWSeF1et3Ij+3e1TuGEJBsFSPRpp9tMAtOnPoDoN46Dia3fvMjlgT9ct5/QuWT6XLh86jLofRNTpq2bn3KX2PTWnDsH+fp5rm1AvThLuYvH2vp/Ky3gkFNK43pNwNGZ0EXmsObQjDxAh7UZSjfQBIIunYt9LVu/oRlDHYuMeFjJWVeEq0D6IMaYJbb0UI051aSw5abyN6vblF8XOhddjkF5w0lS4fviC92OrmyvX3O0jCteXdHOUzS4O+i+bM7d8Q53GHV5NCzXA3ZjBQPzevkW7KLhWZBCEaDQ7L8Kog1E4eXFe59+isR9vtdQ2ux9VJ8egchR8+168lernXcqdOikeww92cNzpBNaSNRc2TX/Ceh3SFJPRTfkuzt+K4xykJfKPLXdCgFzM+1fSnRUox+dVaRB3pxQ7YgvtPOFXxuDdGmpwaiBq1dNvQI1CLdaDOOUqB9cuFMaV7hdI7VrnrgPTRh9IH35G+8/DHBx9If/RR9frdSADrXg4JnR+iz6OIAH+4M/NoHQQ7NBdrRNl2FlTz4xec0ZRVjsXzkRT6YojgCRU00Rc+0klH9HKzpW72QuaU6RgiIhA+moOnUMhpQnMarm+ZRGPPcqV3vHL3IelrX5F+73d5fNmyfQjTL39Z+v2vVm/YrfQup9tJZrMd2nsmurVI3rByNOdIyOn+lHB9G92D7OwpZEDPP09hEM4/+smD7cYIdSACaepijajOodeLaufcmBCub24UG+9WM70KWzkIUmoPV0766KPZEM9i/NEfMSlcrXTBU0ifRCNSmOzkPrPc3b45DR2aJDSyvtlo4wnbOnzUKqHnlafQFxftVu8CaNHkzwuVWmvk6qNwrOTRQC3aoSW7lQzxsAG5MCj3jLl6VlWRyjHrte0Urm0n5wddmUEXgpM6NbPFG3sKWRP1J9jryffnMhvnFJYDet54ClnlmCiho5bFm2/zsjPCE+Ne8AngoyWL9oRhAFoh7pVB1rcw27l6Vs6+2a5q3Q5X93KlZwzcNDdE9xg9NziWg3vL0gcGEYzxnRk2Hjh29hRemSih54unkCV0EdBIWPJFp8op5GpgooyBaIFFaOb2hFqkAw3lU2hDDx26e1yGynFg1mvb7UIsR9e40j6i5vrUdJ9GpDDcqQqVoynpFhkrBAtf0QhtY0/h5cPzlBQSRlvz7pa8DolLv32eCNMkUiiaCoeEQVqHCYnjoVFopt2N9oTcIDA/IHcyLSNAz76ncAOX0+0eUwpjamZATfdCs49yfzfRJrkhhYLnXPxJ98SsEsuWpPDIBekp3zwMH10W0muCsEDVtOk1bdhZNpWE8HBpDlKvA5yC1ZhA+GhLHhaxWDcDuldpR9a30rtc7pn9FKwPKwHolXLXGMdDDyjZfqSjR7uh6AezrHLgk7vrBRmIWkpE25MUAtB+W+cUekKwdvljKnca5pzCvDvczjmFfWqWBmrbcXDS6upds+4pRPgokcJRADo7pGRwm5E6pIVyCB9tRLFG9hFyTqEnbK9fcApSCEDX2zR8VIQOI7c0img7f5wD/HMI+gl1KBHO+iYdun0YRV56Vrr6VlbPPilcv0PpWwUJ3ckFz5OiWGMniAGb7dRASg8IO3ScvdYxW+cUXpkooW1WaAb0kVO+fRxq15TSmvJqW04L56GnIgWrW84hSVbuHScJXTXrZQxIQiukcnSvxE2VHyIVCE2/I10qzHacU9gEs50mmskKT6GdSeErRy4WAW3HQjMedkYQI0TGSkJrzGjNKeFVRpJsspvNdiMolNi/CrXtZl2HhqeQJHTPKqUwrOSHNa7LocHKkefKSdySws+A9hWd/DYmhQxon01zChFfxhEdfhZyDaL6KHfBCuUVuL67lVQ/V04ac3GA/yMoNLNhnzywSu5erpDmkycdmgBdYB2aXd8tKNboFk2DDPEcsrWn0Cqh7UoKuaJFfVxt4EqerWktmFWNYo09aq4X8dCkcpCY7FvxCFSOjbu4LgcKnsu5Ia5tB7MdAzqH6qOBhEqk0B8zgqHLKvjbjxS+cvSSBdD2KzTjMWI5NF9EE1nfsHLkzMpJXUpmwJTQq2UihXuOzHr46H5OOF8pqo8qWW5Jgc5uebWVE8YarZWTQnbPKbxWBmhb9in0iJYU3HiTXd9qW5arj3ZriS4l288V/EWPlUdRfXTDXjQN6l3FKscgSGFS1OUQLSlIh86o/rhZaKZY2862pNACaPvlFEaLAT1GscaGBLu+M2BdsQ4Nte0GEObfNYoy+o+gadCH1Zv2oJyuaBpEz4p0P91msMBERDld+L3V+rjogqV7onYnhdeOXZYW2ZYUoouw7kX1UTe6YCU42i6jteaVsNChu9A0iGDUtQJ9CvtXzb6Ert7M5XRJQhdQH1rOmk2DRP96kMK0Vp9UEdIdN5sG2bhPIQPatqQwxDp0BNH9/rhqdsGCB44ALQL8SULnBrlP4cpHRAr3ANBECgtjiPsDKUTKN6scWfQpREuKhC66YNWZRfztSwrLAG3DQjNG1rdbdMFqFHU5stw0CFYObkkxgOqjAPSqR9nWjbtgcdMgqBxKqABPYUtKFV2w/HG92NPNzp7CVyZKaLsVmokYXbD8CdMOnVGD7QpIIUqBqeiCNaR0rnD1jsv9a6v2PhIJvZqbBo0qorZdvNvoJMuVk9ixwnXAPDGzclLEvqTw2vErRR3altVHRcBdRPMlzILnabi+CdDRTk1I6PZhkEK2clTumfXm9QTowTVo61Zgx0q6V0uaPVZEJ9mGJClLXJQjopeb7WzpKQSgvfb1FHpCZifZCFrvGH0Ks8WWFCCFuSH0KexjlWO2a9t9D403QQqhQytGbTv0+ubm9WZr5HpUT9XhIYrZ3VNoldC2I4VGJ1nuU4h6jaILVgqNN2HlELEcvUblJEjotY9AhxaksBtmOzSvt3bBQvVRbkWHkhox3RPVSjq0XUnh9ROvTDLb2YkUcvkOUfDcHUgivLgpo7aShG5HOV16uDOglY4RdqysrN516FG0pODWyGgaNKBysUY0ryelSNSHFilY/rjmNctd2+gXnEQKb5QB2m4tKUICAZo/qpEOGjB4oWi8yeV0e8w+hSOwQ/eunP3WyFUbUKyRAT2EpkHpHi7WWFCDHQg7aUafQjhWvAkjSbas8ab9SKEV0PYjhWGzaVAUNc9JEw1AjYbkAyksQFtNiUZYIzKSZMcrZz98dP0u5Od2L4eEzvZr6V7DDg2vSob7FGbcvqQIhoYaXWvvPoU3TlyTdK99PYUe7lQJHZrt0CgRnUSDKVKjIygRraa65Rx3duteQZie/RSs6nW75L7lctcKjh0dUEitj3dqaI2cg82uES0p8GzxRw2/t+jsZltPIQPazqQwZNihfVEu4p/kZrJZLdiuRNgUnebadgTozuUA9I4D0oezXGhmJ6rMFJZD80mTytGNTwW/dx4m8yZRwyApYjkM17edw0fLAW3DnEIW0oRpXwR5HwGunNSS4oq6RndkmQPuqjtH6blfvf3l2c4pXLtN6RyXC9wCiz5Jkqudo+RIB8JH6dMG4mgQ44vpnnA5pm1JCm9OlNA2yyn0GP3rNcK0iOVozCBqPthOWgdK1pIaTXIx1692oBdW1fa9s9ySomrNNhjsOhAMjYSDpBGZBO9PCzf6hpUj5i6aOEpF/G1JCq2Atl9OYcTAtDemkg7tZx2aSGEL9w0KtbOhowud3QhMBOiO4aqte2dX5fiwevU2pX0MpsNsP+k/Wozj7IKcydsi0JzURMo3CgdHrYC2Iym8aWtSWMxbwSPbDSsH+ymaRb1GttzFO5Vkr4KoaHieXVtnV0J/9KFr5Va6Lohpph+fJMoJhUFuI0uksCmFMpNIXGfFSQC6NuiQQrvmFBp6Z0RH780E9FGERGe15qwSRBApGBgSsXrl3BAp065Nu4mozSqgxzejQHUWRcAUdFdhH2EIjBD9jRpSYIT+aKmnm837FL568vqcJ4W190MKo4bW4UMfBk0IaXRHTrPxDiFK3N+tG5Fu6QHXhl2zDejlG+VsH5K9Uz1KrIC0AwSOwkeoEiOsh6aEDFlE25lonnOk8MGdmQGt1ZkV/YOWSv/3PjUaGrQ9gFNNc0rLoN+yJjjzU9WFjDRpIlW+iE5qdH0CtrAmlD1XuWwpRyl1QUCm+qrX75hlQMsj65RUr4JyHGZcP2nPpOLTJ2xIcRQHqxyeUCmKw1a/YPlUevXUTemloGg5o4vGM+a45yk9/kIFbO//VNOcNmX0tjw65czsVE1Z3sm4m3Pou9NKWEGNdDSYinSh/XC8B30Bs4NqflAroFxi9ZZZVjk+kldtkTvG1I5RFWWTBtTUgJbo1bhjkBbp1EMd9LH1ljzp0/g2GrNo9Ia+WPb4BSdNhYSuNcEuRtsMp3RnN8Qt9/d9nGqaU9zfUb7izE5VvMsjuieiISo6ghJKCLsTwf5c0iCUpwe9EusgGLnWbuWmQd9GRx+Mb1u2D2H6Rx+5htZyC9CCGkVAkhvFZfgBgkSVBLeli2mG9sxCumyl8/4XnDiVbp26YbFyzC1SGDYoRe19kEKRWVgX1euAZs2fVANcQpzUaNg6MsjbI60jUlDinWq0A4D+4Nsf+/rXP/YHX//Y178hGePrPL5R2n7jGxji+DfE+IY5eP/r3zC2xr+U/pdOizP/AY2vS9/5oHpkHYgpjQgXORchHE1cE0ckX/m4Z70nZmGEtvkFJ5PCckDbLXzUEp6GFlIw3mmBGGDdKOx3GdS+IFiHO5RwO0x4uQHlmZBaE1c9CcWbUAI5tTEvt7TLrV1yuEuJ9iiJfjnVKyd6lSSRyGElOyJnh+XsiJIZwsgNy+khOU0vDSmJASXeJ8f75WiXHOpSggWlqV1pzKv+lFKXVOri2tK4+mKbku6FwxIfgJ0poINZ1LFGXxVihKiZpIsiYHUhd3lrZDt6CidJaJuFjxqGjrDhAPfC/qX6uZ6BaCDUgvBohNILPwsauRZQ7QVhpQOozdU+jOrRXWNK17iIyHP1rXT1rUBU/sAqV/9qF20H1qBzJnZWu/ppuwpVPminD+2W0UKF/rdzjCOeB+HizvUrsNP1qaluXCvSAbdlsB2lfum50YRMbzUAmwy3holxH4KoGZYUstcvOMFT+Nrpm5Lba9/qo4adK8rFDOAy5HQmLjojqus2Z1QW0oAUYTrVr8Y61GSnlupGlbBsP8x5xBpR5X8YFTyIvRXG1K5xYBQQX6F2j9NANV7E6y3HEby6XOkcR1ZVx7BaGFHaB+ALzA/AgyNiRJM9CBONc9YMXIPt8F82c/AGaRqBpMpNcjWfxVrnCZdajNrWU/j62VdhtrOnp7CkRkeMFtm+KLQObgDO1BDBmYAR6R5o0pOHvORkFqjUBLVkN9vU+giF6P6WH+RY00FUhGkfQgAGQbyAhBeFO3VDnNMQXbvRd3kICYv5AVT/yPapmT4l1YO2AUQB4zAX4kKJbncrV86lz8DaM3erR4sjrsURNbpqlAJHbfULTvIUvnH2lqR57Bo+GioVshAGaW8UvYV8MY7uiKEyGJzhJKezLCAzEJzBDnDECBO1OBJp4XYhcKf7oSQQNDP9cq5fJvmdI6APIBEwNwhHY45r1jDogX68E3U2ZLpJgGP0mqD7BPVAkJPSqYWEAbETenwLhz4HmLOivDlrz0JN8kQtPdxt35KCJbSNSaGxEzGi1bzcocITZU94XAhC0lbBEZvQZBYNe5AWzl5xUqlpRDuUOI0uqAeJbjXerSS64KMmcBNGIb+7IcJTfawT0/EeEupqmlt20/sTeD9oH98eSgRx2IjfRw5sDlXNQ+1cpp9TaQL0kZJYMhFB+oTeqNE61lsM7Y+Wt3WzHylkQHvsSwpri+5iM/xSFCOFTTrOBe8417CRy6HTNtqJ6lswBrcbWnWI7Q9hdpKThhDl3C0acWAU4jbOMagJIB6KSly8RFu4skXZRR50e3Qgl5FAHETNDZBRJIPlmaFCaVa5HyHiqHzcL9RXFM9mGLQnbHdSaAW0HUmhsEMb01AppNjL7ewJOv4oF9pi3YMGAbeBa3e0phnWXKq0rR0e6RBHnApwYxQMjEYY34Y4522ItvxOEdCH/y2obXmjywSMKjlYV5rTXGgvx5U3AGgdgplUZ8K06LDISYSI5zYyvfXyKA47ksI3zr0qqbYlhcV+sqFSlJInapTCR8RSlAPZ4jBOs2iEAkA7TWljQFqzgtuSxQ6xNwRa8FZ0sIWjsYCuhzjIQh1ynYsecUgG2l/AfZPjk2SMaqJCxxBxfwA0IkxQva5e+FCiRnJKMaquzvKcsTkpfP3sTYsOHbIjpTDKZ1lC1US2Kfex1EQzcJAwriFNuOSiYWoj0q3BFxvZtNfMWGxhLLakoS2IeGXIci5625o2Ch2J+l1i2splQJqFPY6PN6bZp5PijF1k0ODM9QmdBtvIzdzBiPkwiZZM6VMkqtiPFL5x7qak2pkUhsolXLTkO4QUjOGZ7otoqObIXXkInSSw2aiHsp881EAKrruGlBCuqhDegkcSk+POa1pjVqQOqJaXaAc3RkPC8GNzfDOSXhtSKOMUiOu0T3eLLwI0eyPGMLK7I6USHKVMb3v+ghZSyCqHnUmhpTcFd5VldhU2uiaXlBATSW1pUTcMCoA/LurYskLCGjaBG16POAMdKCesq4TXegOscO81sv7QkFb99GaWx/WcnAstmd4fpdO6xWlhZkm5G5K6VSSLuguGbBbe++gDUDbmDSl843wJ0DYlhdYOFXXhojVXN9INhT4dNVDVmiHEozIiTA0JjS3W8D+zhq37k7oonE4Erh5VbmnoAUCflBY3q8Iq7oSE7kNOLk7iT7rro7rILajHeZiMkqoTQ7wRvbMhZdibWcvX68w8SN7R6x7oFzsfSCEBWrE3KZxQu9GQf8WnecQ4TvBaFnY3pfEbeA2g6x5R04M1E29EE71cvTFWUbjVmmjBTbKWS38A1v6IUIW5FQbMyTgzQqPCnHXCcXPeOE8joKSBpJEqhg8ZKT1A6oofODzzcqPz0FN4/pakeGxNCid/xdaqHaUYpggu1JwGrA0dgFtAkJ5dF9ZEYmLRSGLI9bCRu8r9qXAbeEzLcVHqi8hPGL/NXgJClxCGFxLkAHTIqGFeV26Zmbpyrr1J4VsXX5Nkj71JYfi2hcJKjnHeoQuRhC4z9kUM8WloAjFLcIjZkMoItzAjPD2RUj0QgVGRp23lo8Uz0z1AvLDOkv1a8m8/hC92HpDCNy+8Vi6hbU0ppngmWr8vks3GAsMWtcS6HynZAYugLJkFre8Pl6AvGF7pf0NlzksCdCmOKvxwv9h5QAqtgHYoxbQXGNat5pGpA1PNnKg6UeY0bExv688Ll2qiFuGLBcYexQLnLClkQDukcJYXGJrvC3x0pPDtMh3aIYX3tMDwvSD4Dm8LP64LnIOk8G2HFE7nQuKgIIXLQhZlg4cnWkpPrLVSN5P/eSxkrlhAQ6jLoINhs5eA1eFnOncCccuPF3y4X+w8IIUM6DqHUkwVtDTpq6dtU9K8XMgM2hRxQhb/eRG+JQ9fqBSAbzVWFK3IpVOZ6SfF2tWwcoRK1LOUZeOQwqlI4dsXXy8C2qEUpdu9ziR2Zt4hbMa0tOYkbB2GHzFahKzuEX6WqMbJqrrHNNVxu2JdlBrzswfRE9aF/8WDyCd6p14XExVQWXhH4UQUqQZ8S+iBVEmEe8Jm04nQbcmozUnhu5ffkFy1DqWwOFCKTjjLc99jiuHmjBFQ4SlGC/G+z3QZ+hisIttPxDOhzWvC3RDXG2jLaa0ckoqtR8Q8xQm+ukg8gS89akTue9iz2JDkMJJweTBgyQxi1FB1fkGhcrx35S2putahFJYyHaaT2dAlYoZvz8vb5iwEqs9EHocooSIe4u+SKGKE6At0POG6GRzaQToDjaYUao6RhuqPi+Bmju5A4B6euejLhhhRLKeeHeDoOxgzAF0WL2rRVUqYdkihqXJ8+urbUlWdQyksvgzeevm44d+OukVkBakHLVnNw/l89Qk0JBa5JP6E0bknkEGTNVHTQwTcBTj+riGF2opcUoNfRbQdvyetNqTpH9VAGoF4AdTi10VsEwKY0MdIpzvBx6VkPPwxPEbRBcP1bQ07cUghSWgGdK1DKQxMF9MKPaZ4FiEWPiMhXG3NcXWlFILjgFQgkvOxUyKHBdH6TUlOneIELdFfvi0naiGoorBGs5n0KiAuiiU0ZQT0abi5fbdWn9IbUnpzGpXYRTUZryWWlT5byRgSdn5Bq4SudSgFs8BIOf0ydVnou6xX+BLIqkJ5g5QRzt+YMsL5WzPoB9eaEZmtKpfmQMEN3lELw5xwxXmvbRmjPh2mOSOJELktSS7jK7JgRPw04xs5MlFurRl3G9F8ZuSTIKOWjl4OKZQ+/co7UmWNvSlFsGQIMx7rRaqHyE9WkdMIwydJGekw801EJlUW5bna8m6kanMqITJeO7T2EbV9SG0fUdpH1fZhtWNUixW4OEFBDRfcIW49j8qL/OZgu9bazmlanJgo6n1xOhbnLOYQ7N8AJRvh16RViwg+T5x1j2iZKdAhhZ+59q5UsczGlMKqaURN43HUsKP5OTklEDOSCEljJkCTHDVkbR6gFMmwobxipHmjVKmrMIoyX+heNerqXC53LlcS3aL4hhblCurcKkWls4lB+A63q6KdJvCdNcGdQoFG1siNNBY/ttw6NsKNgsxs2WKYns1J4a8A0LU2JoURQwcVyPAaJmEiZG6RVILcPk6j4jbgWhTdetRglvu+dTAoC0pUFJrpBGSx0y33rpB7VmL0rkSn7r5VKDST4LozyR6AW0xjXfSPSkwUmunCqcIdaPjJVWZQIZLumXDeqEUmNJD6hJk3EDeMhl6L1WVCeKANSeGvTJTQdqs+Wh7TLADti7EZDtUQkfbH1T6RpN2S0hI9KukJ4U5AmbQI1Ivp5tGJcl6ok9SrpvqV/jVcX5SLjvavVQbWqaRSZwbVzCDKimKnH+0PUZG/j/5LTfZyCSU+D2p3dELYhyH7oag0iexatpyg1kyM+6okDH2a8I3WQXDcTIqWth8ptALa1oVmBMGqEzXBEpzex1Y5w+ImSmdktVQ/dIMoobkbNZDSDEdUtevXsty8gkauXxneJA+vc41scA1vkIfWy6MbuEAjGsMpXMFRIw0716/mhrh46YBGEM/SqQYY2Z24Q2LQXrRopxbvBo9s4SKoJD4bUpycm2KeGoNb0S9aIwPQDimUPnv9XWlhTbk6EpzhtIxS3N+ppjmFBhbjK97HqepClrLnUR3yL8p1khIwGMMGx8ysjatyEYLD7QpXVVTTg5CyGbTlRCtBQHbEKDRaGJOF6ozS0cuVLtrnHTrSNYaDottxx6hClDE/LOcJ30Nc6LFPyQygJKSpkLgTXVooB6bIhZoEplVwRLbledgRI3w3SAUIm45DO/2ClikDutpjhs5ELTv3Pq0NGxlK93+qaU69Ioc0MsNTeYycVlZD4/BX00AxuzQMzE1sXGtl8wU9+rl0HWkOarxHTvbL6QFU5AcWRwiajFfozQrpzX0roW8M0Ha1Mkgqx0p5YK08tJq2Ckqdr5UHVkPJJsW6dzU07O7l+F8UPB/DXYE2myj3r6YH5GQfGtbHuEUQ6TmkVYsSNoRsEtXwTXKTQn/c+OSemJnrZY9fcNJU+pUb70pPN+Dxev+Dvta2PLYP5GzTGfRdNGfu7yRJlm3wZbib0EhKa8q6W9hCTLQsXCAYqZEuhTSBZB8JTnl0vXTqhHT8mDmOGttj9zOOYXvcOsyTnzlVPbyGW3z3Qg+JdKG6Lko5thOmuXlXBnm7TSk3l9nFKvx2+wXLhvQ5AvSTyyzN3orjHqck8I0ud0GDXsz4VNOfFinFjD9zLSeoekT/epTQ1VHyS9SVy4i6idx+s8D19HtI7kq7dknbd8zS2LvX1TtOUNZg6UPLOSg/cOJk4ConIS2qI4hIj2VBI3nRPr/gpKn0uZvvSgvsSgpN1VkDI2StAyVrOfqiMYnHOupAs82BHvrJHi3W7Vq3Vfqd35b+03+apfHl35HH1qNdYqLLoInotJKHfbqJ2woGkihMY7aR1euiVtZvR1LIgLarp7AUkh9lUYcCX6jI2Jg29FS4/dpJOioJdFTREj2uzXuk//MP0l//9SyN//MPruUb0aci3acQO4x2Gi5JWKaFWZpJoc8MoirWB5tZ3Zl54Cm0SmibNq8X9TRQsj9i9ApqYM+zaI3MygZBSskOyqle19b90i9+Lv3sZ7M0fvGL6pVbYBzM9NMjAg0rSEiTAt2Whe8dNf1Tmi8JRmgAuqxpkB09hZ+/+Z4F0DbzFAqvisdsgYXYZVY2mlMghaF21CqHstGrZHph0MgOurYdmFVA/93fuVZtkbNs1Mv0E6BBCtHum7WOZqMjFkd3xLmKzQRA289T+KtlgLaZp9BQORCWqYmwDVHTllQOxGnkoT0nupRUP9oTto8o2eHqnS/PLqB/Ia/ZBiN3fgifAbaOTo3UevpsdMs1oY0QYjyE1uGNWbpu2tVTaAW0/UihSG0KcX1ELiJKEro5g4ZXwXZ3qAONfBJob6XkBlXuKVi9c3Yl9C9+Lq/dBU9NxzCENOke8S6Nu1ioIugUIVPwHYq4bd0IuAvblxSyymHj8FERqsbZe1pDHKWdRXg+Ijw7RL8fJTsElzXcfqOu3UdmGdBV63fC0djBbvM0YpsQshfuYH84x5cG4pynGDOKPnrCUzV3sw0p/PxElcNupDBiJGkjiD4JC3QLuka42/JKhLTnTjU9AI90xzDaHnetqN579AEA+i//Uvrf/xtGDNq5K6A37oUbkm6n9mH0mU31ISgqwlHULVm2MKYNT6EnNqn+nUMK7UUKRfogu4vROSUuukbAeRHMK1GOpKOnfG4Awc09466eFdX7jt8voP/qr6Qf/1j64helX/s16X/9L0zvAuhd8JB3r0CUiAB0oocN0nnO70qrCFdCI3vEdWBRsZmnGDqkcM6TQtpyrXwkg9QnjA5AnHiCAE5SoDP96GHcuRwRGn0rqvYfuy2gSdwW7ce3E70E3z//c8n6d2dME6A371c4uho3FanymT4jxDTMMdPo2JJBfi4KJ4hcw4i9SeGr70lP2Dh8VCTneeN6PfcjhIMwpQZz6EcYQytvNTuIGLru5Uof4oqqXj4m/XwqQP/N3wCaJHR//dfvJHr/5E8kXScY/9Vf/dW//du/GZj+4Q9vi+lf/Lx66z6lfxXdS2h23z6scSweorG5OaeRqeUzunHqokSTnUkhA9qupBCBaZxQ6OfGbQGW0EhfRVg9EqWIhOVIQg8pPStcHKpfffDERECTMP67v5N+4zekCX8/+tFEOU1orqtjAP/wl/z3D//wD8ab/+IvpsY0AXr7PnlwDULzCqNKflghCZ3s1aIFLdzOIUoZrQHt7DmBJTrJq2I/UvirN9+XnqyxLymsM6sC1CdRMaOJM1OC7egAG++Cf44kdMeo3DMuD66WB9dWHThZBmiC7EcfScuWESYH+/v/9V//lWBKeAVGv/xlML/iOwmyySQd/u53v/tLy9/fkGinv2efnRrTpHJs2y8PrZUHVild43IevFBNdcPQgTStvPCtoKM9tyg3Olo4pNCWpDBolpKJukUUB+ensM0O+aqKSAHMDcmFEUjooXXy0IbqA6dKgP7pTzH47zvf+U4Ro3/8x3+MQ//tv5UBes0aOvatb33rl5P+jBsgGJR+8pOJQp0AvX0/cl4GV9FnEIYOEUoKazSX+4AbqN5sUCRaXjieQruSQrMAgI8TvOFVQYUNjhdthwsj00+AVgrjSv8K1+A6eXh91SELoAmIv//7BMU//MM/JFz+/d///T/90z9NAWjC6NatdOC3fuu3BIJfe+01sXP16lWxQ2Ib/9LdDV18IqBflunSg2uV7hVKYbkKCd0PXoh4/3Zk0zSlVH/KVDnCVh3a8RTazVNo6htebnvcIErG5JBrDR9hj5ZmBbowpvSuJDTLI2urDp02AE2ymZSNL32JcPj222/fuHGD1eYflQFaqBC7dtHsC1/4gsDub/7mb9JU7NPOm2++KfZJeOO/du6ERm7VoXcclIfp4bAeErpjVMkN0KeCxyfSyT3uuZtyIA5Ao61WzGrlsCspfNLG4aO1ER2NNNlmR6SwibtwwwjdCZ9chgHdOab0rYTKMbKh6ogF0B9+KP3X/ypt3FjkgT/5yU9KgP6938N7Pv1p2n3//fcFar///e+Ld4ppa2srK9tfFlMS4XjtjTdg+CsCeucBeYQldM84SWg5N2iUQGBTNDLEmrgLrS+ui8IGCFAJ2sjXO4EUfsFitrMfKQyJOkm6CO0X1b1I5oVI5eiACyMziFpeBKP+laRAk5isOmoB9J/+qfTBB9L/+B/SZz4jrVs3EdC/+7sS7YyOFuFr8L9JgKY/Aro40uz1So2N0t/+bUnl2HkIpHBoHUlouXNUzg5Ch6YHCKrSdKC1fRN4oQ47dKzUYM62pPALt963ANpuhWZChqcQ1S1E4Cjr0KF2JVoAKSRAk4TuIpVjlYsAPby26sjZkg5NHO4v/oLAKP32b0ubN08ENAnvP/gD6dAhlrlv/L//9/9w8KmnpHS6BOiWFqmmBiYOiZTnv/niF7+I99DtYZHQVZDQRAo3KL3jCiI6BqFDJ3oUwxSdM0o61Sd0T7ETgI09hb/22vvSJ2rsSwpF4jesHAmIZ6FDwwhdgCBM96rtQ3LnuNI37iJFdmxD1eGzE812RA1JGE8J6K9/XXrvPamzs2Sc/uxnJZbKBqCbmyUSyVeulN5w4ECZDv3zn1fsO+Zavb16xTbX2Hq5f42rb5WaH2EdGuFTyC9synBp6rhRJccTMdrO2pMUWgFtx/BRM5YDlUVRhSPFgC5o0Q410aVlWYfuGoebcIhI4YbKw2cmAvpHP5K+8hVp+/aJgKaDf/Zn0n//7zBIE45pfPObCOFoaCgBmrSLYBBKC2nPBP3f+Z2JXpuf/23V0fOuLfuqN+5xrdkhr9gqj25Wu5ZzBjjp0FwCj+uUikJKeqlWtF1J4a+99mnpE0vtSgqj3NbErMsY4EQ9olkkoYkUJnpQGSM/CLPdwDhqIA2vqzp8ugxzpBtYfITCyvHRRx8Zc1IwvvMd6Qc/MJwmBPRf//UyQNN+PI5XSR0nBWayY+Xnf0u3kLxxn2s9AXq7PL6NbioAOsZFlUjlaGFSGEAFR+4lHpuqn5CdSOEklcNWtStZ3eT8JU3kXzVljFC7CBJjUeMLKscoyi5CQq+vskpo4oUkoSXp4IED3/zmN7/61a/+y7/8C8H0H//xH7/2ta99+9vfBqZJnSgqxAT0CYAOBKREokQBJw8C9JEzrk37qtfvdK3d7hrf7hrdqADQXSCFRF6bUwLQaGHhi2olHdqupPDfv/au9Albk0LdcKygSovK+d5qkEkh6anpATU3hJpGQoceXld96GzJykEaxf/8n4TODz744JdT/T3z8Y9L+XwJr1MCmiT0HQFdceS0a9Pe6g27XKu3yeNb5JF1Sue4GkXRXgRRtYikFeTJal6zD4udq4/+xuvvSR+3cfVRjn8AI+QqSiI4CYCOtItgaPYUjirdK1xDa6FyHDhphI8Kxwpbjg8ePEgS+uvlf4aEvny55PybGaAPn3Zt3Fu9fpdr1Q7X2GaoHIUxNdoFRT/YLgCNvkTCU+gJmxGkIZuSQiug7VhoxuieFkOZ/kCKK+bD9a2JUknJXjU3KDOgUZBucG3loZNlnsL//J/h27vd3yc+gQi7YniGADSB2AR0sKlJikbvDOiqQ6flDbuqN+xxrd4uL9+ijG5SC6OwQ0c6oO63ZLUmtB3i2nxRs4uhAWg7kkIGtI09haJqP+vQmhngrwVh6FBEukq2D2Vwu8blgTXy4JpqK6BJ5fjWt2DE+NKXgGwx/st/kb76VfA/wjEh1RpsRIAmBhkMWl3f0vDwHQH986pdB+Tlm6Fp9K9TOMxfzfSi7jpJaOTJwhlEypJWH0MJJaM0h41bUkxSOWxGCj3cftiHWA5VBPijnl2WDR1dLKGH5PwgCoT2rZQH11XvP1ZGCoWB4gc/KBsE9B/9CK9OQOf3vw/oT5Do//E/lgXlTQ4f3boPhdP7VkDzyQ0o6UEYoaOdDOiMinhortToTRgqh80LzZQD2oY5hewm9MbhW6nnbkDEC4XlTtQwyPSjuG1hrBoV+VdV75+UU0gyePKYEp0//KH0jW9Iv/mb0o0b0q1b0uc+BxfjHcSzAPSmvQrdS73jSicH+KfNQA4iha05ltDc9hP6RrjUn9O2nkJbk8I60aE1xF2NI24jBYvrcoQLyNtLsNaRHyDpqJKQ7l1RtffYzJNkf/xjpGZ997uIaiIpLnK/75r1vXmf0rtK7h5XCiOIHRWAjqLWDJch5UKpASGeY2a3WRuHj37xjfelj9k2pzBiFLbzxlQ0mEqxbyXNdcA6SoDODiodI2r3mNwzXrXn/soYiJwAMaZXl6N64z6FLt01jgzZ7CCavMS4wwuXMdBE45X6lIhIMbpq1Nk4fJQBbVtSyHkrXEceJQ/ruUWs6D4Yaue2QFxoJtOvtI8gRKlr+ewXmnFt2I2cc075xidJdhstD9tYQrOJgzNkRcq3SCu0cfioVULbtPooLHcwEUCHbmBDRyvyZBVRGRpCeoDAxC1URl07D85ybTvXuh1yF/od4kGR6YW7J9YFRoh6jejEDEZYL0pEx8qa2tuTFJYDem6Swtr7IYVmsxU/Wu8wptm3QlpHmEtzwF/Yr2RI6xhS8gToWa5t9wvXmm2oo4BijYNKshf9VqBAc9kkepgE4sLvzZWTIpaWsiFb/IKTSeEX33hPkpaaFf2Dlkr/9z41Ghq0PYBTTXNayxUIaoIzP5VRyIK7ufliOmE6kFSbkxrXa+Ta/Z1osIL6SUME6+qts159dNUWtH4jBTrdK0rMsL6RU1tSolIjwqp80bLQ/gnrrQlOHPPpFyyfSl9889PSkmZ3Y5qGztviuOdpQ8odKmB7/6ea5rQpo7fl3Q0zPVVTVpzE3ZJ1t+TcrXlwQaR8d2qxbi3ep6V60SSzfUgtjGrdK0mHrt41yzr0L+T1O+TCcrVrXCViiirRg1qyT4v1cpflLj3c7g526OwydDdnsCIsKlVabxM3D+fhNrbZ+fMLTpoKCf0pE+xitM1wSnd2Q9xyf9/HqaY5xf0d5SvO7FTc4g7nQY8VDcmFUfRCbuDkwpYsDNKhPGp0JLuVVJ8W73Jt3IUA/Cltzw9j/P3fucY3QY9PopCuEu1wk+qM6gVsMofHm7MTvBZrnXWl+EWSGsflGSMMh7l7aet8+QUnTqX/8Ma7c5YUhg1KUXsfpFBkFnK+NKFZ86PcjButY1Po3IrG9Hm04I5zZ7dYL3qs/OxnH/uzP/vYD3/4sT/7IXwlU48/n7TD48/vcNAYH+OBfTr/X/6sesVmVfQGJ30DPd24HwViktgHVM/dv0W3xWJVhuIClwWhlhCDFNWBBaYJ0DXBefILTiaF5YC2X/P6Ug/ZsI42KzEtAHOH2sRqNMnCIIOANGn0puhGIYGqRvXZqPpSTKlNKvUZtaFdbumQg11yeFCJ9CvJfjk1JCeHlfSgkh2mgc6cuWEilEqeuN2YnBuR0+j7raSHleSgHO+To31ysFdp61Was0ogr/oyytKM8lJMezakym2oLJNgVT6GOudAcysCkthgFxclZnR/1CjHUTJCm4BuMACtGpguMKDbZvQLWkuphx5TT+F/mGi2s1vz+rAR0MOZheIJjk48omE9bNJ5+OQiyANHQGn7KKL+CWTtQyga3TGsdK2AJ69vJbrEDtJY4xpez8HT6+WRja7RDa6x9a6xjbQvj9E+HVnnov2RDfLwBhdt6f3obr8aZ+hZgZIJNNA1mTTmQVwoN4DYumgBWIRxIycKNKoNCA/URc0kr9lA1lMusQxAm7J5Cgl9D78gYrZas+gcwMM8yWPmKfz1W+9IT9Tat/qo8ZgGGriuBVecQSgpu8FptGVRhpl+S/gOOwEvUmdJnyYRmx9EIB6NwqjatVzpHkdxo96Vau+42rcKGO1fLQ+sVjHWyUNr0CAZIXtr6YjSv1rpozevULrp/cvhC+wcR0UbWOiGtdwAV0jqg3WF7p8wKpyj8FeLUDZocD07Uvf9caOAKgdy6OVdsPRlQdahC5MA3XavvyBtudk4h0O1EI3OWAH9OOUU3nwbnWRt2pKiqEazeOZ0LO4eFEdch6gMJso3onN9B/TpTD+r1D1EE5HPgs71cLuQQJXbh9CznnSSLtGzflzuGUcd3t5xpWc5qmoQ4ruwo6C59zgSYWjaMUL3g0zb9mFUViccZ7mUTLIXzRETnUqq1234BVk0NhOdT6Imjj+uQ3uOc2akqWzUTZTQ7pIOTduCGx6Z9nKV4+6/ICkYmgloMdDPuCb4WOYU3nxLWlBn1/DRYiOsiNHizRvTvVGdUOJPqAEuDtbI1WfwqM3A+pskedmuxLqRcZjoAvJSfQrBmoAOsTqk5gcJmjKa2g/JHWMAOmkmdAQ7o0r7KO+M4Uh+SGnn2FRCcGaAR5+S7FG41RVyzmPoYAsDogjbgKaRUhvgGkR4nZ/R7OMEhTpzO0V6LOS0voylNbcQ1pcFZ/ALTgC01pKZpHI8Hp7Cf3ftdanCY19SaOxwIARqdEREYLHuF4oHUrPUphRcLc34FdV4D1ePRpdiRHqwI1oY9QBugnV6AK19siRrudp+BjtiqLl+c0pa+BCEcQZKhUJKhfj3RJcS71ZERneE27eF2xHLL9raNnGhUQJcIEH3GylImtfsEuQ1Y5JKlRof5C+oc3CIagU0JPRjSQrfuviKJPvsSwpri92Rw2bsZYRrLUfQ5AE1duOQiKjjyBmHJJVREb1DlNwFrCOdiJymEe8WiITkJqCnelGNN9mFlijpXqjCNKWDJIDpSLIHop3ejPd3o1uAMGXghB2GehNkTSOcZ5MzZDNMiqQ3+8ziuVxm0vB0GqRwpgUMpvMLGo7GkLF9PMNH37x4rQhoO5JCo5+sSDEMmZiGSq0jFTzhro9yoS00m0KgJgEOeYesVQdzUEKCKKbPvK0dlhAhuRFxwbCOs7kNO91QJPBSJ9rQE7+kEWOiGWXOZ9gi8tz5GKUU2J6Qgd2wETGiKknl+rgeiCP90Rc340XZ/Ow1Mr318iiOufkLhu6LFL52/oqkeu3bp9BQo0Nm+XvRKdmsqSVkYT13tK/n0CXSARrZj9jMfS+FrRptZ9sxgEgoJEBhGH0tSNwiu0T46sIcHAJFQhxpxwjm8e/GTg5VQYSCIZqn4GmQ5wgkKD96PTcj9HIj52IoUokJTO5QOLd+QUFjokb3oxk9aqQbpy9Kbr99SWGx47fQPYx6h1GkZuGZHtU8HJlphP/HgTwEMHH7ZGEDaUZrQ+ghwgoBh3laFYVuW7hsTVvOBD1HMLP0NQ6ypC9adnF7kKLckjbC9sFHkzhzIKUHkhDG/ijXgY6VUq1E0o3hVQlP1V1lTv2C4mljAHpGpPDqibOSXm9jUhgqk3AiCVwoo2g2HNNFCi0h28+mPQIoFGtkPaH8bkOSuRqXXGrMAOLNaZS4FYPJEwDawnIXZZkyiJITL4mp8OBwSVz420H7Em5um4IiuQ0Jd0tWFfqPz/ylvbESlD0czl/K9J7Tv2DI8HAJIT2jCFjp8rHT0qKAjUmhtTeFCZGicVpMRVVPUXW3rd3t5aRUKABJUceWZTYr2QECYtro1dLE8rVRwJ0bR6DUfkIzbgNUWDSPpEUgqIp/T8C63IBadaLtC4LIhPnFEzf1orAhlQWIjdIF8+kXnPkPKl08ekp6KmBrUmjtUFEUz0ZtUlbpuLGQoba2ZrlUqRCZcSRuwcHBjhh/DIpBfVxle59OGOXIIUHj3ARTRj9zu6TOhZr4eIp4p04ivz6GwAyifXgURDmrCqwUEZIi9oh1DN1jkc117Bp8gF/sPMgpZEDX25oUTqjdWGuGK9VFyjwvorBncxq/gfFYjAFeCGmK66QP+CKGWkKS1WuEDUHr9bL5z89KC+GYthgJ3YtWGDoMcHC5674wZ50wdumEQirTOxtShkiuNZ8hxgcLlUcjhZxfECrHBYuEtikpnPgVWxO0IhYlO4ILEaAFd/EwxGG9jqFLC0tQXTSJ49pirKWE2ZkXEX51Q8YXNQcvV+4yyg8QmkUvgZjRl02k0tD/BlJGzV9RFsdqmZm6x6atf8EylcOupDB820JhJcc479CFmtJlkKqz2EaEFC/Kda/p6RAQFy95Qoa2UCygYbj3gmZ2Y6TszHQPBBKG4aUU7BqeeSfCeV9o5vLxM9KieruTwjs8E63f17KwuUCLWlJnVVEiJTtgyU1TNAtGDc2hqD/UWl4SfSTqio4e8zlAgJ7w6Hh4X+w8KDRz+fjZIqAdSjHtBYZ1q3lk6sDUiAHNOi5zKixrdXfw54VLNVGL8MUCY49igXOWFF6C2c4hhbO8wNB8X+CjI4WnDh2TFjc4lMJZ4DwhhccPHbUA2iGFzgLnOCk8efh4uYR2SKGzwLlMCk9YVA6HUjgLnPOk8AQkdMChFM4C5wkpPH7wqEMKnQXOI1JIgH768SaFddZOC0GHMzkLvBMpPDHRbPf4UQqj+VrECHJwOJOzwDuQQqvZ7jGlFEUJjXgGhzM5C7wjKTx2gFSOx54UltXxdjiTs8Dbk8JjB45YdGiHMzkLnOOksBzQDmdyFjjHSeHxgxMktMOZnAXOZVJ4zAJoh1I4C5w3pNCqjgRnOC2jFPd3qmlOoYHF+IoP+ULOAufIAhnQzzQZ+T91UcvOvU/pLhEZSvd/qmlOvVF06auLPPQLOQucIwskleOo9KkgspHvf/jj7rY8tg/kbNMZ9F00Z2bvcs4CH/sFSsdJQi8OWJq9Fcc9TkV1SnS5Cxr0Ysanmv60SCke9oWcBc6RBZbFcjiUwlngnCeFxw8emwOeQseR5ixw2uGjxx1PobPA+RQ+etzxFDoLnE+ewmOOp9BZ4DwKH3VIobPAeZVTiAB/hxQ6C5wvpPCEU2jGWeB8IoXlgHY4k7NAJ6fQ4UzOAp1CMw5nchbokEKHMzkLdEihw5lsRQrLizU6nMlZ4JyvPuqQQmeB84gUWiW0QymcBc55UnjyyPFJXbBsQymMqPBgKcLdIYVznhSWAdpGlEKvDanBdq2tXW1r587y7dpE9dEhhXPRUwhA25AU0uMpBBATlHmotMWHd0jhXPcUTpTQNqIURUCrAtYGoB1SOKc9hUdO2LB5vcYqxwRAQzjVBB1SOMdJ4dETtm1er4U6gGka4YIW6XQ3Z7ndZdAhhXOYFJ48etK2pFDzxTD8Ma01q7Vk1aa01pAw9TmHFM5RUlgGaLtRCqM0hBZIqg0JLZBQ6xP6spBDCucyKTx6yraksFhNXW1Mag08AhMktEMK5xwptADappSCJbQ7IGpJFaWFQwrnaPgoAG3z5vXBiRWxHFI4h0nh8dPSooBDKZwFzpfw0eNnLIB2oiudBc5xUngKErreoRSP5QLDDim89/DRE2eLgHYoxaNcIK3ipTb9+SZ9SUB/up523J9qhb2cFviwv8llIX1pm/5SG10Rl6sJzsI3SVfEemuCujdifSbcNyk8cU7S/Q6leIQLJCTpi/zuF1rU3KA8tt61Za9r28uulRuUjiG3L6E/1+R+ofmhrIjA9ExA1330BarpfjXbr2b61PYh9+IGfVG9/snmB//F1oT4ivXumja6lkZXzA6ouSF9SYOu++mK+rL7Dx8FoOsdSvFoFkiQUn1qqrdy39GKc1cqLlyrPHe14szlitOXK89eqTh/jUbVkdNq3ypd8ZI8e2DXJZH8VL1WH3dt3F159GwFXfTCNWNcvlFx9mrlgZPK8o3u51v05x5Yvqn+lF/zJ+mKFcfOla548TrG2StVB+mKG7Rnm93PNd4fKTx13gLouUkpauckZ9Ihd6NVL5+ouHit8vSlylMXpxh0/Awh+5XK4+e0SCdpIw/guqTJPNdatW1fxYXruG0mXPrslcqTuC4wd/aKPL5Zd3n0ZcH7uS6u+Gxz1Y4DQDCdf+ICL9MOPsa5q5Vnr7rGN+kVdXfy196FFBKgVd/E3I2ZTY2GBm0P4FTTnNIy/DFc92Ff6EEvkKCpZfoJNAzlC1OjGb83Df69aVy45hpdpyue+7ruMw1arFBx+grDaMJ1L1SevMCAvmBM6aLnr1YdPuV+sQWgnNF1ccVoJ+6cKa5YWmDpiufoiqe1Zxqg09/7AqWTZy5KL4XdjWkaOm+L456nDSl3qPD/t3clXlEcW38YhpmeZpbuRqPRKAqyyT6salwQFfeNJM9ojInGRI1RQWGYfdgVRECNGrO8xBdNlEUwq9vLe99bzvmW853vT/rurd5nYwYZQJxz6vTpmu6pW7f7V7fur251FR5fvKgos0XrOFsNWxB/QVOrYPYq67a3Da29Rnen0RUxwQ2eLilLtfVaDn7MLSubpNysauv2UHLd0O9fNPovUZBaL1HqOlCeLsrdxZSs5/LfjFluVpVl61tUBE3VCgqJlwjl5L8Z63PW2B0eDTb6MkWyTTILpqtglcKAvUBRUWbRQlcRiXEWNHUKcstLmbzV2L2iRfSRJJ0EZflOWXEV7LS1Zjf3RlHMcpeVMkVrjeC92BVywV93t6d+cs5c/551y15Ipg9O0Gdb0MlpUVSpxU81eVhTllr3CeRy6SVsyVqDpz0mBfmrFEp0cdY8sfuNVq7G4WrVMHkvJymsEEjhipeJFHLmHGODQ/RcpaN4AggDaih0wX7JxVTc7AM3lF2YL1Lh6OXmUs1epVxgnKaPTuNQXWo2l5rF6TM5/QpuUVEalWWt3GQE6ib4u35eqOnYZzgkEq2+FSyTQzd5iU+lUFCtL88I1QqKV13t5qOn2bS82Eihw+3XWPOi9biXVGjmVXKJQNpkiwJP1Lx5nwKyMkyNxEtOPXYaOncaOJmaMylvRq72/glCEKOWu6jI8vb74BDLsjwd5p1/4gxZ4JQza7aZDn+cerqJPnnOfPAjZhmOSLBJGfTJRsrVKsv1dVnLa9nl0am/uMhSfxhJQiAxUHBQu58pWZv6SQPl7QhxtcVn8HayJeuwW4ieFKoBHWm0T7O0on5Xoe+zTM1rleHiTDhOzuXC4+CWloQuanERXp23ksUbXrlIIcfk0udaRKOlsEaAYE+ntW4fZ84GSwn3UPw9gRZaNtIM7rsQdTWYPErZJDwdFuCXTA67qJg+1YgNDIDrQHGUD8cKTW8d5uhsTrPA2OxVVLIVnBN8d1HI5biVlN0bwTajuDN2HH5emG/dWg+NTTHeIt+TeqKBS1sZQ6RQCeiQYRiOr19WuUZT9U3Hol9v0HDCkauc+mZQ1XSq0XSy0XS6CUgPulABRS0qNB84avr0QurpC8yuAxLoX5FIIZdZxuStEtxTJbzAi21wMuAsvlGM1tSaQ0Af0kLL9tVSuxsjMtHIXW6zrN0udQuIm7N2LmUFtq4LHiqgfDJsh33FB5+CY00521R9iLuDXViEliuy3GU25s0d6o4o1ADO2RYMqaxchcGj7Aqqwam26OThONvYxcWR5vQGRAod7lYctguDeiYTcazRVJNj5b/u6P/3R11eeTH5BX80ZSj6Nc0yw6VBQ+9VQ9/npveOs68XBK4cwGTT512Gy9chmY+dhqb5SkUKwd+wbt4rvma/7Mi+exSd10ybcJs529jkxiFbTwcJPZATkmTX09lmPvop91pUX2aAc2L68KTR2SY3hk17ODY39dBxowwgv2ocGkW0Uq52ta9P/rtxD/DLCTqiRYWmQ8fFpiL0CSR1yifwHC64AdA8C+IybByVBQ4PeT4KJ9vbyazZqpA4UaTQBaTQGpoUIpoXlg/3z/v+4sLvuhfc65337zv0v76jx6+lQfbOxYU/XZv3en6JOUPhcrxRzOSt0Q/eMh/6iH29kFtSzC0uxmkJkJaUkIdbyGre0PffNH/4Kbew4JWKFMLLM31wglhc8rbICVO9hQ3oxJeXWStqmapNkKzVW6xrtlnhvGKjdcMuyi62BOiLTzVitC8aufNWpp5skPwcwBO7oJCblw8uLBXMxgRAC8l04pyKGrrbzeCrwFuOKBdaC7gTkkS6xY+B7sqNqFFVLUmoHSjFZZYzijcI7qilajO2IqdfJgwHjuKoTpSk0O1p11jyIrDAus2F//hW/5/fG/75Lf0ffzZB+se39L/vUP99T/PpkRziT8uciVu52lq2UT9w03zoOEdnWd75wPLuUcuBo5Z3j1nrDyOyoerz8vX9NwigC18pUggG1XTstBBHgHd2roW15BLHLOhmoCLAvZbbwEvhcqvwZDnGRHBMlxA7eOX0Z83c/JVRyU3LMzY4hGAkHBscLJfHZFcpqFiwhQYK2GnduJvTvEE3OqRAJvQMqUdOTBiwJF6TMJIDxthSs50D64Ya4ZwkQbXlJFAV9AY5HHXJFZ0u7ChMB4+hTxIlKfT6OjTm3LCuPWK6EpyNb9oX/fuOkQf0P7+jHw5wmiVlGq4y0IDlr7KWI6Chx0nTsPrLn6dcu62/elt/7Ut99yD4JMRg5PMWml1Y8GqRQgD0URnQdIMTx1nTS0JN4iFj3svJ2GpuFU5Mg+xr+dbtb4EXK/jBp5sIoCeWC8SLOu+SAX2mmbXmMIVrFd68PwDQYIlNR04ia8wog44aZ3e0iPD68BRxdSI6OYQGiIDusNbsQuMFKvB6ZeAkOyEb9AaBWXELiuhGJ0+LQSL4IdyiwmhJodfbobHkRiaFYIaPH8r777v6f36Xil7HHeM1Vzr40FzwnEAB0OhyoEcBryRdTFKAel5BgMvxqpDC1wvRRxSHXSmnH96ZddV2Tj3UCqaLKathwOsor2UqNzOrtjLlG+EXa+1eSiJn0B5ONuKcuGjkAlk/2ai0skxaPvh+RneowTKsnh/wxFHZQlHgYNTuwYkfxOUw1b+H0+Iiyk2TAS2UbK3ZSZTahHoJx1qmrJb06gqXg8u1rt+N01ek/zrboJPHvj1KUujxgsuRE6m1rUBeONzP/c8PuqG++Te8i4EX/vF1ikZTwQRxJgR0WS0PaCCFHB/NVy7vibOueECfeuVI4bJS67qdgaTQ22k+jO4Zl6Eghc1ejDgAI/R3YfJ28dRQMWzXborabnGvrTR99Jk0ZQJ96LIajslNPXHOKA9iyBYaCmdfK0a6hm+/DN4Xp1lg+uAkBn2AFNa9xaVPRAoXFpre/ySQFPoIwfV1iccuqsmDxl5BClPfPxlICj0dDFDY9KhJoc/XqXY5Aj1uU0ZFSq7t/x5oGo7lkLGOqrd2FPzXPc3bu/PBww52OaDlgYNhOvwxkMKQExfRQl/9AsNOrxgpxMa8uISMTKlGZ3FeW6OTzVnFe4rCsJ00WOtoDQqkYTNgquvAE42KjKaXWrfsB19WCkbSH53mzDnMytUGwetQk0I4Nnno42dY8BPm5xvBh4b62MkEKU8nu9SmDlKGkpteatlWrxyWCdEVOHw0GbbDNwge9spq43m30d0WqCkAOqOclaf7TUQK0Ye25EbwuK2ZFZlFttTcUg1TyfEGe1GlJq28pLJUHLNTkMKCNWzBm6ZPGyxb9rJCqwpa9nNpielUA7iDQc3uFYgUpuXRH58jFF7NxsjkScuOtzEErcvE2RSSyxsUKcTX3OwFExstGYVqv15COdsUw9idTNl64HaW1VspwBBckhwSaG/uDvTvFxZwlhzz4RPilEDi959uAkLJTSgXJC4sUg6PhFABvfkWjs2FDsS86wC2N4WLIk0upT9rIppGvyWFw6thJ4gUAqZTM1RXrZnl5owwnAnMxvyVkfsIbn4+t7T0VfymEPzj8o2q2Rpqu0t/ctZaXkM7WsPM5RDjfLsPip+xRCd3QX7qkU8E6yuOZLMZldjjp9tSPzwJ1hE9AUD2BZdlzyGc3WHNtm57h1KYcH5eFLFTUUQKF+Sbj5yiXG1hI4UE09bKjeZTTQFumAx66Ig27ObSS2OJFLbIgE58UxhvBRlipM3HThvFcYOg+FmravAheHIS/HLexRmzmRi/GmQXF4uDFULJAF/ruu2cIZOz5ILjzmZUwD04vmbOYo3Z5oPHjZKXIgyPtIDNjlYuGunCwOn8QfqSqHtbyKuUoxXNsykntm8Knc2exDeF06kg9tfWHKrJHXbCnXqOh1FBrQhJ6mRyqyUGGYPcJSXWTfuAiqkCy94O+ozdsn0/U7SBY/LY+QXMhp2Wd44Ym32U2p0Fx4MB0p9ZFgMJXlKMkVFBYrQKSleh+XGZlbH2hxovTh/NTXxTOK0K4ihsOdUiEb6IM3gUFhoZ0uo69TezsciFzuHt943CMIJPOS8KRyGcrWCz8ZtCZ1vgJE9oRWvq2KUxy8URw7eOqF2sSArKaAb3Zs1WoFtMjM9Z097arTHlJJYpmW4Fl9uYxaV0gyNogqU/9Cd3rjbK7mPKN5Ag8OTlAsEy7z+MCAv3FaM4l0MctG7FmX2rNpOR4MnI5bgc096DOATp8EdQUPY0iERrtVJiLN8Udnf0aIzZiWVKZkBB6MGpbHP9YYwjuNupYI5P5nvwU5TMH5xk2TwyR+cF/CV+/T4gghUb8dPrkLCWAA3evK/LdOo8A4710pIX0Rc/oCyrxc/LUWJrOECD04zfTZ5sYBeXsBMNdYclhb3dfRp9VoIUzpSC3OJCbkGhZc9B+kwzrmQAjFCcW4dR7kan5b3j7Ioybn6+NBlhckvJ4NqqBcKqwWzeamBs1rq9KJSf+4ZfXLdhVMXfBXIpF059Zqo245BZZvkU6LvcxpmyrXX7jWfsonYoEY84rxAHWIjEOiJx8uvOaPou9WtSMhOkcCYVhPovKebYXHZhEZO/mqndY926nylez75ezM3L5/g5Dy/ctzBFaxnbBtyrAJJtA/hO3JJS/Bpjcal1yz7LngPmQ0dTDxy17HvPsnYHO78QJ/ILgZspe5I48YjNYxfZQKJ577umg8fMB49Z977LrNuOq9vMW4mzVl5woRm00IasBCmcHQoSvgh+BXS4GcRDyKpkpkRBBPSbVh7NJMlkAJcgK8WJvq8XsgsLcHIfGfdl4vckMzGUyEvE9ZnAb1xWOmWrj3a3X9LQCVI41xUkgGZUgFZ2NVOsIEfWMCFT6sqwh5EbZNDNskmaotVHu1u7Nak5CVI4xxVUAxpNtaBIHBTMLEP3pmS9tQS3YsKUvyaMCnFYfbSnAyx0doIUznEFAdDABYvXccVrmeJ1kLj8NVxmeVxCoQBoECG1nNINuNxUaJ4Xhy0p+rp7FT50ghTOWQV5NwDHCjNtwtz0OCmoALTg3hSsCbPXRxy2pOjr6FWPQydIYULBF1tJJ7PMqgL0BqYguJHEbUuKnraLCVKYUHAKFQQWCBbaikOEOEpota0ngJ6uLSl62xKkMKHgFJNCduVqLn81y2+WBym3OsRCYXEihX3tCVKYUHDKFVStdcutKJ++fQoHMbCyIsGZEgrOkX0K+7t6NVSCFCYUnCv7FF5JRAoTCs6lfQoR0LpMfgUQOVaZ4EwJBV/SfQr7u69ocldb+YU/yCIg1spNzORmDCY4U0LBGd+8frBnUJO3mgEQS6lqs3rhhQCPKnxWRSli/O/ksuiBVROJcRaUUPAlUVDTeM6uKa1hqrZAslZtxpNVdSgGUxVJlYpjxCy0kqK1whSqWP87uWxOFQ52yrWNs9yEgrNeQc3WPfVJq7Yxa7Zb18CRpDd3sHmruJWr2VhT3irWVoPHSfx3cikfJ9xMn7iEgrNeQc2+dw5obBvRb66sJUl0OaRP0IQ0UZafnI7b+InTxqP/76SzEqWIt6CEgi+Jgpr6Awc0ZbUE0Jt5H9pavZlNkMKEgi8pKdyxa6e2mEyJIomFY9mGlyTOVMFmB2/rNpcCaXNewThECrduqdUvLeXED2aEL2emNs6UXsoxObgs35SntDz8aprJjUvhwcmag8txW3OmSdwcUxAwIK+LF7dIYW1trWF5WfziTNwym7VmF32uhSabX9EkpSqOk89+eoH+rCn1s+apLzlcFgSdtaPceAuaewpCmedacI+LgO1/pjxSuKF2E7W8LI5xpvRS3IWOrGlCkWQU0xRkna24mkQ8Sg6XdbcLS3jFW9DcU9Dbadlar96CIw6RwrU1W2gR0HGhFADounr1dh4RlqOMMRtq5+D4ZtWbRMVd7hxSEFcB3lqPS3PElRSWVKw1Z5TFj1JwKkBHsd5mTNnwa1fGJxuw9Js/7nLnjoJk96C6/ertLOJACvOKqy2ZcZx8KAI6/PqTL5INs3ZlHLOyAfNPB8jmjoIE0MRCx5cU2qo2BK3FP6WkML3UUrc/eD/gqcmG2bEhjln14pxxlzuHFCQWul69L2McSGFN3S5qmS2+pFAF6Clt96H3do+fJQvukeMsd7YoKKLT7sXt7O0kxVYrwYeOOyms3bGbSrclSGGCFIZXULzH2U75u6meQaq7n+q6TAOyGxzGJs/sIoUbd+yiot4ELkEKXz1S6KPt6FinfPGN9tHP2idPtc/+mgTp+R/ax0+Txx5RgzeM5xzEYM8OUrh9b71uaeksIIXSVW9Q+1be7I1owAL+641oOUIKmqgaKgMWERmRauWP9r9h9imczLOa4OYwpNDupVovan/6Rfv0ufb3x9rf1On3J9pnz7WPfsHx5ibvrCCFhw4f0bxRMvOkUDEIT0nHFvWi9gFXHbjAvVHafi/wquJE2qMk1FXxnvDVgGRX98hC9cJu7hSpKId/go2h1AriRlIOn6qSLeFUkAuhW8I+DdxWOUSFQ1lo3NC2G60yAPe3x2ETXH3y3ODvNjZ7Zp4UvnfoT5o3SmeUFCLV0I6Oa0fHkkceakfGhDT+E+XrlK3FeRf+ODom3QA3Jz36mWq/hM+97aL20U/yf0eEopLh/NFPFBi5Zq+hdxDKVF5VyjJ0XTY2uQ39n2vHHqmujo7pvv+RPu+S9oiAO3lZtPz+1Bo1e/UDUM5PwYKEOrddDDM0hhhSVRL0hScjVbL7Mirr6YDeP1CFh+N0k1gfO3kg4z8lq5+GVI6+dxC53USkkG5yJ/3+VEDt46faX35XIRvOf/09+edf8RLJUoFlzgQp3LFziy59piOFAOgHI8n3h5KHRpKHH0LSwhH8M1cb3egk7wkBjZdGHibjncPJD4bxNgB9+yUAFtXajZfuDwv/HR7ly+HvMRJAU5eu4C94j+Iq/jIE/zV09MD7M/Rd1Q6N4i/81QejICjlu++NSkB39vKywhqkZo/hyrVkvmRRllirUaxz60VxlCDIUjZ7Ays5oqhkZy9SMU+7dkQunC8ZoE+fc0DDw2Zm9+EDGUZFAp8GebAE0J7IpJBucOh+eCCA9bfHYFzoBqful9+0j5/w8AUoU/B2zth1UEMA9+OnursPjA3OGSaFddtq9ctsM0wKmxHQgCT99duGS/0USYaL/fpbt1O++nNK3zX0z8Ds9Qwauq+kfH1HOzSi+8sPhq4+/eWrRnc78fO6yTseprr78L+XBviE572DVIsfXrP+Uj/B6DClvHr5qhbeNwK613gBAH0NAf1gBKRTF/tSbn8DsgDQtBrQIEsrADqURs0+/ZXrUA4kA6mMoBERqu+9anS1hX0aBNBQJQqUhft7BqjeAaiJ9kcA9BiIBqFoocHoQsUGbkIr5UvWXxzQf/mN7qtv4Rdjk0cA9PBDw+VBgyhaqAY8EC90fRFJoR08k1bgfzx2dd/dpc8041UwK+M/oz/98680wS48GWi9+Mtvj4Es4t6e9hklhaVVFYrAysyQQsruRbs7/JC8DDc/0okW4vt78KP+1pcANWLIfWCwU25+Ba9K9+1duIHfepUYpC5432CQkHEDzprUiQfZpQHEytCI8hJ0AsTVGaM6eozEQicDoIdGQZCx0WW4egOwlQKvE7J2Yed3SrbQYWgQNJ4r13k8Yc1Bo+D6hG/bWMmRh1BhvBNHfH1g9nQ/PsBKdvYQl6NdqDO4LnAPVKPZQ/M9GDyuwZsgFFs4WvcxdMGDKxAhFsgDusmjv3Fb++SZ4F08fWbov4G72Lf4Us87wXILbwTQ3NWXJN/2XP/5bSPfo84UKVyanc/Ec5mSKEghMUsE0GBERZTgw9L95S6a7ZsioMEeXHDrJECfdwqjHMRCo78IFtrTjltHyqkL3jpvoQ3EQoNhwx8xkaudPYjOkYcI6AtuPQ/oByO48bWvI+Xmbcjq7tyjvB1UW7ewzxqx0AqXI0gj6EwA0EPoYFD+TkGQVCVAYQRCybscUB+oNla+G0mCr0NHLDQlWmhsvdD+4ZIY4AB/CdoqCDUMfo6PCwGNPQ8oqJBOTqDMwB2LAy00WpMHo0qPGayv4caXOPAMD7PRJdjmnoGkZ8+VXrVuaEQwNDNFCm3Va00Z5TNOCkVAD8hm77wrhQA6RQa0HwANWQJo4tcKgPbJPvSI5LCKx3GRFPKAJn62cJXczP8LXY4m0eUA15OgHEFJ/HX0McZ/BudVAjQ0nkikEHrhYaVPL9cnCeoTmRQqKwl1GB2TK4mA9gouB2QFQJP/NnlEQN8QLPTIqFABxTPBE8GHjkQKwR3XgYFQD24k/fE3ww3RAMPzBDT/8beA4Q4dPBYB0DNECmu27ZBC3zNMCgVAeyQLDYBOJoCmFYDWI6AfEkArLTSQ+kdK3Gh5JoRcaiyYFGqVXC0CKRRAQJAB5bjbjE5/TKRQJYjUKnpSyJtYkRQOq0jh6EMC6B45BN3kSVZYaHwgY48Cn8awAHHVcw5FCulzLbrRcRWgwZ0AlZFL+Hmh4Ibp7j3gvWcloI0qCz3tpHDd1q3GWUIK0YfuR99RsNBunQho4wWBftEXPJDVEgtNSxZahALvTYpH9I8Ff9TRKpNC8KGFO738PYK1U5JCuAcK54sCP9vXhfCCHh8IKAF0tKQQfGieEsi1Qrl0hLiM5EODCW/2CNA570wOJoXDEqDJf5vcMqCbPLTwQFRy0YoHP+dQpJBGa3JPGuJIevo85fv7gHKKeBpUVy+SCvjLGXvy3fsyph8/AYiTSzNHCtdv22ZUTU6aUVJ48Qp6F/xbR1L4g4oUwo9ICr8UfOhzLfg6HUpAe+TUJAOadzkkUkhJgQPwVXAILCQpdInl4BbCAsEigJZJIVbVE9CKQpBCZa3426IghVhnuxgpBOYQiRR6JFIIf0RSKHgFQXIvuJPv8+S7X2yNYUghdDJ9wtgFwDrlzg/0mRa82uAwXL2V9Pe/64Eon7VjYztr1925K0D/2XPD5UHsQ2aQFK7ftsO4rGxWkMKhESB8hr7r0F8brlzXw8nnN1NufaXvHeAhDigxXL6m+/MduBmwbui9qh+8gSUDrN1t+oEbeAP/X/J3/cBNNFoEHCpSKPmvdq9AsAJJ4TAIkqqhv3YL3VMB0DIphJv1vCxJ7sANfj6aQAqB0fZdU9WK3KPesD4MKcSG6hW6IAR0ECkcGkm5/gXATtIXKCw8Lqq7Dx8XeSAquZiuiS6HgnyHjhT6jI0uYcj5yTP97W/osy1gQQw3v056Tsbynv0VcAwWGn7X3bsvxlaeIlMPQzeniRRu2L5Tmm03w6QQnvXQCPH2SABibBxfPHb9XiFSiG6lMrAymjT+SIgUAgcaeyT9VyiBuIxgzARS2NOvsNA8oH0ywZJIIf8vZVFDKBFuU5JCMfYhCuJPHo5TQtuTSOFoQK2Sxh4RHzoSKRTqLE1OAsv6I/DUMRUpBAd9SKHs6EMcGIYOgYz0kQcyHvg0JB+6Z2DiSCHw7+tf8EPR6HLcuWv49nvVmMbT57ofHujAhyHDdsIwyAXPDEcKa7bvmvnpo0gKcbBMCzAFvOLJCPqsGPqWR/EIjoWreESwjkO3S/OAhl74vnR1WC5qdNwoAlo7Mg4goOXZvT4jgIN03yIpRCAqqyEVheV4eB/6MhrIYEF8nUnnDnaRtL3hwKL4OkckhSTePm5UAJoWAD1u6OJJYQfGwx+on8bQqDyTU4o0haokVB4BbfdMOH0Uxx6eIYwAAAMeSURBVDqg2fDDzGCDRZdaFf3+XbTiw6PECZn56aN7ZsX0UVU0xB06ANGsvkpiCrRD8IZDXOWzEp2X7gl41s3kHvIW0XjLJaiLgtvs+L5poZwgQYo6AysIfZW4vLQ94hR73i0O+GKFd4KlOVLNQZVUDYr7aRIcCV1JoZxopo9iTAfMhBAyDJPQfg+PGhtdEafyTRcp3PXOu7ols2r6aMhsmJtDf3IXIeuL8fs8X+CJanalLxYVfDFMSp5AQf9knlWU1Qj6phDsNHjnYIOTcAapanJS0tNnSU+e6m99jQR9ompMEynctv/tlKWliW8KE98URlAQ4wAXPPrrX+jAZfr19+RffsPj/WF0mqGTPO+eRd8Ubti2a6ZJYeKbwpdEQXBXGp0YCGxwwBHHm8GHmW3fFM4KUpj4pjCx0MxUkcKa7bsT3xQmvimcOwvNKC10YqGZxEIzL/1CMwjowLkcCVKYIIXTu9DMiilcaAYAvbRUXNG/TLHSf+xZYUMDm+rq0hJr3T7K0x5qqYcXTg6f4qPoaUhe8r690ydxDimIPnTdfnZpsQo5AMS8akTOpFGnzmpqdu6j8tYwhWvZwrUcOUJixJPos/jfgjfZ8k0MHBVFsXmrLbsOUv5LRlcn5cZkFNMUZMGT8XbFWrIx+iyU7BKzLpL1dcMvEwtyxaiRKyhLEp57RAVdQrFGt6JWsWo0YTWiVNAdu4L+S4AELm+1CmZF6zhbDVugBtILgBBJYeoyW1pWRVpWOX+chwlOKuaRNB/OM8vTVpTPz66cn12BV1eU879zmbjtLB75tNzG5Vdzy4X9ALhMG6alJczmvbSzlW7y0E3uVHKkmyF54JhKjpGzqfJJUNbuSbV7A66qimoiWUmunCbOQgnmFp+5Bcs32b1wAsnkbDU1eyEL98AJfynVjkfhhFw1233S76qrYbNwPylQvMoXYnb4LU4/kYKVgdvMcKnZS46+WDWSVQubddMOX+oFd3Q3x5h1tjJb9qallxC0EGzACbCsvCqCIhvBTJl0NY3AbF5WZdqKMkAdYA9S2gpEIGI1s1xCrOKk4v8BOMdjjxhx3wgAAAAASUVORK5CYII=\"}]}"},{"id":2478,"title":"BLOCK x3 (Version 2)","description":"An extension to problem 2451 ( \u003chttps://www.mathworks.com/matlabcentral/cody/problems/2451-block-x3-version-1\u003e ).\r\n\r\nIt is based on an android game - BLOCK x3. The objective is to align the 1's in the matrix using minimum movements. The valid movements are  horizontal and vertical (by one step). A zero (0) indicates an empty space. Your task is to count minimum number of movements required to align the three 1's vertically or horizontally.\r\n\r\nNew challenge in this problem : There are some static elements which can not be moved and can not be included in the alignment. These elements are indicated by 2. This makes the problem a bit challenging.\r\n\r\nExample:\r\n\r\n\r\n  Input =[0 0 0 0 0 0 0 0;\r\n          0 0 1 0 1 0 0 0;\r\n          0 0 0 2 1 0 0 0;\r\n          0 0 0 0 0 0 0 0]\r\n  \r\n  Output = 2;\r\n","description_html":"\u003cp\u003eAn extension to problem 2451 ( \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/2451-block-x3-version-1\"\u003ehttps://www.mathworks.com/matlabcentral/cody/problems/2451-block-x3-version-1\u003c/a\u003e ).\u003c/p\u003e\u003cp\u003eIt is based on an android game - BLOCK x3. The objective is to align the 1's in the matrix using minimum movements. The valid movements are  horizontal and vertical (by one step). A zero (0) indicates an empty space. Your task is to count minimum number of movements required to align the three 1's vertically or horizontally.\u003c/p\u003e\u003cp\u003eNew challenge in this problem : There are some static elements which can not be moved and can not be included in the alignment. These elements are indicated by 2. This makes the problem a bit challenging.\u003c/p\u003e\u003cp\u003eExample:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eInput =[0 0 0 0 0 0 0 0;\r\n        0 0 1 0 1 0 0 0;\r\n        0 0 0 2 1 0 0 0;\r\n        0 0 0 0 0 0 0 0]\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eOutput = 2;\r\n\u003c/pre\u003e","function_template":"function y = blockx3_2(x)\r\n\r\nend","test_suite":"%%\r\n\r\nx =  [0 0 0 0 0 0 0 0;\r\n      0 0 1 0 1 0 0 0;\r\n      0 0 0 2 1 0 0 0;\r\n      0 0 0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(blockx3_2(x),y_correct));\r\n\r\n\r\n%%\r\nx =  [0 0 0 0 0 0 0 0;\r\n      0 0 1 0 1 0 0 0;\r\n      0 0 0 2 0 0 0 0;\r\n      0 0 1 0 0 0 0 0];\r\ny_correct = 3;\r\nassert(isequal(blockx3_2(x),y_correct))\r\n\r\n%%\r\nx =  [1 1 0 0 0 0 0 0;\r\n      1 2 0 0 0 0 0 0;\r\n      0 0 0 0 0 0 0 0;\r\n      0 0 0 0 0 0 0 0];\r\ny_correct = 3;\r\nassert(isequal(blockx3_2(x),y_correct))\r\n\r\n%%\r\nx =  [0 0 0 1 0 0 0 0;\r\n      0 0 1 2 1 0 0 0;\r\n      0 0 0 0 0 0 0 0;\r\n      0 0 0 0 0 0 0 0];\r\ny_correct = 2;\r\nassert(isequal(blockx3_2(x),y_correct))\r\n\r\n\r\n%%\r\nx =  [0 0 0 1 0 0 0 0;\r\n      0 0 1 2 0 0 0 0;\r\n      0 0 1 0 0 0 0 0;\r\n      0 0 0 0 0 0 0 0]\r\ny_correct = 1;\r\nassert(isequal(blockx3_2(x),y_correct))\r\n\r\n\r\n%%\r\nx =  [0 0 0 1 0 0 0 0;\r\n      0 0 1 2 0 0 0 0;\r\n      0 0 0 0 0 0 0 1;\r\n      0 0 0 0 0 0 0 0]\r\ny_correct = 6;\r\nassert(isequal(blockx3_2(x),y_correct))\r\n\r\n\r\n%%\r\nx =  [0 0 0 1 0 0 0 0;\r\n      0 0 0 2 0 0 0 0;\r\n      0 0 0 1 0 0 0 0;\r\n      0 0 0 1 0 0 0 0]\r\ny_correct = 4;\r\nassert(isequal(blockx3_2(x),y_correct))\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":17203,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":"2014-08-03T08:43:51.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2014-08-03T08:31:00.000Z","updated_at":"2026-05-25T00:44:08.000Z","published_at":"2014-08-03T08:31:00.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAn extension to problem 2451 (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/2451-block-x3-version-1\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://www.mathworks.com/matlabcentral/cody/problems/2451-block-x3-version-1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt; ).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIt is based on an android game - BLOCK x3. The objective is to align the 1's in the matrix using minimum movements. The valid movements are horizontal and vertical (by one step). A zero (0) indicates an empty space. Your task is to count minimum number of movements required to align the three 1's vertically or horizontally.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNew challenge in this problem : There are some static elements which can not be moved and can not be included in the alignment. These elements are indicated by 2. This makes the problem a bit challenging.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[Input =[0 0 0 0 0 0 0 0;\\n        0 0 1 0 1 0 0 0;\\n        0 0 0 2 1 0 0 0;\\n        0 0 0 0 0 0 0 0]\\n\\nOutput = 2;]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":56538,"title":"Cricket - Report the Result (Part II: Test Matches)","description":"Given two scalar strings representing the scores for a test match, return a string reporting the result in the appropriate form:\r\n\"Match drawn\"\r\n\"TeamName won by n runs\"\r\n\"TeamName won by n wickets\"\r\n\"TeamName won by an innings and n runs\"\r\nThe strings will be given in the order the teams batted and will have the form \"TeamName r/w \u0026 r/w\" (where r is runs and w is wickets). If a team is all out, their score will be given as just \"r\" (rather than \"r/10\"). The convention \"d\" is used for declared innings (eg \"432/1d\") and \"(f/o)\" for following on (eg \"England 123 \u0026 234 (f/o)\").\r\nGiven that tied Tests are very rare (and drawn Tests with the scores level equally rare), you do not need to worry about scores being level. You can also be grammatically incorrect for a win by 1 wicket or 1 run (ie you can report the result as \"... won by 1 wickets\", rather than making a special case for \"1 wicket\"). Other than that, your function will need to cope with all other possibilities, such as \"West Indies 123 \u0026 456/7d (f/o)\".\r\nThe strings will always use single spaces between the team name and the scores, but team names may be multiple words (\"Sri Lanka\", \"South Africa\", etc) which will also be separated by single spaces. The two innings scores will be separated by \"\u0026\".\r\nFor a match to have a result (not a draw), the losing team must have completed two innings with their total score still below the winning team’s score. If the winning team is batting last, their margin of victory will be in terms of wickets. Otherwise, it will be in terms of runs. If they batted only one innings, their victory will be given as an innings and runs. All possible permutations of how this can occur are summarized in the table below.\r\n\r\n\r\ns1 = \"India 250 \u0026 307\";\r\ns2 = \"Australia 235 \u0026 291\";\r\nres = reportresult(s1,s2)\r\nres = \r\n    \"India won by 31 runs\"\r\n\r\n\r\ns1 = \"South Africa 573/4d\";\r\ns2 = \"Bangladesh 147 \u0026 172 (f/o)\";\r\nres = reportresult(s1,s2)\r\nres = \r\n    \"South Africa won by an innings and 254 runs\"\r\n    \r\nTest matches are played over a fixed time period. If the match is not completed in that time, the result is a draw (regardless of the score).\r\nTo avoid running out of time (thus resulting in a draw), a team can choose to declare their innings completed before they are all out. This is effectively a gamble to forfeit the remainder of their innings for the sake of time.\r\nAfter both teams' first innings, if Team B is sufficiently far (200+ runs) behind Team A, A can choose to make B follow-on, meaning Team B has to bat again immediately, while Team A can keep their second innings in reserve if needed. Hence, the order of innings is A, B, B, A (if necessary), instead of the usual A, B, A, B.\r\nWith these rules, you can always figure out the order of play, and the result, given the team scores. For example:\r\nTeam A 543/6d \u0026 123/4\r\nTeam B 234 \u0026 456/7d (f/o)\r\nTeam A scores 543 and declares with 4 wickets still in hand\r\nTeam B scores 234 (all out)\r\nTeam B, forced to follow-on (309 runs behind), scores 456 and declares with 3 wickets in hand, giving A a target of 148 to win\r\nTeam A scores 123 for the loss of 4 wickets, leaving them 24 runs behind Team B's total\r\nResult: match drawn, because the team with the lower score (A) had not yet completed their second innings.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 1713.07px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 856.533px; transform-origin: 407px 856.533px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 373.5px 8px; transform-origin: 373.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven two scalar strings representing the scores for a test match, return a string reporting the result in the appropriate form:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 81.7333px; counter-reset: list-item 0; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 40.8667px; transform-origin: 391px 40.8667px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 45px 8px; transform-origin: 45px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\"Match drawn\"\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 2.5px 8px; transform-origin: 2.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 36px 8px; transform-origin: 36px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eTeamName\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 26px 8px; transform-origin: 26px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e won by \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 18.5px 8px; transform-origin: 18.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e runs\"\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 2.5px 8px; transform-origin: 2.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 36px 8px; transform-origin: 36px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eTeamName\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 26px 8px; transform-origin: 26px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e won by \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 27px 8px; transform-origin: 27px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e wickets\"\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 2.5px 8px; transform-origin: 2.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 36px 8px; transform-origin: 36px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eTeamName\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 74.5px 8px; transform-origin: 74.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e won by an innings and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 4px 8px; transform-origin: 4px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003en\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 18.5px 8px; transform-origin: 18.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e runs\"\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 244px 8px; transform-origin: 244px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe strings will be given in the order the teams batted and will have the form \"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 36px 8px; transform-origin: 36px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eTeamName\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2.5px 8px; transform-origin: 2.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003er\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e/\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4.5px 8px; transform-origin: 4.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ew\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 8.5px 8px; transform-origin: 8.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u0026amp; \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2.5px 8px; transform-origin: 2.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003er\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e/\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4.5px 8px; transform-origin: 4.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ew\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 28px 8px; transform-origin: 28px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\" (where \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2.5px 8px; transform-origin: 2.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003er\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 39px 8px; transform-origin: 39px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is runs and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4.5px 8px; transform-origin: 4.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003ew\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.5px 8px; transform-origin: 1.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is wickets). If a team is all out, their score will be given as just \"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2.5px 8px; transform-origin: 2.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003er\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 46.5px 8px; transform-origin: 46.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\" (rather than \"\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2.5px 8px; transform-origin: 2.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003er\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 116px 8px; transform-origin: 116px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e/10\"). The convention \"d\" is used for declared innings (eg \"432/1d\") and \"(f/o)\" for following on (eg \"England 123 \u0026amp; 234 (f/o)\").\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 375.5px 8px; transform-origin: 375.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven that tied Tests are very rare (and drawn Tests with the scores level equally rare), you do not need to worry about scores being level. You can also be grammatically incorrect for a win by 1 wicket or 1 run (ie you can report the result as \"... won by 1 wickets\", rather than making a special case for \"1 wicket\"). Other than that, your function will need to cope with all other possibilities, such as \"West Indies 123 \u0026amp; 456/7d (f/o)\".\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 382.5px 8px; transform-origin: 382.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe strings will always use single spaces between the team name and the scores, but team names may be multiple words (\"Sri Lanka\", \"South Africa\", etc) which will also be separated by single spaces. The two innings scores will be separated by \"\u0026amp;\".\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 384px 8px; transform-origin: 384px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor a match to have a result (not a draw), the losing team must have completed two innings with their total score still below the winning team’s score. If the winning team is batting last, their margin of victory will be in terms of wickets. Otherwise, it will be in terms of runs. If they batted only one innings, their victory will be given as an innings and runs. All possible permutations of how this can occur are summarized in the table below.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 516.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 258.25px; text-align: left; transform-origin: 384px 258.25px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 586px;height: 511px\" 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\" alt=\"Table shows 6 permutations of innings and results. (1) Team A bats first and third, Team B bats second and fourth. Team A wins. The margin is given as \u0026quot;n runs\u0026quot;. (2) Team A bats first and third, Team B bats second and fourth. Team B wins (with the fourth innings still in progress). The margin is given as \u0026quot;n wickets\u0026quot;. (3) Team A bats first and fourth, Team B bats second and third (following on). Team A wins (with the fourth innings still in progress). The margin is given as \u0026quot;n wickets\u0026quot;. (4) Team A bats first and fourth, Team B bats second and third (following on). Team B wins. The margin is given as \u0026quot;n runs\u0026quot;. (5) Team A bats first and third, Team B bats second (only one innings). Team B wins. The margin is given as \u0026quot;an innings and n runs\u0026quot;. (6) Team A bats first (only one innings), Team B bats second and third (following on). Team A wins. The margin is given as \u0026quot;an innings and n runs\u0026quot;.\" data-image-state=\"image-loaded\" width=\"586\" height=\"511\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 8px; transform-origin: 0px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 265.633px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 132.817px; transform-origin: 404px 132.817px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 92px 8.5px; tab-size: 4; transform-origin: 92px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 20px 8.5px; transform-origin: 20px 8.5px; \"\u003es1 = \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 68px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 68px 8.5px; \"\u003e\"India 250 \u0026amp; 307\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 108px 8.5px; tab-size: 4; transform-origin: 108px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 20px 8.5px; transform-origin: 20px 8.5px; \"\u003es2 = \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 84px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 84px 8.5px; \"\u003e\"Australia 235 \u0026amp; 291\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100px 8.5px; tab-size: 4; transform-origin: 100px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eres = reportresult(s1,s2)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 24px 8.5px; tab-size: 4; transform-origin: 24px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eres = \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 104px 8.5px; tab-size: 4; transform-origin: 104px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 16px 8.5px; transform-origin: 16px 8.5px; \"\u003e    \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 88px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 88px 8.5px; \"\u003e\"India won by 31 runs\"\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 108px 8.5px; tab-size: 4; transform-origin: 108px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 20px 8.5px; transform-origin: 20px 8.5px; \"\u003es1 = \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 84px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 84px 8.5px; \"\u003e\"South Africa 573/4d\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 136px 8.5px; tab-size: 4; transform-origin: 136px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 20px 8.5px; transform-origin: 20px 8.5px; \"\u003es2 = \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 112px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 112px 8.5px; \"\u003e\"Bangladesh 147 \u0026amp; 172 (f/o)\"\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 100px 8.5px; tab-size: 4; transform-origin: 100px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eres = reportresult(s1,s2)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 24px 8.5px; tab-size: 4; transform-origin: 24px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eres = \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 196px 8.5px; tab-size: 4; transform-origin: 196px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 16px 8.5px; transform-origin: 16px 8.5px; \"\u003e    \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 180px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 180px 8.5px; \"\u003e\"South Africa won by an innings and 254 runs\"\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 16px 8.5px; tab-size: 4; transform-origin: 16px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e    \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cul style=\"block-size: 143.033px; counter-reset: list-item 0; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 71.5167px; transform-origin: 391px 71.5167px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 40.8667px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 20.4333px; text-align: left; transform-origin: 363px 20.4333px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 331.5px 8px; transform-origin: 331.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTest matches are played over a fixed time period. If the match is not completed in that time, the result is a \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 15px 8px; transform-origin: 15px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003edraw\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e (regardless of the score).\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 40.8667px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 20.4333px; text-align: left; transform-origin: 363px 20.4333px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 241.5px 8px; transform-origin: 241.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTo avoid running out of time (thus resulting in a draw), a team can choose to \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 23.5px 8px; transform-origin: 23.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003edeclare\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 98px 8px; transform-origin: 98px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e their innings completed before they are all out. This is effectively a gamble to forfeit the remainder of their innings for the sake of time.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 61.3px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 30.65px; text-align: left; transform-origin: 363px 30.65px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 347.5px 8px; transform-origin: 347.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAfter both teams' first innings, if Team B is sufficiently far (200+ runs) behind Team A, A can choose to make B \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 28px 8px; transform-origin: 28px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003efollow-on\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 330.5px 8px; transform-origin: 330.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, meaning Team B has to bat again immediately, while Team A can keep their second innings in reserve if needed. Hence, the order of innings is A, B, B, A (if necessary), instead of the usual A, B, A, B.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 354.5px 8px; transform-origin: 354.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWith these rules, you can always figure out the order of play, and the result, given the team scores. For example:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 74px 8px; transform-origin: 74px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTeam A 543/6d \u0026amp; 123/4\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 83px 8px; transform-origin: 83px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTeam B 234 \u0026amp; 456/7d (f/o)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003col style=\"block-size: 102.167px; counter-reset: list-item 0; font-family: Helvetica, Arial, sans-serif; list-style-type: decimal; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 51.0833px; transform-origin: 391px 51.0833px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 185.5px 8px; transform-origin: 185.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTeam A scores 543 and declares with 4 wickets still in hand\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 86.5px 8px; transform-origin: 86.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTeam B scores 234 (all out)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 40.8667px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 20.4333px; text-align: left; transform-origin: 363px 20.4333px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 361.5px 8px; transform-origin: 361.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTeam B, forced to follow-on (309 runs behind), scores 456 and declares with 3 wickets in hand, giving A a target of 148 to win\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 275.5px 8px; transform-origin: 275.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTeam A scores 123 for the loss of 4 wickets, leaving them 24 runs behind Team B's total\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 339.5px 8px; transform-origin: 339.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eResult: match drawn, because the team with the lower score (A) had not yet completed their second innings.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function res = reportresult(s1,s2)\r\nres = s1+s2;\r\nend","test_suite":"%% Draw, 4th innings in progress\r\ns1 = \"Sri Lanka 253 \u0026 342\";\r\ns2 = \"West Indies 300 \u0026 147/5\";\r\nres = reportresult(s1,s2);\r\nassert( isequal(res,\"Match drawn\") )\r\n%% Draw, team 2 following on\r\ns1 = \"India 622/7d\";\r\ns2 = \"Australia 300 \u0026 6/0 (f/o)\";\r\nres = reportresult(s1,s2);\r\nassert( isequal(res,\"Match drawn\") )\r\n%% Draw, 3rd innings in progress\r\ns1 = \"Sri Lanka 282 \u0026 287/3\";\r\ns2 = \"New Zealand 578\";\r\nres = reportresult(s1,s2);\r\nassert( isequal(res,\"Match drawn\") )\r\n%% Draw, only 2 innings (yawn)\r\ns1 = \"India 537/8d\";\r\ns2 = \"Sri Lanka 952/6d\";\r\nres = reportresult(s1,s2);\r\nassert( isequal(res,\"Match drawn\") )\r\n%% Four completed innings, Team 1 wins (#1 in table)\r\ns1 = \"India 250 \u0026 307\";\r\ns2 = \"Australia 235 \u0026 291\";\r\nres = reportresult(s1,s2);\r\nassert( isequal(res,\"India won by 31 runs\") )\r\n%% Four completed innings, Team 1 wins by a single run (#1 in table)\r\ns1 = \"West Indies 252 \u0026 146\";\r\ns2 = \"Australia 213 \u0026 184\";\r\nres = reportresult(s1,s2);\r\nassert( startsWith(res,\"West Indies won by 1 run\") )\r\n%% Team 1 declared (#1 in table)\r\ns1 = \"New Zealand 178 \u0026 585/4d\";\r\ns2 = \"Sri Lanka 104 \u0026 236\";\r\nres = reportresult(s1,s2);\r\nassert( isequal(res,\"New Zealand won by 423 runs\") )\r\n%% Four innings in order, Team 2 wins (#2 in table)\r\ns1 = \"Pakistan 181 \u0026 190\";\r\ns2 = \"South Africa 223 \u0026 151/4\";\r\nres = reportresult(s1,s2);\r\nassert( isequal(res,\"South Africa won by 6 wickets\") )\r\n%% Four innings in order, Team 2 wins by a single wicket (#2 in table)\r\ns1 = \"Pakistan 269 \u0026 219\";\r\ns2 = \"West Indies 273 \u0026 216/9\";\r\nres = reportresult(s1,s2);\r\nassert( startsWith(res,\"West Indies won by 1 wicket\") )\r\n%% Team 1 wins after Team 2 follows on (#3 in table)\r\ns1 = \"Pakistan 310/9d \u0026 160/5\";\r\ns2 = \"Ireland 130 \u0026 339 (f/o)\";\r\nres = reportresult(s1,s2);\r\nassert( isequal(res,\"Pakistan won by 5 wickets\") )\r\n%% Team 2 wins after following on (#4 in table)\r\ns1 = \"Australia 401/9d \u0026 111\";\r\ns2 = \"England 174 \u0026 356 (f/o)\";\r\nres = reportresult(s1,s2);\r\nassert( isequal(res,\"England won by 18 runs\") )\r\n%% Team 2 wins after following on and declaring! (#4 in table)\r\ns1 = \"Australia 445 \u0026 212\";\r\ns2 = \"India 171 \u0026 657/7d (f/o)\";\r\nres = reportresult(s1,s2);\r\nassert( isequal(res,\"India won by 171 runs\") )\r\n%% Innings victory for Team 2 (#5 in table)\r\ns1 = \"Sri Lanka 144 \u0026 139\";\r\ns2 = \"Australia 323\";\r\nres = reportresult(s1,s2);\r\nassert( isequal(res,\"Australia won by an innings and 40 runs\") )\r\n%% Innings victory for Team 2 with declaration (#5 in table)\r\ns1 = \"Bangladesh 211 \u0026 209\";\r\ns2 = \"New Zealand 432/6d\";\r\nres = reportresult(s1,s2);\r\nassert( isequal(res,\"New Zealand won by an innings and 12 runs\") )\r\n%% Innings victory for Team 1 (#6 in table)\r\ns1 = \"India 474\";\r\ns2 = \"Afghanistan 109 \u0026 103 (f/o)\";\r\nres = reportresult(s1,s2);\r\nassert( isequal(res,\"India won by an innings and 262 runs\") )","published":true,"deleted":false,"likes_count":1,"comments_count":16,"created_by":287,"edited_by":287,"edited_at":"2022-11-13T04:10:24.000Z","deleted_by":null,"deleted_at":null,"solvers_count":14,"test_suite_updated_at":"2022-11-13T04:10:24.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2022-11-07T20:56:16.000Z","updated_at":"2026-05-24T22:31:43.000Z","published_at":"2022-11-08T15:17:22.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven two scalar strings representing the scores for a test match, return a string reporting the result in the appropriate form:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\"Match drawn\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eTeamName\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e won by \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e runs\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eTeamName\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e won by \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e wickets\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eTeamName\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e won by an innings and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e runs\\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe strings will be given in the order the teams batted and will have the form \\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eTeamName\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003er\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e/\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ew\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u0026amp; \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003er\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e/\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ew\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e\\\" (where \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003er\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is runs and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ew\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is wickets). If a team is all out, their score will be given as just \\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003er\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e\\\" (rather than \\\"\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003er\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e/10\\\"). The convention \\\"d\\\" is used for declared innings (eg \\\"432/1d\\\") and \\\"(f/o)\\\" for following on (eg \\\"England 123 \u0026amp; 234 (f/o)\\\").\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven that tied Tests are very rare (and drawn Tests with the scores level equally rare), you do not need to worry about scores being level. You can also be grammatically incorrect for a win by 1 wicket or 1 run (ie you can report the result as \\\"... won by 1 wickets\\\", rather than making a special case for \\\"1 wicket\\\"). Other than that, your function will need to cope with all other possibilities, such as \\\"West Indies 123 \u0026amp; 456/7d (f/o)\\\".\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe strings will always use single spaces between the team name and the scores, but team names may be multiple words (\\\"Sri Lanka\\\", \\\"South Africa\\\", etc) which will also be separated by single spaces. The two innings scores will be separated by \\\"\u0026amp;\\\".\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor a match to have a result (not a draw), the losing team must have completed two innings with their total score still below the winning team’s score. If the winning team is batting last, their margin of victory will be in terms of wickets. Otherwise, it will be in terms of runs. If they batted only one innings, their victory will be given as an innings and runs. All possible permutations of how this can occur are summarized in the table below.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"511\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"586\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"Table shows 6 permutations of innings and results. (1) Team A bats first and third, Team B bats second and fourth. Team A wins. The margin is given as \u0026quot;n runs\u0026quot;. (2) Team A bats first and third, Team B bats second and fourth. Team B wins (with the fourth innings still in progress). The margin is given as \u0026quot;n wickets\u0026quot;. (3) Team A bats first and fourth, Team B bats second and third (following on). Team A wins (with the fourth innings still in progress). The margin is given as \u0026quot;n wickets\u0026quot;. (4) Team A bats first and fourth, Team B bats second and third (following on). Team B wins. The margin is given as \u0026quot;n runs\u0026quot;. (5) Team A bats first and third, Team B bats second (only one innings). Team B wins. The margin is given as \u0026quot;an innings and n runs\u0026quot;. (6) Team A bats first (only one innings), Team B bats second and third (following on). Team A wins. The margin is given as \u0026quot;an innings and n runs\u0026quot;.\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[s1 = \\\"India 250 \u0026 307\\\";\\ns2 = \\\"Australia 235 \u0026 291\\\";\\nres = reportresult(s1,s2)\\nres = \\n    \\\"India won by 31 runs\\\"\\n\\n\\ns1 = \\\"South Africa 573/4d\\\";\\ns2 = \\\"Bangladesh 147 \u0026 172 (f/o)\\\";\\nres = reportresult(s1,s2)\\nres = \\n    \\\"South Africa won by an innings and 254 runs\\\"\\n    ]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTest matches are played over a fixed time period. If the match is not completed in that time, the result is a \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edraw\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (regardless of the score).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo avoid running out of time (thus resulting in a draw), a team can choose to \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003edeclare\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e their innings completed before they are all out. This is effectively a gamble to forfeit the remainder of their innings for the sake of time.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAfter both teams' first innings, if Team B is sufficiently far (200+ runs) behind Team A, A can choose to make B \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003efollow-on\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, meaning Team B has to bat again immediately, while Team A can keep their second innings in reserve if needed. Hence, the order of innings is A, B, B, A (if necessary), instead of the usual A, B, A, B.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWith these rules, you can always figure out the order of play, and the result, given the team scores. For example:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTeam A 543/6d \u0026amp; 123/4\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTeam B 234 \u0026amp; 456/7d (f/o)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTeam A scores 543 and declares with 4 wickets still in hand\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTeam B scores 234 (all out)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTeam B, forced to follow-on (309 runs behind), scores 456 and declares with 3 wickets in hand, giving A a target of 148 to win\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTeam A scores 123 for the loss of 4 wickets, leaving them 24 runs behind Team B's total\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eResult: match drawn, because the team with the lower score (A) had not yet completed their second innings.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/image\",\"target\":\"/media/image1.png\",\"relationshipId\":\"rId1\"}]},{\"partUri\":\"/media/image1.png\",\"contentType\":\"image/png\",\"content\":\"data:image/png;base64,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from bearing angles in 2D","description":"A robot moving on the plane has a sensor that measures the bearing angle to two mapped landmarks, that is, the world frame coordinates of the landmarks are known and represented by position vectors |P1| and |P2| .  The bearing angles to the landmarks, with respect to the x-axis of the robot's coordinate frame are |th1| and |th2| respectively.  The robot's forward direction is parallel to its x-axis and its heading angle, with respect to magnetic north is |thb| .\r\n\r\nDetermine the pose of the robot expressed as a homogeneous transformation matrix with respect to the world coordinate frame.  In surveying this is known as a resection problem.","description_html":"\u003cp\u003eA robot moving on the plane has a sensor that measures the bearing angle to two mapped landmarks, that is, the world frame coordinates of the landmarks are known and represented by position vectors \u003ctt\u003eP1\u003c/tt\u003e and \u003ctt\u003eP2\u003c/tt\u003e .  The bearing angles to the landmarks, with respect to the x-axis of the robot's coordinate frame are \u003ctt\u003eth1\u003c/tt\u003e and \u003ctt\u003eth2\u003c/tt\u003e respectively.  The robot's forward direction is parallel to its x-axis and its heading angle, with respect to magnetic north is \u003ctt\u003ethb\u003c/tt\u003e .\u003c/p\u003e\u003cp\u003eDetermine the pose of the robot expressed as a homogeneous transformation matrix with respect to the world coordinate frame.  In surveying this is known as a resection problem.\u003c/p\u003e","function_template":"function T = user_function(P1, P2, th1, th2, thb)\r\n% Input:  P1 a 2x1 vector representing the coordinate of a point\r\n%         P2 a 2x1 vector representing the coordinate of a point\r\n%         th1 bearing, a scalar angle\r\n%         th2 bearing, a scalar angle\r\n%         thb heading, a scalar angle\r\n% Output: T a 3x3 homogeneous transformation matrix\r\n  T = ;\r\nend","test_suite":"%\r\nP1 = [10 20]';\r\nP2 = [20 20]';\r\nP = rand(2,1)*5 + [5 5]';\r\nthb = rand*0.2;\r\nx = P1 - P;\r\nth1 = atan2(x(2), x(1)) - thb;\r\nx = P2 - P;\r\nth2 = atan2(x(2), x(1)) - thb;\r\n\r\nT = user_function(P1, P2, th1, th2, thb);\r\n\r\n%% test size and complexity\r\nassert(all(size(T)==3), 'The matrix must be 3x3');\r\nassert(isreal(T), 'The matrix must be real, not complex');\r\n\r\n%% bottom row\r\nassert(isequal(T(3,:), [0 0 1]), 'The bottom row of the homogeneous transformation matrix is not correct')\r\n\r\n%% x coordinate\r\nassert(abs(T(1,3)-P(1))\u003c1e-4, 'The representation of the x-coordinate is not correct')\r\n\r\n%% y coordinate\r\nassert(abs(T(2,3)-P(2))\u003c1e-4, 'The representation of the y-coordinate is not correct')\r\n\r\n%% valid rotation matrix\r\nR = T(1:2,1:2);\r\nassert( abs(det(R)-1) \u003c 1e-4, 'The determinant of the rotation submatrix is not correct')\r\n\r\n%% correct rotation matrix\r\nR = T(1:2,1:2);\r\nassert( abs(atan2(R(2,1), R(1,1)) - thb) \u003c 1e-4, 'The rotation matrix is not correct, check your calculation of the heading SSW and whether you are using radians or degrees')\r\n\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":13332,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":22,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":77,"created_at":"2019-01-21T00:32:56.000Z","updated_at":"2026-05-24T23:32:59.000Z","published_at":"2019-01-21T00:37:35.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA robot moving on the plane has a sensor that measures the bearing angle to two mapped landmarks, that is, the world frame coordinates of the landmarks are known and represented by position vectors\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eP1\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eP2\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e . The bearing angles to the landmarks, with respect to the x-axis of the robot's coordinate frame are\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eth1\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eth2\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e respectively. The robot's forward direction is parallel to its x-axis and its heading angle, with respect to magnetic north is\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ethb\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e .\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDetermine the pose of the robot expressed as a homogeneous transformation matrix with respect to the world coordinate frame. In surveying this is known as a resection problem.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":60461,"title":"Generate a point cloud on an equilateral triangle 2","description":"The input is the iteration parameter H.\r\nThe output is a point cloud W involving N points.\r\nW is N uniformly distributed points on an equilateral triangle.\r\nThe side length of an equilateral triangle is 2.\r\nThe relationship between H and N is as follows:\r\nH = [1 2 3 4 5 6 7 8 9];\r\nN = [4 10 19 31 46 64 85 109 136];\r\nThe results for cases where H is 1 to 6 are as follows.\r\n\r\n\r\nEx)\r\n[W,N] = lattice2(H=1) -\u003e W = [0 0; 1 sqrt(3)/3; 1 sqrt(3); 2 0]; N = 4;\r\n[W,N] = lattice2(H=2) -\u003e W = [0 0; 0.5 sqrt(3)/6; 0.5 sqrt(3)/2; 1 0; 1 sqrt(3)/3; 1 sqrt(4/3); 1 sqrt(3); 1.5 sqrt(3)/6; 1.5 sqrt(3)/2; 2 0]; N = 10;","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 749.188px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 374.594px; transform-origin: 407px 374.594px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe input is the iteration parameter\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eH\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe output is a point cloud\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eW\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003einvolving\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003epoints.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eW\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eis\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003eN\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003euniformly distributed points on an equilateral triangle.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe side length of an equilateral triangle is 2.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe relationship between H and N is as follows:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 40.875px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 20.4375px; transform-origin: 391px 20.4375px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2188px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eH = [1 2 3 4 5 6 7 8 9];\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2188px; text-align: left; transform-origin: 363px 10.2188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eN = [4 10 19 31 46 64 85 109 136];\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe results for cases where H is 1 to 6 are as follows.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 197px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 98.5px; text-align: left; transform-origin: 384px 98.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" width=\"193\" height=\"191\" style=\"vertical-align: baseline;width: 193px;height: 191px\" 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\" 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\" 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\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eEx)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 64.3125px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 32.1562px; transform-origin: 391px 32.1562px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 21.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.7188px; text-align: left; transform-origin: 363px 10.7188px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e[W,N] = lattice2(H=1) -\u0026gt; W = [\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003e0 0; 1 sqrt(3)/3; 1 sqrt(3); 2 0]\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e; N = 4;\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 42.875px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 21.4375px; text-align: left; transform-origin: 363px 21.4375px; white-space-collapse: preserve; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e[W,N] = lattice2(H=2) -\u0026gt; W = [\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003e0 0; 0.5 sqrt(3)/6; 0.5 \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003esqrt(3)/2; 1 0; 1 \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003esqrt(3)/3; 1 sqrt(4/3); 1 sqrt(3); 1.5 sqrt(3)/6; 1.5 \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003esqrt(3)/2; 2 0]; N = 10;\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [W,N] = lattice2(H)\r\n  W = [0 0; 1 sqrt(3); 2 0];\r\n  N = size(W,1);\r\nend","test_suite":"%%\r\nH = 1;\r\nA = [0 0; 1 sqrt(3)/3; 1 sqrt(3); 2 0];\r\n\r\n[W,N] = lattice2(H);\r\nassert(isequal(N,4))\r\nassert(sum(abs(sortrows(W)-sortrows(A)),'all') \u003c 1e-6)\r\n\r\n%%\r\nH = 2;\r\nA = [0 0; 0.5 sqrt(3)/6; 0.5 sqrt(3)/2; 1 0; 1 sqrt(3)/3;\r\n    1 sqrt(4/3); 1 sqrt(3); 1.5 sqrt(3)/6; 1.5 sqrt(3)/2; 2 0];\r\n\r\n[W,N] = lattice2(H);\r\nassert(isequal(N,10))\r\nassert(sum(abs(sortrows(W)-sortrows(A)),'all') \u003c 1e-6)\r\n\r\n%%\r\nH = 3;\r\n[~,N] = lattice2(H);\r\nassert(isequal(N,19))\r\n\r\n%%\r\nH = 4;\r\n[~,N] = lattice2(H);\r\nassert(isequal(N,31))\r\n\r\n%%\r\nH = 5;\r\n[~,N] = lattice2(H);\r\nassert(isequal(N,46))\r\n\r\n%%\r\nH = 10;\r\n[~,N] = lattice2(H);\r\nassert(isequal(N,166))\r\n\r\n%%\r\nH = 100;\r\n[~,N] = lattice2(H);\r\nassert(isequal(N,15151))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":2217275,"edited_by":2217275,"edited_at":"2024-06-09T21:16:00.000Z","deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":"2024-06-09T21:08:07.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2024-06-09T14:14:29.000Z","updated_at":"2026-05-24T21:53:50.000Z","published_at":"2024-06-09T14:24:00.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe input is the iteration parameter\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eH\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe output is a point cloud\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eW\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003einvolving\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003epoints.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eW\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003eis\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eN\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003euniformly distributed points on an equilateral triangle.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe side length of an equilateral triangle is 2.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe relationship between H and N is as follows:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eH = [1 2 3 4 5 6 7 8 9];\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eN = [4 10 19 31 46 64 85 109 136];\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe results for cases where H is 1 to 6 are as follows.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"191\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"193\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"191\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"193\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId2\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"191\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"193\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId3\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"191\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"193\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId4\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"191\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"193\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId5\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"191\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"193\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId6\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eEx)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[W,N] = lattice2(H=1) -\u0026gt; W = [\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e0 0; 1 sqrt(3)/3; 1 sqrt(3); 2 0]\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e; N = 4;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[W,N] = lattice2(H=2) -\u0026gt; W = [\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003e0 0; 0.5 sqrt(3)/6; 0.5 \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003esqrt(3)/2; 1 0; 1 \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003esqrt(3)/3; 1 sqrt(4/3); 1 sqrt(3); 1.5 sqrt(3)/6; 1.5 \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003esqrt(3)/2; 2 0]; N = 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":56448,"title":"nth permutation of 11...100...0","description":"Given some number of ones and zeros, numOnes and numZeros respectively, find the nth permutation of the vector [ones(1,numOnes), zeros(1,numZeros)] according to the lexicographical order.  For example, if numOnes = 3 and numZeros = 2 then there are nchoosek(5,3) = 10 permutations of 11100.  The lexicographic numbering is below\r\n1     11100\r\n2     11010\r\n3     11001\r\n4     10110\r\n5     10101\r\n6     10011\r\n7     01110\r\n8     01101\r\n9     01011\r\n10   00111\r\nso for instance nthPerm(3,2,8) = [0,1,1,0,1].  You can assume that numOnes and numZeros will always be greater than or equal to 1.  Lastly, your code should not enumerate all possibilities since one of the test cases contains billions of permutations.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 435px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 217.5px; transform-origin: 407px 217.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eGiven some number of ones and zeros, numOnes and numZeros respectively, find the nth permutation of the vector [ones(1,numOnes), zeros(1,numZeros)] according to the lexicographical order.  For example, if numOnes = 3 and numZeros = 2 then there are nchoosek(5,3) = 10 permutations of 11100.  The lexicographic numbering is below\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e1     11100\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e2     11010\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e3     11001\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e4     10110\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e5     10101\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e6     10011\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e7     01110\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e8     01101\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e9     01011\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e10   00111\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eso for instance nthPerm(3,2,8) = [0,1,1,0,1].  You can assume that numOnes and numZeros will always be greater than or equal to 1.  Lastly, your code should not enumerate all possibilities since one of the test cases contains billions of permutations.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = nthPerm(numOnes,numZeros,n)\r\n  y = [];\r\nend","test_suite":"%%\r\nnumOnes = 1;\r\nnumZeros = 1;\r\nn = 1;\r\ny_correct = [1,0];\r\nassert(isequal(nthPerm(numOnes,numZeros,n),y_correct))\r\n\r\n%%\r\nnumOnes = 3;\r\nnumZeros = 2;\r\nn = 4;\r\ny_correct = [1,0,1,1,0];\r\nassert(isequal(nthPerm(numOnes,numZeros,n),y_correct))\r\n\r\n%%\r\nnumOnes = 10;\r\nnumZeros = 1;\r\nn = 11;\r\ny_correct = [0,1,1,1,1,1,1,1,1,1,1];\r\nassert(isequal(nthPerm(numOnes,numZeros,n),y_correct))\r\n\r\n%%\r\nnumOnes = 18;\r\nnumZeros = 7;\r\nn = 408913;\r\ny_correct = [0,1,1,0,1,1,1,0,0,0,1,0,1,1,1,1,1,1,1,1,1,0,1,1,1];\r\nassert(isequal(nthPerm(numOnes,numZeros,n),y_correct))\r\n\r\n%%\r\nnumOnes = 17;\r\nnumZeros = 23;\r\nn = 40207127;\r\ny_correct = [1,1,1,1,1,1,1,0,1,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,1,1,0,1,0,0,0,1];\r\nassert(isequal(nthPerm(numOnes,numZeros,n),y_correct))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":2198965,"edited_by":2198965,"edited_at":"2022-11-04T08:56:28.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-11-03T23:19:23.000Z","updated_at":"2026-06-03T00:30:12.000Z","published_at":"2022-11-03T23:52:22.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven some number of ones and zeros, numOnes and numZeros respectively, find the nth permutation of the vector [ones(1,numOnes), zeros(1,numZeros)] according to the lexicographical order.  For example, if numOnes = 3 and numZeros = 2 then there are nchoosek(5,3) = 10 permutations of 11100.  The lexicographic numbering is below\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e1     11100\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e2     11010\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e3     11001\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e4     10110\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e5     10101\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e6     10011\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e7     01110\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e8     01101\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e9     01011\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e10   00111\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eso for instance nthPerm(3,2,8) = [0,1,1,0,1].  You can assume that numOnes and numZeros will always be greater than or equal to 1.  Lastly, your code should not enumerate all possibilities since one of the test cases contains billions of permutations.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":60227,"title":"Rotate matrix 60 degrees","description":"Given a 2*m-1 by 4*m-3 matrix in which certain elements form a hexagon of side length m, rotate the hexagon 60 degrees counterclockwise, as shown below for m=3:\r\nA=[\r\n'--A-A-A--'\r\n'-B-B-B-B-'\r\n'C-C-C-C-C'\r\n'-D-D-D-D-'\r\n'--E-E-E--']\r\n\r\nrot60(A)=[\r\n'--A-B-C--'\r\n'-A-B-C-D-'\r\n'A-B-C-D-E'\r\n'-B-C-D-E-'\r\n'--C-D-E--']\r\nA=rot60(A,n) should rotate the hexagon 60*n degrees counterclockwise (and thus clockwise if n\u003c0).","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 348.75px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 331.989px 174.375px; transform-origin: 331.996px 174.375px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42.017px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 308.991px 21.0085px; text-align: left; transform-origin: 308.999px 21.0085px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven a 2*m-1 by 4*m-3 matrix in which certain elements form a hexagon of side length m, rotate the hexagon 60 degrees counterclockwise, as shown below for m=3:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 265.724px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 328.991px 132.855px; transform-origin: 328.999px 132.862px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 328.991px 10.2131px; text-wrap: nowrap; transform-origin: 328.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eA=[\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 328.991px 10.2131px; text-wrap: nowrap; transform-origin: 328.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'--A-A-A--'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 328.991px 10.2131px; text-wrap: nowrap; transform-origin: 328.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'-B-B-B-B-'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 328.991px 10.2131px; text-wrap: nowrap; transform-origin: 328.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'C-C-C-C-C'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 328.991px 10.2131px; text-wrap: nowrap; transform-origin: 328.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'-D-D-D-D-'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 328.991px 10.2131px; text-wrap: nowrap; transform-origin: 328.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'--E-E-E--'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 328.991px 10.2131px; text-wrap: nowrap; transform-origin: 328.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 328.991px 10.2131px; text-wrap: nowrap; transform-origin: 328.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003erot60(A)=[\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 328.991px 10.2131px; text-wrap: nowrap; transform-origin: 328.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'--A-B-C--'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 328.991px 10.2131px; text-wrap: nowrap; transform-origin: 328.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'-A-B-C-D-'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 328.991px 10.2131px; text-wrap: nowrap; transform-origin: 328.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'A-B-C-D-E'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 328.991px 10.2131px; text-wrap: nowrap; transform-origin: 328.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'-B-C-D-E-'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 328.991px 10.2131px; text-wrap: nowrap; transform-origin: 328.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'--C-D-E--'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0085px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 308.991px 10.4972px; text-align: left; transform-origin: 308.999px 10.5043px; white-space-collapse: preserve; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eA=rot60(A,n) should rotate the hexagon 60*n degrees counterclockwise (and thus clockwise if n\u0026lt;0).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function A=rot60(A,n)\r\nA=rot90(A,2*n/3);\r\n","test_suite":"%%\r\nA=['--A-A-A--'\r\n   '-B-B-B-B-'\r\n   'C-C-C-C-C'\r\n   '-D-D-D-D-'\r\n   '--E-E-E--'];\r\nB=['--A-B-C--'\r\n   '-A-B-C-D-'\r\n   'A-B-C-D-E'\r\n   '-B-C-D-E-'\r\n   '--C-D-E--'];\r\nassert(isequal(rot60(A),B))\r\n%%\r\nA=['  A A A  '\r\n   ' B B B B '\r\n   'C C C C C'\r\n   ' D D D D '\r\n   '  E E E  '];\r\nB=['  C B A  '\r\n   ' D C B A '\r\n   'E D C B A'\r\n   ' E D C B '\r\n   '  E D C  '];\r\nassert(isequal(rot60(A,-1),B))\r\n%%\r\nA=['   3 1 4 1   '\r\n   '  5 9 2 6 5  '\r\n   ' 3 5 8 9 7 9 '\r\n   '3 2 3 8 4 6 2'\r\n   ' 6 4 3 3 8 3 '\r\n   '  2 7 9 5 0  '\r\n   '   2 8 8 4   '];\r\nB=['   1 5 9 2   '\r\n   '  4 6 7 6 3  '\r\n   ' 1 2 9 4 8 0 '\r\n   '3 9 8 8 3 5 4'\r\n   ' 5 5 3 3 9 8 '\r\n   '  3 2 4 7 8  '\r\n   '   3 6 2 2   '];\r\nassert(isequal(rot60(A),B))\r\n%%\r\nA=['    A B C D E    '\r\n   '   F G H I J K   '\r\n   '  L M N O P Q R  '\r\n   ' S T U V W X Y Z '\r\n   '1 2 3 4 5 6 7 8 9'\r\n   ' a b c d e f g h '\r\n   '  i j k l m n o  '\r\n   '   p q r s t u   '\r\n   '    v w x y z    '];\r\nB=['    E K R Z 9    '\r\n   '   D J Q Y 8 h   '\r\n   '  C I P X 7 g o  '\r\n   ' B H O W 6 f n u '\r\n   'A G N V 5 e m t z'\r\n   ' F M U 4 d l s y '\r\n   '  L T 3 c k r x  '\r\n   '   S 2 b j q w   '\r\n   '    1 a i p v    '];\r\nassert(isequal(rot60(A),B))\r\nn=randi([-100 100],1);\r\nassert(isequal(rot60(A,3*n),rot90(A,2*n)))\r\n%%\r\nA=[nan nan 120 nan  90 nan  60 nan nan\r\n   nan 150 nan 120 nan  60 nan  30 nan\r\n   180 nan 180 nan nan nan   0 nan   0\r\n   nan 210 nan 240 nan 300 nan 330 nan\r\n   nan nan 240 nan 270 nan 300 nan nan];\r\nfor n=1:6\r\n   assert(isequaln(rot60(A,n),mod(A-60*n,360)))\r\nend\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":245,"edited_by":245,"edited_at":"2024-05-10T20:49:04.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2024-05-08T01:12:38.000Z","updated_at":"2026-06-05T03:50:02.000Z","published_at":"2024-05-10T20:49:04.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a 2*m-1 by 4*m-3 matrix in which certain elements form a hexagon of side length m, rotate the hexagon 60 degrees counterclockwise, as shown below for m=3:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[A=[\\n'--A-A-A--'\\n'-B-B-B-B-'\\n'C-C-C-C-C'\\n'-D-D-D-D-'\\n'--E-E-E--']\\n\\nrot60(A)=[\\n'--A-B-C--'\\n'-A-B-C-D-'\\n'A-B-C-D-E'\\n'-B-C-D-E-'\\n'--C-D-E--']]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA=rot60(A,n) should rotate the hexagon 60*n degrees counterclockwise (and thus clockwise if n\u0026lt;0).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":1499,"title":"Kryptos - CIA Cypher Sculpture: Vigenere Encryption","description":"The \u003chttp://en.wikipedia.org/wiki/Kryptos Kryptos Sculpture\u003e contains four encypted messages.\r\n\r\nThis Challenge is to Encrypt two of the original messages for the sculptor.\r\n\r\nThe method employed is \u003chttp://en.wikipedia.org/wiki/Vigen%C3%A8re_cipher Vigenere Encryption\u003e. One clarification is that \"?\" are removed from the coding sequence and then re-inserted in the final encoded message.\r\n\r\nOriginal phrase: Between subtle shading and the absence of light lies the nuance of iqlusion.\r\n\r\nFor coding purposes spaces and punctuation are removed, except \"?\".\r\n\r\nPhrase to encode: BETWEENSUBTLESHADINGANDTHEABSENCEOFLIGHTLIESTHENUANCEOFIQLUSION\r\n\r\n*Input:* Encode Phrase, Vigenere alphabet word, Vigenere shift word\r\n\r\nVigenere alphabet word ='KRYPTOS';\r\n\r\nVigenere shift word ='PALIMPSEST';\r\n\r\n*Output:* EMUFPHZLRFAXYUSDJKZLDKRNSHGNFIVJYQTQUXQBQVYUVLLTREVJYQTMKYRDMFD\r\n\r\nThe encryption matrix for this case:\r\n\r\n  KRYPTOSABCDEFGHIJLMNQUVWXZ\r\n\r\n  PTOSABCDEFGHIJLMNQUVWXZKRY\r\n  ABCDEFGHIJLMNQUVWXZKRYPTOS\r\n  LMNQUVWXZKRYPTOSABCDEFGHIJ\r\n  IJLMNQUVWXZKRYPTOSABCDEFGH\r\n  MNQUVWXZKRYPTOSABCDEFGHIJL\r\n  PTOSABCDEFGHIJLMNQUVWXZKRY\r\n  SABCDEFGHIJLMNQUVWXZKRYPTO\r\n  EFGHIJLMNQUVWXZKRYPTOSABCD\r\n  SABCDEFGHIJLMNQUVWXZKRYPTO\r\n  TOSABCDEFGHIJLMNQUVWXZKRYP\r\n\r\nFollow Up Challenges:\r\n\r\n1) \u003chttp://www.mathworks.com/matlabcentral/cody/problems/1500-kryptos-cia-cypher-sculpture-vignere-decryption Vigenere Decryption\u003e\r\n\r\n2) Dictionary search\r\n\r\n3) KRYPTOS Part IV\r\n\r\n\u003chttp://math.ucsd.edu/~crypto/Projects/KarlWang/index2.html#1 KRYPTOS Solutions\u003e\r\n\r\n  \r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","description_html":"\u003cp\u003eThe \u003ca href = \"http://en.wikipedia.org/wiki/Kryptos\"\u003eKryptos Sculpture\u003c/a\u003e contains four encypted messages.\u003c/p\u003e\u003cp\u003eThis Challenge is to Encrypt two of the original messages for the sculptor.\u003c/p\u003e\u003cp\u003eThe method employed is \u003ca href = \"http://en.wikipedia.org/wiki/Vigen%C3%A8re_cipher\"\u003eVigenere Encryption\u003c/a\u003e. One clarification is that \"?\" are removed from the coding sequence and then re-inserted in the final encoded message.\u003c/p\u003e\u003cp\u003eOriginal phrase: Between subtle shading and the absence of light lies the nuance of iqlusion.\u003c/p\u003e\u003cp\u003eFor coding purposes spaces and punctuation are removed, except \"?\".\u003c/p\u003e\u003cp\u003ePhrase to encode: BETWEENSUBTLESHADINGANDTHEABSENCEOFLIGHTLIESTHENUANCEOFIQLUSION\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e Encode Phrase, Vigenere alphabet word, Vigenere shift word\u003c/p\u003e\u003cp\u003eVigenere alphabet word ='KRYPTOS';\u003c/p\u003e\u003cp\u003eVigenere shift word ='PALIMPSEST';\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e EMUFPHZLRFAXYUSDJKZLDKRNSHGNFIVJYQTQUXQBQVYUVLLTREVJYQTMKYRDMFD\u003c/p\u003e\u003cp\u003eThe encryption matrix for this case:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eKRYPTOSABCDEFGHIJLMNQUVWXZ\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003ePTOSABCDEFGHIJLMNQUVWXZKRY\r\nABCDEFGHIJLMNQUVWXZKRYPTOS\r\nLMNQUVWXZKRYPTOSABCDEFGHIJ\r\nIJLMNQUVWXZKRYPTOSABCDEFGH\r\nMNQUVWXZKRYPTOSABCDEFGHIJL\r\nPTOSABCDEFGHIJLMNQUVWXZKRY\r\nSABCDEFGHIJLMNQUVWXZKRYPTO\r\nEFGHIJLMNQUVWXZKRYPTOSABCD\r\nSABCDEFGHIJLMNQUVWXZKRYPTO\r\nTOSABCDEFGHIJLMNQUVWXZKRYP\r\n\u003c/pre\u003e\u003cp\u003eFollow Up Challenges:\u003c/p\u003e\u003cp\u003e1) \u003ca href = \"http://www.mathworks.com/matlabcentral/cody/problems/1500-kryptos-cia-cypher-sculpture-vignere-decryption\"\u003eVigenere Decryption\u003c/a\u003e\u003c/p\u003e\u003cp\u003e2) Dictionary search\u003c/p\u003e\u003cp\u003e3) KRYPTOS Part IV\u003c/p\u003e\u003cp\u003e\u003ca href = \"http://math.ucsd.edu/~crypto/Projects/KarlWang/index2.html#1\"\u003eKRYPTOS Solutions\u003c/a\u003e\u003c/p\u003e","function_template":"function encoded=encode_vigenere(phrase,word1,word2)\r\n encoded=phrase;\r\nend","test_suite":"phrase=upper('Between subtle shading and the absence of light lies the nuance of iqlusion.');\r\nphrase_encode=phrase(regexp(phrase,'[A-Z?]'));\r\n\r\nencoded_exp='EMUFPHZLRFAXYUSDJKZLDKRNSHGNFIVJYQTQUXQBQVYUVLLTREVJYQTMKYRDMFD';\r\nword1='KRYPTOS';\r\nword2='PALIMPSEST';\r\nencoded= encode_vigenere(phrase_encode,word1,word2);\r\n\r\nassert(strcmp(encoded_exp,encoded))\r\n%%\r\n\r\nphrase=upper('It was totally invisible Hows that possible? They used the Earths magnetic field X The information was gathered and transmitted undergruund to an unknown location X Does Langley know about this? They should Its buried out there somewhere X Who knows the exact location? Only WW This was his last message X Thirty eight degrees fifty seven minutes six point five seconds north Seventy seven degrees eight minutes forty four seconds west ID by rows');\r\nphrase_encode=phrase(regexp(phrase,'[A-Z?]'));\r\n\r\n\r\nencoded_exp='VFPJUDEEHZWETZYVGWHKKQETGFQJNCEGGWHKK?DQMCPFQZDQMMIAGPFXHQRLGTIMVMZJANQLVKQEDAGDVFRPJUNGEUNAQZGZLECGYUXUEENJTBJLBQCRTBJDFHRRYIZETKZEMVDUFKSJHKFWHKUWQLSZFTIHHDDDUVH?DWKBFUFPWNTDFIYCUQZEREEVLDKFEZMOQQJLTTUGSYQPFEUNLAVIDXFLGGTEZ?FKZBSFDQVGOGIPUFXHHDRKFFHQNTGPUAECNUVPDJMQCLQUMUNEDFQELZZVRRGKFFVOEEXBDMVPNFQXEZLGREDNQFMPNZGLFLPMRJQYALMGNUVPDXVKPDQUMEBEDMHDAFMJGZNUPLGEWJLLAETG';\r\n\r\nword1='KRYPTOS';\r\nword2='ABSCISSA';\r\nencoded= encode_vigenere(phrase_encode,word1,word2);\r\n\r\nassert(strcmp(encoded_exp,encoded))\r\n%%\r\nphrase=upper('The fox jumped over the moon');\r\nphrase_encode=phrase(regexp(phrase,'[A-Z?]'));\r\n\r\nencoded_exp='VUIPFSBYVQMMWPIMEVPZCVK';\r\nword1='KRYPTOS';\r\nword2='MATLAB';\r\nencoded= encode_vigenere(phrase_encode,word1,word2);\r\n\r\nassert(strcmp(encoded_exp,encoded))\r\n\r\n%%\r\nphrase=upper('Between the Devil and the deep blue sea');\r\nphrase_encode=phrase(regexp(phrase,'[A-Z?]'));\r\n\r\n\r\nword1='AWEIGH';\r\nword2='MATLAB';\r\nencoded= encode_vigenere(phrase_encode,word1,word2);\r\nencoded_exp='SENMEDWTZNDDFIBLNNCHVTEDIBBCEZOA';\r\n\r\nassert(strcmp(encoded_exp,encoded))\r\n\r\n\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":5,"comments_count":3,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":64,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":28,"created_at":"2013-05-11T20:36:34.000Z","updated_at":"2026-06-05T12:06:16.000Z","published_at":"2013-05-11T21:19:07.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Kryptos\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eKryptos Sculpture\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e contains four encypted messages.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to Encrypt two of the original messages for the sculptor.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe method employed is\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Vigen%C3%A8re_cipher\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eVigenere Encryption\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. One clarification is that \\\"?\\\" are removed from the coding sequence and then re-inserted in the final encoded message.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOriginal phrase: Between subtle shading and the absence of light lies the nuance of iqlusion.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor coding purposes spaces and punctuation are removed, except \\\"?\\\".\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePhrase to encode: BETWEENSUBTLESHADINGANDTHEABSENCEOFLIGHTLIESTHENUANCEOFIQLUSION\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Encode Phrase, Vigenere alphabet word, Vigenere shift word\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eVigenere alphabet word ='KRYPTOS';\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eVigenere shift word ='PALIMPSEST';\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e EMUFPHZLRFAXYUSDJKZLDKRNSHGNFIVJYQTQUXQBQVYUVLLTREVJYQTMKYRDMFD\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe encryption matrix for this case:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[KRYPTOSABCDEFGHIJLMNQUVWXZ\\n\\nPTOSABCDEFGHIJLMNQUVWXZKRY\\nABCDEFGHIJLMNQUVWXZKRYPTOS\\nLMNQUVWXZKRYPTOSABCDEFGHIJ\\nIJLMNQUVWXZKRYPTOSABCDEFGH\\nMNQUVWXZKRYPTOSABCDEFGHIJL\\nPTOSABCDEFGHIJLMNQUVWXZKRY\\nSABCDEFGHIJLMNQUVWXZKRYPTO\\nEFGHIJLMNQUVWXZKRYPTOSABCD\\nSABCDEFGHIJLMNQUVWXZKRYPTO\\nTOSABCDEFGHIJLMNQUVWXZKRYP]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFollow Up Challenges:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e1)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/1500-kryptos-cia-cypher-sculpture-vignere-decryption\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eVigenere Decryption\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e2) Dictionary search\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e3) KRYPTOS Part IV\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://math.ucsd.edu/~crypto/Projects/KarlWang/index2.html#1\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eKRYPTOS Solutions\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":60743,"title":"Bit Stream conversion to Audio Frequency Shift Key using Bell 202 (Modem)","description":"Convert a character binary bit-stream transmitted at a certain baud-rate into an audio stream using Audio Frequency Shift Key (AFSK) using the Bell 202 standard at a given sample rate. \r\nFrequency of sine audio wave: '1' = 1200 Hz, '0' = 2200 Hz\r\nDuration of each bit: based on baud-rate\r\nDigitized audio stream is produced at the sample rate and is smooth between bits (initially starts at zero phase shift). Normalize final signal to +-1 peak-to-peak.\r\nplot(binaryToBell202('101',1.2e5,600))\r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 500.438px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 408px 250.219px; transform-origin: 408px 250.219px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 21px; text-align: left; transform-origin: 385px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eConvert a character binary bit-stream transmitted at a certain baud-rate into an audio stream using Audio Frequency Shift Key (AFSK) using the Bell 202 standard at a given sample rate. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFrequency of sine audio wave: '1' = 1200 Hz, '0' = 2200 Hz\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eDuration of each bit: based on baud-rate\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 21px; text-align: left; transform-origin: 385px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eDigitized audio stream is produced at the sample rate and is smooth between bits (initially starts at zero phase shift). Normalize final signal to +-1 peak-to-peak.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 20.4375px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 405px 10.2188px; transform-origin: 405px 10.2188px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; text-wrap-mode: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eplot(binaryToBell202(\u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'101'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e,1.2e5,600))\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 307px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 385px 153.5px; text-align: left; transform-origin: 385px 153.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function audioSignal = binaryToBell202(binaryStream, sampleRate, bitRate)\r\n  audioSignal = binaryStream;\r\nend","test_suite":"%%\r\nrng(2718);\r\nr=num2str(randi(2,1,1e6)-1);\r\nr(r==' ')=[];\r\na=sum(round(binaryToBell202(r,1.2e5,1200),4)*10000);\r\na_correct=0;\r\nassert(isequal(a,a_correct))\r\n%%\r\nrng(3141)\r\nr=num2str(randi(2,1,1e6)-1);\r\nr(r==' ')=[];\r\na=sum(round(binaryToBell202(r,1e5,300),4)*10000);\r\na_correct=-195091;\r\nassert(isequal(a,a_correct))\r\n%%\r\nrng(1728);\r\nr='101010111000';\r\na=sum(round(binaryToBell202(r,1e5,1200),3)*10000);\r\na_correct=5850;\r\nassert(isequal(a,a_correct))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":5,"created_by":145982,"edited_by":145982,"edited_at":"2025-03-25T00:29:31.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":"2025-03-25T00:29:31.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2024-09-26T22:45:14.000Z","updated_at":"2026-06-06T01:54:20.000Z","published_at":"2024-09-26T22:45:14.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eConvert a character binary bit-stream transmitted at a certain baud-rate into an audio stream using Audio Frequency Shift Key (AFSK) using the Bell 202 standard at a given sample rate. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFrequency of sine audio wave: '1' = 1200 Hz, '0' = 2200 Hz\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDuration of each bit: based on baud-rate\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDigitized audio stream is produced at the sample rate and is smooth between bits (initially starts at zero phase shift). 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":60746,"title":"List Ormiston prime pairs","description":"The numbers 1913 and 1931 form the first Ormiston prime pair. The numbers are consecutive primes, and they are anagrams of each other—that is, the digits of one number can be arranged to give the second number. 347 and 743 are not an Ormiston prime pair: they are anagrams of each other, but they are not consecutive primes. \r\nWrite a function that produces the nth Ormiston prime pair.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 93px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 46.5px; transform-origin: 407px 46.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe numbers 1913 and 1931 form the first Ormiston prime pair. The numbers are consecutive primes, and they are anagrams of each other—that is, the digits of one number can be arranged to give the second number. 347 and 743 are not an Ormiston prime pair: they are anagrams of each other, but they are not consecutive primes. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function that produces the nth Ormiston prime pair.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = OrmistonPrimePair(n)\r\n  p = primes(n); \r\n  y = isequal(sort(p(n)),sort(p(n+1)));\r\nend","test_suite":"%%\r\nn = 1;\r\ny = OrmistonPrimePair(n);\r\ny_correct = [1913 1931];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 18;\r\ny = OrmistonPrimePair(n);\r\ny_correct = [55313 55331];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 54;\r\ny = OrmistonPrimePair(n);\r\ny_correct = [131413 131431];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 126;\r\ny = OrmistonPrimePair(n);\r\ny_correct = [343337 343373];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 180;\r\ny = OrmistonPrimePair(n);\r\ny_correct = [481513 481531];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 1080;\r\ny = OrmistonPrimePair(n);\r\ny_correct = [2744279 2744297];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 1818;\r\ny = OrmistonPrimePair(n);\r\ny_correct = [4696613 4696631];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 5436;\r\ny = OrmistonPrimePair(n);\r\ny_correct = [14589013 14589031];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 7290;\r\ny = OrmistonPrimePair(n);\r\ny_correct = [19592813 19592831];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 10800;\r\ny = OrmistonPrimePair(n);\r\ny_correct = [29470879 29470897];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 17496;\r\ny = OrmistonPrimePair(n);\r\ny_correct = [48654979 48654997];\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 11111;\r\ny = OrmistonPrimePair(n);\r\nsmf_correct = 81182;\r\nsmf = sum([max(factor(y(1)+1)) max(factor(y(2)+1))]);\r\nassert(isequal(smf,smf_correct))","published":true,"deleted":false,"likes_count":0,"comments_count":1,"created_by":46909,"edited_by":46909,"edited_at":"2024-10-09T05:13:23.000Z","deleted_by":null,"deleted_at":null,"solvers_count":8,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2024-10-09T05:13:08.000Z","updated_at":"2026-06-06T01:40:24.000Z","published_at":"2024-10-09T05:13:23.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe numbers 1913 and 1931 form the first Ormiston prime pair. The numbers are consecutive primes, and they are anagrams of each other—that is, the digits of one number can be arranged to give the second number. 347 and 743 are not an Ormiston prime pair: they are anagrams of each other, but they are not consecutive primes. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function that produces the nth Ormiston prime pair.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":61292,"title":"The Singularity - Event Horizon (Absolute Zero)","description":"Inspired by Problem61291\r\nMô tả:\r\nBầy đàn gồm M agents phải di chuyển xuyên qua không gian 5D Hyper-Torus ( kích thước mỗi chiều L = 100 ) từ Node 1 đến Target Node. Để sống sót, thuật toán của bạn phải tuân thủ nghiêm ngặt các định luật vật lí sau:\r\n1. Hình học Torus 5D: Khoảng cách giữa 2 điểm là khoảng cách Chebyshev trên mặt Torus:\r\n        d = max(min( | x_i - y_i |,100 - | x_i - y_i | )) cho i = 1..5.\r\n2. Động lực học Pulse: Chi phí năng lượng tại thời điểm cập bến t bị nhân bởi hệ số \r\n        Pulse(t) = 1 + 2 sin^2(pi*t/2). Lưu ý: V_{swarm} = 20.\r\n3. Kẻ săn mồi ( Dynamic Predator ): Predator xuất phát từ pred_start với V_p = 15. Nếu tại bất kì node trung gian nào, Predator có thể đến trước hoặc cùng lúc với bầy đàn ( t_p \u003c= t_s ), bầy đàn sẽ bị tiêu diệt ( Energy = -1 ).\r\n4. Cấm quay đầu ( No-Revisit Rule ): Để tránh việc \"câu giờ\" đợi nhịp Pulse thấp, bầy đàn không được phép đi qua 1 node quá một lần trong cùng 1 hành trình.\r\n5. Hệ số bão hòa \u0026 Thuế Entropy: *Sat  = M + 0.125 x M x ( M - 1 ).\r\n6. Cộng hưởng Spin: Nếu tọa độ thứ 5(ω) của node đến gần sát một số nguyên ( sai số \u003c 0.05 ), một hình phạt Spin = 50 x (step + 1) sẽ được cộng vào chi phí","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 405px; display: block; min-width: 0px; padding-block-start: 0px; padding-inline-start: 2px; padding-left: 2px; padding-top: 0px; perspective-origin: 468.5px 202.5px; transform-origin: 468.5px 202.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 10.5px; text-align: left; transform-origin: 444.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eInspired by \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://ww2.mathworks.cn/matlabcentral/cody/groups/97667/problems/61291\"\u003e\u003cspan style=\"border-block-end-color: rgb(0, 91, 130); border-block-start-color: rgb(0, 91, 130); border-bottom-color: rgb(0, 91, 130); border-inline-end-color: rgb(0, 91, 130); border-inline-start-color: rgb(0, 91, 130); border-left-color: rgb(0, 91, 130); border-right-color: rgb(0, 91, 130); border-top-color: rgb(0, 91, 130); caret-color: rgb(0, 91, 130); color: rgb(0, 91, 130); column-rule-color: rgb(0, 91, 130); outline-color: rgb(0, 91, 130); text-decoration-color: rgb(0, 91, 130); text-emphasis-color: rgb(0, 91, 130); \"\u003e\u003cspan style=\"\"\u003eProblem61291\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 10.5px; text-align: left; transform-origin: 444.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eMô tả:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 21px; text-align: left; transform-origin: 444.5px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eBầy đàn gồm \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eM\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e agents phải di chuyển xuyên qua không gian 5D Hyper-Torus ( kích thước mỗi chiều \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eL\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e = 100 ) từ Node 1 đến Target Node. Để sống sót, thuật toán của bạn phải tuân thủ nghiêm ngặt các định luật vật lí sau:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 10.5px; text-align: left; transform-origin: 444.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e1. \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eHình học Torus 5D\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: Khoảng cách giữa 2 điểm là khoảng cách Chebyshev trên mặt Torus:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 10.5px; text-align: left; transform-origin: 444.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e        d = max(min( | x_i - y_i |,100 - | x_i - y_i | )) cho i = 1..5.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 10.5px; text-align: left; transform-origin: 444.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e2. \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eĐộng lực học Pulse\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: Chi phí năng lượng tại thời điểm cập bến \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003et\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e bị nhân bởi hệ số \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 10.5px; text-align: left; transform-origin: 444.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e        Pulse(t) = 1 + 2 sin^2(pi*t/2). Lưu ý: V_{swarm} = 20.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 21px; text-align: left; transform-origin: 444.5px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e3. \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eKẻ săn mồi ( Dynamic Predator ):\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e Predator xuất phát từ pred_start với V_p = 15. Nếu tại bất kì node trung gian nào, Predator có thể đến trước hoặc cùng lúc với bầy đàn ( t_p \u0026lt;= t_s ), bầy đàn sẽ bị tiêu diệt ( Energy = -1 ).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 21px; text-align: left; transform-origin: 444.5px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e4. \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eCấm quay đầu ( No-Revisit Rule ):\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e Để tránh việc \"câu giờ\" đợi nhịp Pulse thấp, bầy đàn không được phép đi qua 1 node quá một lần trong cùng 1 hành trình.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 10.5px; text-align: left; transform-origin: 444.5px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e5. \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eHệ số bão hòa \u0026amp; Thuế Entropy\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: *Sat  = \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003eM\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e + 0.125 x M x ( M - 1 ).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 444.5px 21px; text-align: left; transform-origin: 444.5px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e6. \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eCộng hưởng Spin\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e: Nếu tọa độ thứ 5(ω) của node đến gần sát một số nguyên ( sai số \u0026lt; 0.05 ), một hình phạt Spin = 50 x (\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-style: italic; \"\u003estep\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e + 1) sẽ được cộng vào chi phí\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [max_energy, best_path] = solve_event_horizon_v3(nodes, pred_start, params)\r\n    V_p = 15;%\r\nend","test_suite":"%% PHASE 1: THE PULSE TIMING TRAP\r\nclear n p;\r\np.M = 1; p.target = 2; p.ATP_total = 2000;\r\nn(1).coord = [0,0,0,0,0.5]; n(1).res = 1;\r\nn(2).coord = [30,0,0,0,0.5]; n(2).res = 10; \r\nn(3).coord = [10,0,0,0,0.5]; n(3).res = 0.1;\r\n[e1, s1] = solve_event_horizon_v3(n, 0, p);\r\nassert(~isempty(s1) \u0026\u0026 length(s1{1}) == 3, 'FAILED T1: Phải đi vòng qua Node 3 để khớp t=2.0s.');\r\n\r\n%% PHASE 2: HYPER-TORUS WRAP-AROUND \r\nclear n p; p.M = 1; p.ATP_total = 2000; p.target = 2;\r\nbase.res = 1; base.coord = [0,0,0,0,0.5];\r\n\r\n% Test 2: Chiều 1\r\nn2 = repmat(base, 1, 2); n2(2).coord(1) = 90;\r\n[e2, ~] = solve_event_horizon_v3(n2, 0, p);\r\nassert(abs(e2 - 1980) \u003c 1e-3, 'FAILED T2: Lỗi Torus chiều 1');\r\n\r\n% Test 3: Chiều 5\r\nn3 = repmat(base, 1, 2); n3(2).coord(5) = 95.5; \r\n[e3, ~] = solve_event_horizon_v3(n3, 0, p);\r\nassert(e3 \u003e 1990, 'FAILED T3: Lỗi Torus chiều 5');\r\n\r\n%% PHASE 4: PREDATOR INTERCEPT \r\nclear n p; p.M = 1; p.ATP_total = 5000; p.target = 2;\r\nn(1).coord = [0,0,0,0,0.5]; n(1).res = 1;\r\nn(2).coord = [40,0,0,0,0.5]; n(2).res = 1; \r\n\r\nn(3).coord = [0,20,0,0,0.5]; n(3).res = 0.1; \r\n\r\nn(4).coord = [0,60,0,0,0.5]; \r\n\r\n[e7, s7] = solve_event_horizon_v3(n, 4, p);\r\n\r\nassert(~isempty(s7) \u0026\u0026 e7 \u003e 0, 'FAILED T7: Vẫn không tìm thấy đường an toàn');\r\n\r\n%% TEST 20: THE OMEGA LABYRINTH (Bản Chuẩn Hóa 100%)\r\nclear n p;\r\n% 1. Cấu hình cố định để Solver và Testcase đồng bộ\r\np.M = 10; \r\np.target = 5; \r\np.ATP_total = 10000;\r\nV_s = 20; \r\nV_p = 15; % Hằng số Predator\r\n\r\n% 2. Tạo 5 Nodes với tọa độ NẰM TRONG KHOẢNG [0, 100]\r\n% Node 1: Xuất phát\r\nn(1).coord = [10, 10, 10, 10, 0.5]; n(1).res = 1;\r\n% Node 2: Predator (Kẻ săn mồi đứng đây)\r\nn(2).coord = [30, 10, 10, 10, 0.5]; n(2).res = 1;\r\n% Node 3: Node trung gian (Đường vòng an toàn)\r\nn(3).coord = [10, 50, 10, 10, 0.5]; n(3).res = 0.5;\r\n% Node 4: Node rác\r\nn(4).coord = [80, 80, 80, 80, 0.5]; n(4).res = 1;\r\n% Node 5: Đích\r\nn(5).coord = [50, 50, 10, 10, 0.5]; n(5).res = 0.5;\r\n\r\n% 3. Chạy Solver\r\n[user_e, user_path] = solve_event_horizon_v3(n, 2, p);\r\n\r\n% 4. CHẶN LỖI CRASH (Nếu Solver trả về rỗng)\r\nassert(~isempty(user_path) \u0026\u0026 ~isempty(user_path{1}), 'Solver không tìm thấy bất kỳ con đường nào!');\r\n\r\n% 5. KIỂM TRA LOGIC SINH TỒN\r\npath = user_path{1};\r\nt_run = 0; is_captured = false;\r\nSat = p.M + 0.125 * p.M * (p.M - 1);\r\ne_val = p.ATP_total;\r\n\r\nfor k = 1:length(path)-1\r\n    u = path(k); v = path(k+1);\r\n    df = abs(n(u).coord - n(v).coord);\r\n    % Chuẩn hóa khoảng cách Torus chuẩn 100%\r\n    d = max(min(df, 100 - df));\r\n    \r\n    t_run = t_run + d/V_s;\r\n    \r\n    % Kiểm tra Predator (Node 2) có bắt được tại Node v không\r\n    df_p = abs(n(2).coord - n(v).coord);\r\n    dp = max(min(df_p, 100 - df_p));\r\n    \r\n    if dp/V_p \u003c= t_run + 1e-9\r\n        is_captured = true; break;\r\n    end\r\n    \r\n    % Tính năng lượng để đối chiếu\r\n    pulse = 1 + 2 * (sin(t_run * pi / 2)^2);\r\n    cost = (d * n(v).res * Sat * pulse) * (1.15^(k-1));\r\n    if abs(n(v).coord(5) - round(n(v).coord(5))) \u003c 0.05\r\n        cost = cost + 50 * k;\r\n    end\r\n    e_val = e_val - cost;\r\nend\r\n\r\n% ASSERTION CUỐI CÙNG\r\nassert(~is_captured, 'Omega Phase: Swarm captured! Code của bạn đang phớt lờ Predator.');\r\nassert(abs(user_e - e_val) \u003c 1e-5, 'Omega Phase: Năng lượng tính toán không khớp!');\r\n%% PHASE 5: SPIN SENSITIVITY \r\nclear n p; p.M = 20; p.target = 2; p.ATP_total = 10000;\r\nn(1).coord = [0,0,0,0,0.5]; n(2).coord = [10,0,0,0,1.02]; n(2).res = 1; \r\n[e12, ~] = solve_event_horizon_v3(n, 0, p);\r\nassert(abs(e12 - 8600) \u003c 1e-4, 'FAILED T12: Spin Penalty không kích hoạt đúng ngưỡng.');\r\n\r\nn(2).coord(5) = 1.06;\r\n[e13, ~] = solve_event_horizon_v3(n, 0, p);\r\nassert(abs(e13 - 8650) \u003c 1e-4, 'FAILED T13: Spin Penalty kích hoạt sai mục tiêu.');","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":5076265,"edited_by":5076265,"edited_at":"2026-03-23T17:47:36.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":"2026-03-23T17:47:36.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2026-03-22T19:51:08.000Z","updated_at":"2026-06-06T20:25:40.000Z","published_at":"2026-03-22T19:51:08.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eInspired by \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://ww2.mathworks.cn/matlabcentral/cody/groups/97667/problems/61291\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem61291\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eMô tả:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBầy đàn gồm \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eM\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e agents phải di chuyển xuyên qua không gian 5D Hyper-Torus ( kích thước mỗi chiều \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eL\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e = 100 ) từ Node 1 đến Target Node. Để sống sót, thuật toán của bạn phải tuân thủ nghiêm ngặt các định luật vật lí sau:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e1. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eHình học Torus 5D\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: Khoảng cách giữa 2 điểm là khoảng cách Chebyshev trên mặt Torus:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e        d = max(min( | x_i - y_i |,100 - | x_i - y_i | )) cho i = 1..5.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e2. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eĐộng lực học Pulse\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: Chi phí năng lượng tại thời điểm cập bến \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003et\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e bị nhân bởi hệ số \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e        Pulse(t) = 1 + 2 sin^2(pi*t/2). Lưu ý: V_{swarm} = 20.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e3. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eKẻ săn mồi ( Dynamic Predator ):\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Predator xuất phát từ pred_start với V_p = 15. Nếu tại bất kì node trung gian nào, Predator có thể đến trước hoặc cùng lúc với bầy đàn ( t_p \u0026lt;= t_s ), bầy đàn sẽ bị tiêu diệt ( Energy = -1 ).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e4. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eCấm quay đầu ( No-Revisit Rule ):\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e Để tránh việc \\\"câu giờ\\\" đợi nhịp Pulse thấp, bầy đàn không được phép đi qua 1 node quá một lần trong cùng 1 hành trình.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e5. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eHệ số bão hòa \u0026amp; Thuế Entropy\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: *Sat  = \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eM\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e + 0.125 x M x ( M - 1 ).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e6. \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eCộng hưởng Spin\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e: Nếu tọa độ thứ 5(ω) của node đến gần sát một số nguyên ( sai số \u0026lt; 0.05 ), một hình phạt Spin = 50 x (\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003estep\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e + 1) sẽ được cộng vào chi phí\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":60989,"title":"Which YAxis does this Graphic object belong to?","description":"Your function gets a graphic object in an Axes that has 2 YAxis. Can you determine which YAxis does this object belong to?\r\nI provided my reference answer to prove the solvability of the problem, but Size 159 is definitely not optimal.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 51px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 408px 25.5px; transform-origin: 408px 25.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYour function gets a graphic object in an Axes that has 2 YAxis. Can you determine which YAxis does this object belong to?\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 10.5px; text-align: left; transform-origin: 385px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI provided my reference answer to prove the solvability of the problem, but Size 159 is definitely not optimal.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function YAxis=GetYAxis(GObject)\r\nYAxis=GObject.Parent.YAxis\r\nend","test_suite":"%%\r\nNumTrials=100;\r\nQA=gobjects(2,NumTrials);\r\nDirections=[\"left\",\"right\"];\r\nAx=gca;\r\nfor A=1:NumTrials\r\n    Random=randi([1,2],1);\r\n    yyaxis(Directions(Random));\r\n    QA(1,A)=scatter(A,A);\r\n    hold on;\r\n    QA(2,A)=Ax.YAxis(Random);\r\nend\r\nassert(~contains(fileread('GetYAxis.m'),'evalin'),'evalin is not allowed');\r\nfor A=1:NumTrials\r\n    assert(isequal(GetYAxis(QA(1,A)),QA(2,A)),sprintf('Incorrect YAxis for Scatter %u',A));\r\nend","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":362068,"edited_by":362068,"edited_at":"2025-08-01T10:41:54.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":"2025-08-01T10:37:08.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2025-08-01T04:35:44.000Z","updated_at":"2026-06-06T06:04:34.000Z","published_at":"2025-08-01T04:35:44.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour function gets a graphic object in an Axes that has 2 YAxis. Can you determine which YAxis does this object belong to?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI provided my reference answer to prove the solvability of the problem, but Size 159 is definitely not optimal.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":61046,"title":"Decipher Message Using Prime of Primitive Root of Two Sequence Key","description":"Provided a message with all characters \u003c= 255 (ascii) as an input, decode the padded message (removing the random character pad), XORing input converted to binary vector (8 bits each) with the inputted prime having primitive root of two as the key. The key is the repeated sequence of 2^x modulo prime key input starting from x=0 until the cycle repeats (unpadded zero binary represented vector of the repeated sequence). After XORing, remove the padded front and return the character array as output.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 105px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 408px 52.5px; transform-origin: 408px 52.5px; vertical-align: baseline; \"\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385px 52.5px; text-align: left; transform-origin: 385px 52.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eProvided a message with all characters \u0026lt;= 255 (ascii) as an input, decode the padded message (removing the random character pad), XORing input converted to binary vector (8 bits each) with the inputted prime having primitive root of two as the key. The key is the repeated sequence of 2^x modulo prime key input starting from x=0 until the cycle repeats (unpadded zero binary represented vector of the repeated sequence). After XORing, remove the padded front and return the character array as output.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function d = deCipher(x,key)\r\n  d=xor(x,key);\r\nend\r\n","test_suite":"%%\r\nx=[39   212    74    83   164    14     3   175    39   212    74    83   164    14     3   175    39   212    74    83   164   14     3   175    39   212    74    83   164    14     3   175    39   212    74    83   164    14     3   175    39   212   74    83   164    14     3   175    39   212    74    83   164    14     3   175    39   212    74    83   164    14     3   19   242    77   208   208    52   219   130    53   242    82   200   207    60   218];\r\nassert(isequal(deCipher(x,19),'I love to swim!'));\r\n%%\r\nx=double('şl‘……‘ŒŒœŚøùè©ădurŢŠª«Ădõ³f ÈijĨĻ™isœ´Ŕ~ê‚ĪŢŞjhĻľŒĒďċĈÓ»ŃÃÜĮîĜŐËīōï´ÖŜąđo‹tŚļŸ‰Ķ¦ĺąˆ®ŝšŊň˜Ç´ļōďƅł¶ÕľŐĔşºśŔ“dk’ĢŠŏĽžá³ÐqfʯĬŕĔăĢċĠŋÆÄĮōĈėĠŽĠ')-100;\r\nassert(isequal(deCipher(x,797),'abcdefghijklmnopqrstuvwxyz !@#$%^\u0026*()_+ 0123456789 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ĺçí°ŒŘčhÔùĺ‚Įąŝĝþ·’Ěĕët§úÓŠ®–vŅņȗŘÞŊЪĮâўèČŢfŕexîÌęĖ‡ë¯zĨàÇÙŠĸŝŘüÓóč©áÓĆĚļęsŒÅuñōď ēŕþøáIJīˆwđĪĈ×ñŔìÄïÌû‚ĻãđéĂþúsÞ©ìĀ×¼ĻĒêiĆàí»ġĩęŠāçökƒöœÀ©y¹šřğƒ’ÿþòt¯ŕêËÊ~ÔìÈð×ŕÚ¿ĭń¬ĩĜĆĒoāý³ĔĒŠĺÂijĈîĭךòËĪµŕÌ¥øŘĆÛţć±ï‘ĔōûŊ¦şĊ—‚ðőù¾ĞûŚ›ľņ£†²èĀĦ„¡ŒřʼnĔäj‚žŘĜÌĥsą¥ºÐľļ¤ÁµšăŚžÉĵû¼ég~ÆŔŞêŏŖæÉ{ً×u¸ŊeĶeęŎķőÑôôşŁŸŇŊéƐĘËœëĩÀţň¬ÆŎįî™għmĞ–Ãėjš¦ðćpĻ…œ»­ĦĐøkąÂċĶ·ğijęþľŠÆå ĹúÀÊĎÍnöĺ¦ĤnďdeîĂægěîrđĂƔ~Ĩtå®IJĕ“Ş™ĴšĥêºĖ½wŅ¶Øœ®æàřÐĺu‹ĬĤ®ĥ†ň³śķŚæüĖ±Ï”àŒôĤŔħġfñĬ•†ġħĚÎáíĘĥŒĘĥ‘ĕ²ĴĘŀfµi€ÓìÒÍdŢŜĦéiåêōĿĝŊĜ¯čģ¾„Šî´đľÇċĥ҈ôĪăŀğăÌÿ°ĥĻŀėë½¾ˆĦķ|s…ÎŝĨĦ«‘–‘–ºıw®íķ˜ÙÔìqԜŖµ°ÆŠģ~Ě¡ïśqīĸdòÛŕ€ãĵţʧĜĖ«ġ»ßÒ¢ŎĶ„ŠkĒÝĪĆŕºĴëĀđ·Đ©ĠoЁŽĴ¶ÄÙċģČėù³‚ØÎŌßĸl±¼ĜėŎŀóí¯ŸmĺĪĥÜĢŔuāyå‹įĆ¾ă؊ėŒ–êďĔķiĭő¢õŔîæÿŠúؤôĴŠ§wĥ©Ţ¶ĕijáoˆč ÔäĖĐÔnrħâf‰év¼ĀÄâĪĤőċäĉ¿ēď¼ļĶhÍÁø¨ÝĨÑŘéùģzįňϚ¤Ň¯™ižĞŠ€İź ŝğœ©yîĂÒąŏ¸ç¥Çijtīı¥tĂŏĥ™ĬČ~ÍĴêĬðŋĕāêiēÛıŘĎŃĭĐÈĞ“Ě†ķÑùĜĤ“ÇĢßĴÔĶğŢĿř¡öıśĮÀÖġ•|ėħn‹´º¨ÍĦ³ĜŁúþƒx‰ÌĀ¸¦ł’ĭ¢”±Ĺdñ›ŀ€ĩŚ¥ħ¬ŠxĨĉîíàèÊıÓĵĢ¡“ˆřkœŃÔĚövĝ´ģĚŎÊĈěëć|đyÒ³èäĝÐ{¸şÁīšø‰ŇÆĝÿőģ쵇‰ïšĈņęięğÀĒlŒØÎʼnŊÒ܌Į¯½æĢÍkĔšÉéĒ›³ĊċjòøkwÿóðōĄĆ÷~˜‚«ĺµ›hģŅĈ”ê̆ĖėŕĢĹązæômüðğŘf¬ĐŎçŌž—šIJďĘāĉ‘}õ—ýęùƒóÍËğìøîхêۓ±žğčó‘‹Ćxýi¨ŊřħĔÛĨϗĿĊ£ĜśĤð‰ž«ãÐĘĴİËįlƒĮoĘĸĸðŌâÖēëÉņĺžÀŽj¨˜Ęľń’ĸĔĐýº²ʼnŚ¾ąfŊŜèģnº‹ČŅēņăĜŠ‹§ĹĪ×ļìōeñº­ýÑÞőğĚ{Ħ©ƒīħĔĠt‘’Ė÷œĝįȏ¬üęČÙjōêàÀÊøŋæ÷îrĮ‚xoiĜįõÒöٙ¬îr“rĠňŖ©‡lĽ¹ìĵ¡āĿ®{sw‰ŠŒČÃŢĽÓŌŐçl¶ÿ¾ÀĻ„dōčŎĚéŜœoijħqÏj·ĵÉu…qðŕ•çĎŚçş­ìŽ‹Î¬ŀĒŘŖIJÖýò¬áŜąĮ̙ĚŊ£řĄãĽ£•Š¦§ğÑĶĢ¢ČçàdãÒøÛÉĨ‹ĩè¢ĂŔċþØϔŗğŎĻƒķñd­ŀĺ|Ęċàäü¦¦Đ鶾óÉØïŅòŒºĮýŁvĢĜč𱁸áŐØĥªğpüÁ÷ďÞśĎÍÔêÁĖ“ˆxöÐëoĝõĸĈŸĈĚÈŎŁþìţñ’ĸņńĺïēĘº~ġ˪Ģēçžħª×ĻgçĈ¶ĈÜóæàoöŔď™ŗķØĭģĕšĨjûÔïŞyt ĥĦ³ĪĮıØĽåˆ~­šðjśÂ†õŖIJ¡”ā×ĵïĄÁî}ėČěp¾Ąöŏ¡“Îō~ÍćpŚý‰‰œŒüÍóÙă·­ĺûĠŔŃĨĆªđÑèōĨÌį‹ŏ¹ªŘóĭ•´zýŐò½ßäĊØ­ëšxèŚľĀî“{ĤŘÈfj¡Ÿ’Ã÷ëŏ¥ĵµĴŖÇĩĺÎĿéŏíĜŝŌý̼Ŝ–Îĺŏ¹†Ńţç|mÉ¡ŐÀıĈŕæŔƒÕė¿¬ęĨÂijåĠĞÇxÂ~è´ôdȃ¸ďĹÆÓùĤÀķöʼnqřĐĝòķŠyúā­ÁƒãĽÿÌ}ʼnŠp–đÀčéĤÏőüĽrŕņĨŀ|ĎĚv„£Ñl©‚üôƅÕŀōœijnð¾âˆńíyćĆėĈ{çÎØģñ¼ĪŘjüĚmƒĕøŖ÷ºo–ŢÁ·›ş’ÜÔ´ÏŊŗ{Ĵč؊„Šʼnõĉłˆ¸ĤʦæžâÛçĭ¨şð•Ŏ¸İġƒċÙûøĀêœfžĘįļé­§ŏģh‘‡š¾ŏŁ}įę˜¿ÑĚïİåû|æĄėÆÛĪµ®Ðœ·™ĭŊý’Ězÿpueļq}Çg¡Ä„îĭʼn©ĵĴ›hŠ¹ÒĴĭÒtÊļtĿĄń±őĔĶíäĥëĊ}ćpÃè“ñ¾|ğİ䱯iôÍá¤ùhù“යōŚÍ•ċŗţÌtÄŌmxIJĿóí‡ŜåĿoäİŃ·»ĖØØĜęµïÒĄļÃİĸä“ü×ÿôîŐñĔpĈāpĬõ¥°ţ§ŝŠŃħéĜĮ™¤Į¨l‘ŞÐ}ĦĜ÷ĀğŠĨ»Ìóâwèðûġÿރıŀ­ÞŘĊĢ̋ˆÊķĥģêyŞƒŇ±Ò|¡ĹĊ—èŚĂxĸĤyĐyŠµōň१ìřçĀÛÏĬ½¸tě¶ŜĜÊôĬ™×†ëtêÚjĐèŋ}…œxŀÇđsĕlŏõăĴkðįĦ‘žĶºÅ×òIJxʑĖōİݔĖòê Ŕ’¿ø¶ğđ²ġÑĩù™İòõąù½ÀĩĠń¯Õţrľë€ÚɲÐúţ™ŝØĽú‡ö¸ŒŢ¢©ăńøĹĈŌŊĺöåţİÃĈezĶłÄņ€³žÑ…ÍėĭuŽä¾×m‘¼įÿţuf´pĝĶ«¡şñÚĥÓÞÖř÷ŊĤ¿n°¥ŐãæeņĎŒkl÷ïמuōĸīĴ¸™o·¸oŽÆōø©¬m­ń²þù§ĎÞ¥¤z~ŃðçË—ĹĐēŁ“ŜœĀð«rĹÃÃäđÅĻèôģńçĻŗĉzŽĴzĵôĺĸĂďĻgđ°¯žÛ¿Ħxwò½ĈţÅÿfĚ‘Í©„ĞꕟŒŠ‘Ð§Ń±ŌÇĶŋđ§¾ÿ§ī£Ñą¼£ĎĂäŎ¡‡ÅÆÿ¶e©˜°Ŗëåļ…ÝĎĶćĕïÖÚ»šň©…ÙĐЙŜĘó×oIJĢ{oćĪá ñĊ”ĈÇäÌşăÉüdsŋÆāgœÄ ŒĿ­ÝÊl|ÇêřÔĥeŘŢÕĀÕüŢ÷řńģá¬ēãŏěžÜ›žÏúĺ–÷Ô캊Ÿ±¢ņþėò¢ċŃřûœ§ÞÒţĹŋĻx£ħğł÷s¢eþĺňäð‘°ĥ‡ýÈĔñōŽşĮ弳ĹĬÚø‰«ĺvęėéªnĒĵíþŒĢöiĭŞ~đ°Ļ÷ÇrĔĕijªķŔìŢģõÔÂŝÖ¢İÄjÊĢăØĶŠġĐāň}äĜÇÞxŢş÷œ«ëÉIJ‹îĠŇ҃ý§áĈؙđļàjߥā’čŌØŖľ´ģļâđÇnĬıģŢōÖĒčõžĈŠyđ°}ĘýÛĠ¡oţ”Ś•Ëĭ©ñ¤Ş”Ÿ°ľ')-100;\r\ny='abcdefghijklmnopqrstuvwxyz !@#$%^\u0026*()_+ 0123456789 ABCDEFGHIJKLMNOPQRSTUVWXYZ';\r\ny=repmat(y,1,100);\r\nrng(2718);\r\ny=y(randperm(length(y)));\r\nassert(isequal(deCipher(x,16067),y));\r\n%%\r\nx=double('“ŀŝřřŝŠšŁţšőšĖþ¸xĤŒ‘ÇãŖ±äÿĨÚİIJŐÉÝz¦vgy•üňłŃijŐÑî‡kv¾äŅ߶Ħu…ðjĺ½igÄÀŞŜřÞÁ£ŋùdfä©kŇðoꊠ¸ÓĤ«ċ•jâ¯ċpg·Đ¦Õŀ}qĻĻg«ŸlÉĉŞÒġ•ÔÆķ¯»™èÁĜŀ¿ĻęÎ{möġæĽ½ÐĤ°ÉÈĔØœđÀġh‡āĠřu˂ċĜÊĂÕĨęâş×ř¼‹­É¯µĦϦøÓĨăøÌ|¿×ÎÔåá×´Òœİfý–™kĬµijş©×Ţç|īdºĩ€¤ıŖÇýqʂèívĜŔij¼˜ĎĂŝÐřšļnzČmŊãÂĺŅŒûįçıċkõĨ÷ĆÒףĦd…‹ċĮàÿĈĹňÙŐIJÒĊ탂ú›ĭðā‹ôȵ')-100;\r\ny='Men go abroad to wonder at the heights of mountains, at the huge waves of the sea, at the long courses of the rivers, at the vast compass of the ocean, at the circular motions of the stars, and they pass by themselves without wondering.';\r\nassert(isequal(deCipher(x,16067),y));\r\n%%\r\nx=double('˜īņŒŒņ³ÓdÓÅℐjīĭÃÚŔŘśÏĻ|f}Ŏu´ÇŕōæĵòöĬĝoÑ®²vÝř»ěíijĥvÍŌæ󼍑ò¶ĬqipˆÄwŜšğ’¥Ôŗ÷ovĀ{·Ň÷²ŅŒÎéªw‚ŗš²Øqśxr®ď¹”ĩiuóöe«ÖwÇńēÌĠ£ÝÆıÂeœúrśļ‚Īė“«lüĎįā|Ðì½Ý…ŜáĄď¾Œ«žåąŎyÄ¿ĐޖæàåŚÇŠÅŝ¡n½iñĔnê‹Ŀāó µ° ÔÅ÷’ËfÄŋüwùݙ£ħ¹ľęž‘ņĻ¤óu‚ħ‚òʼnÞðÖîí¿ĜŜĀl˜ğĿţ½ĚœĦ¯iĒµĒÕ¦ĿğţŃľöìśzûĽľėã¢İĨuПĈêÏłĘĀŋÙņħÓŗò´­ùšħòij“ñœowĽāÜĥĐÏā½ĸĈđ®Ńšô{‘řăğğôĶŇñeíp‡ģĭą—«°')-100;\r\ny='No man can be judged a criminal until he is found guilty; nor can society take from him the public protection until it has been proved that he has violated the conditions on which it was granted. What right, then, but that of power, can authorize the punishment of a citizen so long as there remains any doubt of his guilt?';\r\nassert(isequal(deCipher(x,16067),y));\r\n%%\r\nx=double('ÆõĘĜĜĘĕĔôĖĔĄĔţĻ}½ñćĒ–ģdıĺíŸå籌˜¿sò¼àĹč÷öæą„īÒ®Ã{ıКºŜ»ŽĦ­Ń·ijˆá¥ğţĝÅ¥ÞŇĺ²kï¼·Ŏā½Ņ…uÍĺy¾uĊšjʬċrn¶ċ¼”Ľ|uôĻuÀŸtÆĒġŠčœÕÆĻ©e†ö~ĉŃsĩČÇ¿gĻčłúºœø´Ì…ŗØœĔÂŝ«ŒëąŘ¸ÄºčĎÊāàåŕÇĖÏţÂØz„³iúĚ³ĵ„ŀÿīÏ´µãӄìÓ×´Òŝí|ø—™rĨªòń~á')-100;\r\ny='When one door closes, another opens; but we often look so long and so regretfully upon the closed door that we do not see the one which has opened for us.';\r\nassert(isequal(deCipher(x,16067),y));\r\n%%\r\nx=double('Ŋy”  ”™˜xš˜ˆ˜ß·āŁm‹ňĎĚŸè­¶qģik‰ĐĔŃïĿĮŀŜµ‘{zj‰Ĉ§ŎIJĿ÷­ŒĖĶÿoÌÔĺÕĽ»µ¦†ÅwŘŕĠÄÁÎŇľkyĩ§³ŋļ²Ą¡ ¸Ôݾv·ĈŽ³Ö©ċvg¯ċzÄĴ|rûö¦¸ØÀÏĖęÏŌ‰ØŸı¨ªÔçyĚIJ¿Ļęβ¾÷ėĤł°œðµÊÈĔÏœĒÂōs‰êčĊd߂ğģ£çÛĪęÜŞÈňn„q„n¦ñČ³ç…ŃÿíÌ|¯ØÌÙð΄zÒşòƒùÛà€éxŝšp¡Îŗİ}óf}êuÁĽō“ð{‹kįóxĚŞô¼£ćĴĎeĔŚŀrkēiĄÐ¹ĻĉřöòòæśyûĩĩčŚíŀd¢‘ņĮÉôĔĿ')-100;\r\ny='What is ominous is the ease with which some people go from saying that they don''t like something to saying that the government should forbid it. When you go down that road, don''t expect freedom to survive very long.';\r\nassert(isequal(deCipher(x,16067),y));\r\n%%\r\nx=[{'ī˜Ďė¨ŒŇ³ī˜Ďė¨ŒŇ³ī˜Ďė¨ŒŇ³ī˜Ďė¨ŒŇ³ī˜Ďė¨ŒŇ³ī˜Ďė¨ŒŇ³ī˜Ďė¨ŒŇ³ī˜Ďė¨Ğ'} ...\r\n    {'pţÅÜw'}    {'âñwnš»¾Śâñwnš»¾Śâñwnš»¾Śâñwnš»¾Śâñwnš»¾Śâñwnš»¾Śâñwnš»¾ŚâñwnšĜ'}...\r\n    {'Ł’ĔčÂŘŝ¹Ł’ĔčÂŘŝ¹Ł’ĔčÂŘŝ¹Ł’Ĕņ'}    {' ijµ¬ģy|Ę ijµ¬ģy|Ę ijµ¬ģy|Ę ijµ¬ģy|Ę ijµ¬ģy|Ę ijµ¬ģy|Ę ijµ¬ģy|Ę ijµ¬ģyė'} ...\r\n    {'Ƶijĺ…ïêŽĆµijĺ…ïêŽĆµijń'}    {'‰ĺ¬µĊpeđ‰ĺ¬µĊpeđ‰ĺ¬µĊpeđ‰ĺ¬µĊpeđ‰ĺ¬µĊpeđ‰ĺ¬µĊpeđ‰ĺ¬µz'}  ...\r\n    {'âñwnš»¾Śâñwnš»¾Śâñwnš»¾Śâñwnš»¾Śâñwnš»¾Śâñś'}    {'²ġ‡œ'}    {'oŜÊã솓çoŜÊã솓çoŜÊã솓çoŜÊã솓çoŜÊã솓çoŜÊã솓çoŜÊãìĕ'} ...\r\n    {'üÏřŐ~'}  {'Øë}dśÁ´ŠĂ'}    {'ÿÌŚœ|ĖģwÿÌŚœ|ĖģwÿÌŚœ|Ě'}    {'îÝŋŢmćĒfîÝŋŢmćĒfĀ'}   ...\r\n    {'Ĉ»ĭĴ‹ñäĈ»ĭĴ‹ñäĈ»ĭĴ‹ñäĈ»ĭĴ‹ñäĈ»ĭĴ‹ñäĈ»ĭĴ‹ñäy'}    {'ĿŒĚē¼Ŗţ·ĿŒĚē¼Ŗţn'} ...\r\n    {'Øë}dśÁ´ŠØë}dśÁ´ŠØë}dśÁ´ŠØë}dśÁ´ŠØë}dśÁ´ŠØë}dśÁ´ŠØë}ś'}    {'ßìzsŜ¶ÃŗßìzsŜ¶ÃŗßìzsŜ¶ÃŗßìzsŜ¶ÃŗßìzŘ'} ...\r\n    {'ªę–ĩÓÆIJªę–ĩÓÆIJªę–ĩÓÆIJªę–ĩÓÆIJªę–ĩÓÆIJªÖ'}    {'ņuóúÅē'}    {'ÝîxqŞ´ÁŕÝîxqŞ´ÁŕÝîxqŞ´ÁŕÝîxqŞ´ÁŕÝîxŗ'} ...\r\n    {'dŗÑØ獈ìdŗÑØ獈ìdŗÑØ獈ìdŗÑØ獈ìdŗÑØ獈ìdŗÑØ獈ìdŗÑØ獈ìdŗÑØ獈p'}    {'vŅãÊõŸšþvŅãÊõŸšþvŅãÊõŸšþvŅãÊõŸšþvŅãÊõŸšþvŅãÊõŸšþvŅãÊõŸšþvŅãÊj'}  ...\r\n    {'xŋÝÄû¡”ĀxŋÝÄû¡”ĀxŋÝÄû¡”ĀxŋÝÄû¡”ĀxŋÝÄû¡”ĀxŋÝÄû¡”ĀxŋÝŢ'}    {'ñâńŝrĈčiñâńŝrĈē'}    {'ńwñøÇĭĨÌńwñøÇĭĨÌńwñøÇĭĨÌńwñøÇĭĨÌńwñøÇĭĨÌńwñøÇĭĨÌńwñøÇĭĨÌńwñøÇĭĐ'}];\r\ny='ABCDEFGHIJKLMNOPQRSTUVWXYZ';\r\nfor k=1:26\r\n  assert(isequal(deCipher(double(x{k})-100,19),y(k)));\r\nend\r\n%%\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":145982,"edited_by":145982,"edited_at":"2025-10-23T11:07:00.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":"2025-10-23T01:21:29.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2025-10-22T22:43:16.000Z","updated_at":"2026-06-06T06:03:32.000Z","published_at":"2025-10-23T01:21:29.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eProvided a message with all characters \u0026lt;= 255 (ascii) as an input, decode the padded message (removing the random character pad), XORing input converted to binary vector (8 bits each) with the inputted prime having primitive root of two as the key. The key is the repeated sequence of 2^x modulo prime key input starting from x=0 until the cycle repeats (unpadded zero binary represented vector of the repeated sequence). After XORing, remove the padded front and return the character array as output.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":53064,"title":"Amazing circle of numbers 1 to n","description":"For given natural number n, create amazing circle of numbers 1 to n without a repeat.\r\nThis circle is that the sum of any two adjacent numbers is a perfect square.\r\nFor example, if n = 32,\r\n\r\nSo, output is\r\n                          [1 8 28 21 4 32 17 19 30 6 3 13 12 24 25 11 5 31 18 7 29 20 16 9 27 22 14 2 23 26 10 15]\r\nIf the condition is satisfied, it is the correct answer regardless of the order of the vectors.\r\nIf there is no amazing circle vector, return empty vector [].","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 984px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 492px; transform-origin: 407px 492px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eFor given natural number n, create amazing circle of numbers 1 to n without a repeat.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThis circle is that the sum of any two adjacent numbers is a perfect square.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eFor example, if n = 32,\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 774px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 387px; text-align: left; transform-origin: 384px 387px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" width=\"768\" height=\"768\" style=\"vertical-align: baseline;width: 768px;height: 768px\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eSo, output is\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e                          [1 8 28 21 4 32 17 19 30 6 3 13 12 24 25 11 5 31 18 7 29 20 16 9 27 22 14 2 23 26 10 15]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eIf the condition is satisfied, it is the correct answer regardless of the order of the vectors.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eIf there is no amazing circle vector, return empty vector [].\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function v = amazing_circle(n)\r\n  v = n;\r\nend","test_suite":"%%\r\nn=32;\r\nv = amazing_circle(n);\r\nv = v(1:n);\r\nflag = false;\r\nif length(unique(v)) == n \u0026 (min(v) == 1) \u0026 (max(v) == n)\r\n    for i = 1:n-1\r\n        lst(i) = mod(sqrt(v(i)+v(i+1)), 1) == 0;\r\n    end\r\n    lst(n) = mod(sqrt(v(n)+v(1)), 1) == 0;\r\n    if sum(lst) == n\r\n        flag = true;\r\n    end\r\nend\r\nassert(isequal(flag,true))\r\n\r\n%%\r\nn=33;\r\nv = amazing_circle(n);\r\nv = v(1:n);\r\nflag = false;\r\nif length(unique(v)) == n \u0026 (min(v) == 1) \u0026 (max(v) == n)\r\n    for i = 1:n-1\r\n        lst(i) = mod(sqrt(v(i)+v(i+1)), 1) == 0;\r\n    end\r\n    lst(n) = mod(sqrt(v(n)+v(1)), 1) == 0;\r\n    if sum(lst) == n\r\n        flag = true;\r\n    end\r\nend\r\nassert(isequal(flag,true))\r\n\r\n%%\r\nn=34;\r\nv = amazing_circle(n);\r\nv = v(1:n);\r\nflag = false;\r\nif length(unique(v)) == n \u0026 (min(v) == 1) \u0026 (max(v) == n)\r\n    for i = 1:n-1\r\n        lst(i) = mod(sqrt(v(i)+v(i+1)), 1) == 0;\r\n    end\r\n    lst(n) = mod(sqrt(v(n)+v(1)), 1) == 0;\r\n    if sum(lst) == n\r\n        flag = true;\r\n    end\r\nend\r\nassert(isequal(flag,true))\r\n\r\n%%\r\nn=35;\r\nv = amazing_circle(n);\r\nflag = false;\r\nif length(unique(v)) == n \u0026 (min(v) == 1) \u0026 (max(v) == n)\r\n    for i = 1:n-1\r\n        lst(i) = mod(sqrt(v(i)+v(i+1)), 1) == 0;\r\n    end\r\n    lst(n) = mod(sqrt(v(n)+v(1)), 1) == 0;\r\n    if sum(lst) == n\r\n        flag = true;\r\n    end\r\nend\r\nassert(isequal(flag,true))\r\n\r\n%%\r\nn=36;\r\nv = amazing_circle(n);\r\nv = v(1:n);\r\nflag = false;\r\nif length(unique(v)) == n \u0026 (min(v) == 1) \u0026 (max(v) == n)\r\n    for i = 1:n-1\r\n        lst(i) = mod(sqrt(v(i)+v(i+1)), 1) == 0;\r\n    end\r\n    lst(n) = mod(sqrt(v(n)+v(1)), 1) == 0;\r\n    if sum(lst) == n\r\n        flag = true;\r\n    end\r\nend\r\nassert(isequal(flag,true))\r\n\r\n%%\r\nn=37;\r\nv = amazing_circle(n);\r\nv = v(1:n);\r\nflag = false;\r\nif length(unique(v)) == n \u0026 (min(v) == 1) \u0026 (max(v) == n)\r\n    for i = 1:n-1\r\n        lst(i) = mod(sqrt(v(i)+v(i+1)), 1) == 0;\r\n    end\r\n    lst(n) = mod(sqrt(v(n)+v(1)), 1) == 0;\r\n    if sum(lst) == n\r\n        flag = true;\r\n    end\r\nend\r\nassert(isequal(flag,true))\r\n\r\n%%\r\nn=38;\r\nv = amazing_circle(n);\r\nv = v(1:n);\r\nflag = false;\r\nif length(unique(v)) == n \u0026 (min(v) == 1) \u0026 (max(v) == n)\r\n    for i = 1:n-1\r\n        lst(i) = mod(sqrt(v(i)+v(i+1)), 1) == 0;\r\n    end\r\n    lst(n) = mod(sqrt(v(n)+v(1)), 1) == 0;\r\n    if sum(lst) == n\r\n        flag = true;\r\n    end\r\nend\r\nassert(isequal(flag,true))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":517609,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":7,"test_suite_updated_at":"2021-11-29T05:26:18.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2021-11-15T02:36:58.000Z","updated_at":"2026-06-01T00:06:15.000Z","published_at":"2021-11-15T04:44:57.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor given natural number n, create amazing circle of numbers 1 to n without a repeat.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis circle is that the sum of any two adjacent numbers is a perfect square.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, if n = 32,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"768\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"768\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSo, output is\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e                          [1 8 28 21 4 32 17 19 30 6 3 13 12 24 25 11 5 31 18 7 29 20 16 9 27 22 14 2 23 26 10 15]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf the condition is satisfied, it is the correct answer regardless of the order of the vectors.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf there is no amazing circle vector, return empty vector 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":53980,"title":"Determine whether a player solved a Cody problem","description":"Write a function to determine whether a player has solved the specified Cody problems. The problem numbers are specified as a vector, and the player names are specified either as a character vector for a single name or a cell array of character vectors for multiple names. Because some names are not unique, the player IDs are included as well. Return 1 if the player solved the problem, 0 if the player did not solve the problem, and -1 if the problem does not exist. If there are  players and  problems, the first output should be an  matrix. The second output should be a cell array of the problem titles.\r\nI plan to check the tests regularly as players solve more problems. If you spot a problem, please feel free to contact me through my profile page. ","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 156px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 78px; transform-origin: 407px 78px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 105px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 52.5px; text-align: left; transform-origin: 384px 52.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to determine whether a player has solved the specified Cody problems. The problem numbers are specified as a vector, and the player names are specified either as a character vector for a single name or a cell array of character vectors for multiple names. Because some names are not unique, the player IDs are included as well. Return 1 if the player solved the problem, 0 if the player did not solve the problem, and -1 if the problem does not exist. If there are \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003em\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e players and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: STIXGeneral, STIXGeneral-webfont, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e problems, the first output should be an \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"37.5\" height=\"18\" alt=\"mxn\" style=\"width: 37.5px; height: 18px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e matrix. The second output should be a cell array of the problem titles.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eI plan to check the tests regularly as players solve more problems. If you spot a problem, please feel free to contact me through \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/profile/authors/1887879\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003emy profile page\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [y,CPtitle] = isSolved(CPnum,players)\r\n% CPnum    1xn vector of problem numbers\r\n% players  either a character vector or a 1xm cell array of player names\r\n% y        mxn matrix of results (1 = solved, 0 = not solved, -1 = problem does not exist)\r\n% CPtitle  cell array of problem titles\r\n\r\ny = randi(3)-2;\r\nCPtitle = 'Verb the adjective noun';\r\nend","test_suite":"%% \r\nn  = 44340:44345;\r\np  = 'ChrisR';\r\nID = 1887879;\r\n[y,t] = isSolved(n,p,ID);\r\ny_correct = [1 1 1 0 1 1];\r\nt_correct = {'Recaman Sequence - III','Hexagonal numbers on a spiral matrix','Spot the First Occurrence of 5','Pair Primes','The 5th Root','MATLAB Counter'};\r\nassert(isequal(y,y_correct))\r\nassert(isequal(t,t_correct))\r\n\r\n%%\r\nn  = 2100:2105;\r\np  = 'ChrisR';\r\nID = 1887879;\r\n[y,t] = isSolved(n,p,ID);\r\ny_correct = [1 -1 -1 0 1 -1];\r\nt_correct = {'distance to a straight line (2D) given any 2 distinct points on this straight line','Simple Robotics 1: On track?','construct matrix with identical rows'};\r\nassert(isequal(y,y_correct))\r\nassert(isequal(t(y~=-1),t_correct))\r\n\r\n%% Grab bag\r\nn  = [46031 46053 46114 46573 47415 51451 51675 51803];\r\np  = {'William','Tim','David Hill','Nikolaos Nikolaou','Dyuman Joshi','Ramon Villamangca'};\r\nID = [173294 496166 10491973 7310613 2873 13897958];\r\n[y,t] = isSolved(n,p,ID);\r\ny_correct = [1 0 1 1 1 1 1 1; 1 1 1 1 1 0 1 0; 0 0 0 1 1 1 1 1; 0 0 0 0 0 0 1 0; 0 0 1 1 1 0 1 0; 0 0 0 0 0 0 1 0];\r\nt_correct = {'Construct dimensionless parameters','Construct finite difference approximations of derivatives','Compute the date of a special marriage milestone','Determine the winner of a goofy golf tournament','List ways to reach a target sum',['Guess the card in Fitch Cheney’s five-card trick'],'Add 100','Eliminate redundant numbers in text'};\r\nassert(isequal(y,y_correct))\r\nassert(isequal(t,t_correct))\r\n\r\n%% My targets (2022/01/29)\r\nn  = [193 375 782 2523 42834 45272 45274 47623];\r\np  = 'ChrisR';\r\nID = 1887879;\r\n[y,~] = isSolved(n,p,ID);   \r\n%assert(all(y==0));\r\ny_correct = [1 0 0 1 1 1 1 0];   %  2022/12/29\r\nassert(isequal(y,y_correct))\r\n\r\n%% Problems with \u003e9 likes that I haven't solved as of 2022/01/29 \r\nn = [283 3075 42888 42996 43670 44376 44630];      % CP 375 is in the target list\r\np = {'William','Tim','David Hill','Nikolaos Nikolaou','Mehmet OZC','minnolina','Dyuman Joshi','Ramon Villamangca','ChrisR'};\r\nID = [173294 496166 10491973 7310613 3877541 9646924 12862873 13897958 1887879];\r\n[y,~] = isSolved(n,p,ID);\r\ny_correct = [1 1 1 1 1 1 1; 1 1 1 0 0 1 1; 0 0 0 1 1 0 0; 0 1 0 1 1 0 1; 0 1 0 1 0 0 0; 0 0 0 0 0 0 0; 0 1 0 0 1 0 0; 0 0 1 0 0 0 0; 0 1 1 1 1 0 1];  %  Updated 2022/04/24\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nfiletext = fileread('isSolved.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'assert'); \r\nassert(~illegal)","published":true,"deleted":false,"likes_count":2,"comments_count":6,"created_by":46909,"edited_by":46909,"edited_at":"2024-11-02T15:08:42.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":"2024-11-02T15:08:42.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2022-01-29T16:08:07.000Z","updated_at":"2026-06-01T00:34:35.000Z","published_at":"2022-01-29T16:17:43.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to determine whether a player has solved the specified Cody problems. The problem numbers are specified as a vector, and the player names are specified either as a character vector for a single name or a cell array of character vectors for multiple names. Because some names are not unique, the player IDs are included as well. Return 1 if the player solved the problem, 0 if the player did not solve the problem, and -1 if the problem does not exist. If there are \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"m\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003em\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e players and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"n\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e problems, the first output should be an \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"mxn\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003em\\\\times n\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e matrix. The second output should be a cell array of the problem titles.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI plan to check the tests regularly as players solve more problems. If you spot a problem, please feel free to contact me through \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/profile/authors/1887879\\\"\u003e\u003cw:r\u003e\u003cw:t\u003emy profile page\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":53995,"title":"Solve an ODE: nonlinear third-order equation","description":"Write a function to solve the ordinary differential equation\r\n\r\non the domain  with , , and either  or . The input variable ord indicates the order (either 1 or 2) of the specified derivative. The function should return the values of  at the requested values of the independent variable .","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 124px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407.5px 62px; transform-origin: 407.5px 62px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384.5px 10.5px; text-align: left; transform-origin: 384.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 176.35px 7.66667px; transform-origin: 176.35px 7.66667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to solve the ordinary differential equation\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384.5px 10.5px; text-align: left; transform-origin: 384.5px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAI4AAAAkCAYAAABfegKAAAAEYElEQVR4Xu1bOYsWQRDd/QkekRh5BEaKKCYaGHklgoKKIAbiFRiJogYGoqJoIuIBBpuICoKRogYaaCIaaGTgERp54C/Q92BqaXr7m+7pr3q+GrYHHgvf9FR3v3pTXdU9Oz1Vr8pABgPTGc/URyoDU1U4VQRZDFThZNFWH6rCqRrIYqAKJ4u2+lBp4TwHxSuADcCvAnTH7C9Dny+BD8DeAv1bNanByylM7gqwBvjkT7SkcFajs4/AH2BlAeGk2D+Cfu8AL4CtVr2sPC4tXn5jXAuATcDbPoVzEZ2dBS4B55TJobkU++/Rbh2wA3hWYAwWTWrwsh0TewowUq8PTbJkxPmKDhc2jvtegOGYfS5T39omX2BMFkxq8HILEznW9sKVEo44rVS0SbHPnObBPIs2WrxQfEwxgtGGb0cp4TC3uFww2qTYf4j+l7dN3kJ4UB6DBi+SI7Uu76WEw6z+CXBXmRgxl2L/HxoHK4JCY7JgVoMXVlNrgdYqNEU4i2BkFfAZ8EtqhsYl3j22fwd0KcE3ov0PwM+FpG/3Xop9JncHYpO34OnIGCbBCwuKQ8CcEtwd6yjhcMDbACZILMl4HQ1EECnZWO7ub4RFx1I0bVUMBbcbOAxwOeH1KOBoLjd7AK63rI4orFT7FPScMjLiKLktgk1sPrJZ6GVos2mBF0Ya8t56xSKOvN10LisUP4pI9s1OFjfCifXp3xdx8Hd/aZEEl/f6LKn54rzpOpFA+9DLlmrWIi+zY48Jhw1d54U2gygeZt8jM/AIU1IJsNlp4KrXXvrPFWaqo9x2TBC5azrudQMGcvePLPLSSTiMOj+bJ0LlNTec/gYc3oV02agLbTgxXzkBzJedX5c3s7ykRBxORMImHUsHukkya/5dQGsyFVGRHA2wGZdFN0lmRKPtUhVaF4H33dYsL6nCcSfg5iFcRnYC4x4gyt4BHePmBQzXFCt3oOfjZZaXVOG4E9jXRCA6UiPaiCCkQruNH443P04q2kyqqgq9HJZ4mR1fqnD4gD8BRqHNCtFGBuMvh0tx4/WEoo2FqsoiL1nC8R37BVZkb0VjGXGXQ1ZQ94EZJ7pp9JFqw0JVJWO1xEuWcGQC3Izjx1Hc19H8XMJdDrlcjVPipwpkCO1M8tJlqXLDN0XDL/u0L54vyeVXV9p9DcmeOV66CIdEywRK7eLykG4LUOpzjCGJxR2rOV5yhONWPdqOkG9lS0Qz7bH2ac8cL12Ew6VqptASRSewBNZOuPt0bqm+TPKSKhzZiNOsonyiub1+HYiezJbykFG7JnlJEQ4Vz1B5Hsg9sIv5hBt9rNY0q7RYn0O4b5aXkHB4fHAQeAU8BvidzD1A66xI9kh4HnUNOAnwSGHcY4shCKFtjIPiJSQcyeBlku4Rg4Zz5B+9xFbJZFtjvH3ZGBQvIeFQ+WcAHjEwImj/awuXvgtNlLmJv7lf6fXl0L76GRQvKTlOX8TVfgbEQBXOgJxlaahVOJa8MaCx/Ac8ViI0v+/5OwAAAABJRU5ErkJggg==\" alt=\"y'y'' = y'''\" style=\"width: 71px; height: 18px;\" width=\"71\" height=\"18\"\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 64px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384.5px 32px; text-align: left; transform-origin: 384.5px 32px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 46.2917px 7.66667px; transform-origin: 46.2917px 7.66667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eon the domain \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"0 \u003c= x \u003c= 1\" style=\"width: 62.5px; height: 18px;\" width=\"62.5\" height=\"18\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 16.3333px 7.66667px; transform-origin: 16.3333px 7.66667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e with \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"y(0) = y0\" style=\"width: 60px; height: 20px;\" width=\"60\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.88333px 7.66667px; transform-origin: 3.88333px 7.66667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"y(1) = y1\" style=\"width: 60px; height: 20px;\" width=\"60\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 36.95px 7.66667px; transform-origin: 36.95px 7.66667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, and either \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"y'(0) = y'0\" style=\"width: 70px; height: 20px;\" width=\"70\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 10.1083px 7.66667px; transform-origin: 10.1083px 7.66667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e or \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"y''(0) = y''0\" style=\"width: 78.5px; height: 20px;\" width=\"78.5\" height=\"20\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 61.4583px 7.66667px; transform-origin: 61.4583px 7.66667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e. The input variable \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 11.55px 7.66667px; transform-origin: 11.55px 7.66667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; perspective-origin: 11.55px 8px; transform-origin: 11.55px 8px; \"\u003eord\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 31.5083px 7.66667px; transform-origin: 31.5083px 7.66667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e indicates the order (either 1 or 2) of the specified derivative. The function should return the values of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-5px\"\u003e\u003cimg src=\"data:image/png;base64,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\" alt=\"y(x)\" style=\"width: 30px; height: 18.5px;\" width=\"30\" height=\"18.5\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 84.4px 7.66667px; transform-origin: 84.4px 7.66667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e at the requested values of the independent variable \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ex\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.94167px 7.66667px; transform-origin: 1.94167px 7.66667px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = solve3ordNLODE(x,y0,y1,yd,ord)\r\n%  x = requested values of the independent variable\r\n%  y = values of function at the requested values of x\r\n%  y0, y1 = values of the function at x = 0 and x = 1\r\n%  yd = value of either y'(0) or y''(0)\r\n%  ord = 1 if yd is the first derivative or 2 if it is the second derivative\r\n\r\n   y = A1*exp(r1*x)+A2*exp(r2*x)+A3*exp(r3*x);\r\nend","test_suite":"%%\r\ny0 = 2.231252941;\r\ny1 = 6.297567308;\r\nyd = 1.557407725;\r\nord = 1;\r\nx  = [0.3 0.5 0.8];\r\ny_correct = [2.790588219 3.308319582 4.544300275];\r\nassert(all(abs(solve3ordNLODE(x,y0,y1,yd,ord)-y_correct)\u003c1e-6))\r\n\r\n%%\r\ny0 = 2.231252941;\r\ny1 = 6.297567308;\r\nyd = 1.71275941;\r\nord = 2;\r\nx  = [0.3 0.5 0.8];\r\ny_correct = [2.790588219 3.308319582 4.544300275];\r\nassert(all(abs(solve3ordNLODE(x,y0,y1,yd,ord)-y_correct)\u003c1e-6))\r\n\r\n%%\r\ny0 = 3.287682072;\r\ny1 = 9.107569130;\r\nyd = 1.154700538;\r\nord = 1;\r\nx  = 0.25:0.25:0.75;\r\ny_correct = [3.669824691 4.306714708 5.456245713];\r\nassert(all(abs(solve3ordNLODE(x,y0,y1,yd,ord)-y_correct)\u003c1e-6))\r\n\r\n%%\r\ny0 = 3.287682072;\r\ny1 = 9.107569130;\r\nyd = 8/3;\r\nord = 2;\r\nx  = 0.25:0.25:0.75;\r\ny_correct = [3.669824691 4.306714708 5.456245713];\r\nassert(all(abs(solve3ordNLODE(x,y0,y1,yd,ord)-y_correct)\u003c1e-6))\r\n\r\n%%\r\ny0 = 3.693147181;\r\ny1 = 3.046411846;\r\nyd = -2;\r\nord = 1;\r\nx  = 0.2:0.2:0.8;\r\ny_correct = [3.36426198 3.152361229 3.034571214 3.000213221];\r\nassert(all(abs(solve3ordNLODE(x,y0,y1,yd,ord)-y_correct)\u003c1e-6))\r\n\r\n%%\r\ny0 = 0.722781494;\r\ny1 = 2.637280183;\r\nyd = 1.030077779;\r\nord = 2;\r\nx  = 0.2:0.2:0.8;\r\ny_correct = [0.950884887 1.231252941 1.581096155 2.030246566];\r\nassert(all(abs(solve3ordNLODE(x,y0,y1,yd,ord)-y_correct)\u003c1e-6))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":46909,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-02-07T03:08:13.000Z","updated_at":"2026-06-01T00:31:01.000Z","published_at":"2022-02-07T03:11:39.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to solve the ordinary differential equation\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"y'y'' = y'''\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ey\\\\prime y\\\\prime\\\\prime = y\\\\prime\\\\prime\\\\prime\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eon the domain \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"0 \u0026lt;= x \u0026lt;= 1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e0 \\\\le x \\\\le 1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e with \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"y(0) = y0\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ey(0) = y_0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"y(1) = y1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ey(1) = y_1\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, and either \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"y'(0) = y'0\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ey\\\\prime(0) = y\\\\prime\\n_0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e or \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"y''(0) = y''0\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ey\\\\prime\\\\prime(0) = y\\\\prime\\n\\\\prime_0\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e. The input variable \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eord\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e indicates the order (either 1 or 2) of the specified derivative. The function should return the values of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"y(x)\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ey(x)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e at the requested values of the independent variable \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"x\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ex\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":58827,"title":"Troubles With Spaces - Convert Some Messy Data Into A Clean Array","description":"I have a bunch of messy data I have to go through. The data is stored in an odd format, so str2num isn't working. And even when I get it to work, it is still too slow!\r\nI need you to turn the strings into numbers. Any '+++++++' values are values that overloaded the data collection device and should be set to NaN.\r\n'- 6.496' =\u003e -6.496\r\n'-10.430' =\u003e -10.430\r\n'+++++++' =\u003e NaN\r\n'+11.664' =\u003e 11.664\r\nOh, and output it into an array because I hate working with cells.  And there's a lot of data so it had better be fast!","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 215.795px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 406.989px 107.898px; transform-origin: 406.996px 107.898px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42.017px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 21.0085px; text-align: left; transform-origin: 383.999px 21.0085px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eI have a bunch of messy data I have to go through. The data is stored in an odd format, so str2num isn't working. And even when I get it to work, it is still too slow!\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42.017px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 383.991px 21.0085px; text-align: left; transform-origin: 383.999px 21.0085px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eI need you to turn the strings into numbers. Any '+++++++' values are values that overloaded the data collection device and should be set to NaN.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 81.7614px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 403.991px 40.8807px; transform-origin: 403.999px 40.8807px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 403.991px 10.2131px; text-wrap: nowrap; transform-origin: 403.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'- 6.496' \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e=\u0026gt; -6.496\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 403.991px 10.2131px; text-wrap: nowrap; transform-origin: 403.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'-10.430' \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e=\u0026gt; -10.430\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 403.991px 10.2131px; text-wrap: nowrap; transform-origin: 403.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'+++++++' \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e=\u0026gt; NaN\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4403px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.909091px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.909091px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.909091px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.909091px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 403.991px 10.2131px; text-wrap: nowrap; transform-origin: 403.999px 10.2202px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); text-decoration: none; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); \"\u003e'+11.664' \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e=\u0026gt; 11.664\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0085px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 383.991px 10.4972px; text-align: left; transform-origin: 383.999px 10.5043px; white-space-collapse: preserve; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eOh, and output it into an array because I hate working with cells.  And there's a lot of data so it had better be fast!\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = str2numBetter(x)\r\n  y = str2num(cell2mat(x));\r\nend","test_suite":"%%\r\n% This first test should cover every possible case.\r\nx = {'- 6.496';'-10.430';'- 0.493';'+++++++';'+ 6.949';'- 5.368';'+13.214';'+11.664'};\r\nfinalData = [-6.496;-10.430;-0.493';NaN;+6.949;-5.368;+13.214;+11.664];\r\n\r\nassert(isequaln(str2numBetter(x),finalData))\r\n\r\n%%\r\nrng shuffle\r\nn = 30000; % Something small to start you off with.\r\nrNums=10*rand([n 1]);\r\nrNums(rNums\u003e9.99)=NaN;\r\nstr=char(sprintfc('%.3f', rNums, false));\r\nneg=2*randi([0 1],[n 1]);\r\ntens=randi([0 1],[n 1]);\r\nstr = [char(neg+'+') char(17*tens+' ') str];\r\nplus=('+');\r\nstr(isnan(rNums),:)=plus(ones(nnz(isnan(rNums)),7));\r\nx=mat2cell(str,ones(1,n),7);\r\ntic; y=str2numBetter(x); toc % The code I wrote took 0.005872 seconds to run\r\nfinalData = round(-1*(neg-1).*(rNums+10*tens),3);\r\n\r\nassert(isequaln(y,finalData))\r\n\r\n%%\r\nrng shuffle\r\nn = 12000000; % Yes! I have this much data!!!\r\nrNums=10*rand([n 1]);\r\nrNums(rNums\u003e9.99)=NaN; % Real world values overflow\r\nstr=char(sprintfc('%.3f', rNums, false)); % This is what num2str uses\r\nneg=2*randi([0 1],[n 1]); % Some numbers will be random\r\ntens=randi([0 1],[n 1]); % Some numbers will be \u003e10, but in an odd format \r\nstr = [char(neg+'+') char(17*tens+' ') str]; % Add the weird 10's place and multiply by negatives\r\nplus=('+');\r\nstr(isnan(rNums),:)=plus(ones(nnz(isnan(rNums)),7)); % Replace NaN with something more annoying\r\nx=mat2cell(str,ones(1,n),7); % The data is in cells, not an array!\r\ntic; y=str2numBetter(x); toc % The code I wrote takes about 3.5 seconds to run, with a size of 130.\r\nfinalData = round(-1*(neg-1).*(rNums+10*tens),3);\r\n\r\nassert(isequaln(y,finalData))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":3115675,"edited_by":3115675,"edited_at":"2023-08-08T14:27:45.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":"2023-08-08T14:27:45.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2023-08-07T21:52:56.000Z","updated_at":"2026-06-04T20:25:23.000Z","published_at":"2023-08-07T22:00:31.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI have a bunch of messy data I have to go through. The data is stored in an odd format, so str2num isn't working. And even when I get it to work, it is still too slow!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eI need you to turn the strings into numbers. Any '+++++++' values are values that overloaded the data collection device and should be set to NaN.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA['- 6.496' =\u003e -6.496\\n'-10.430' =\u003e -10.430\\n'+++++++' =\u003e NaN\\n'+11.664' =\u003e 11.664]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOh, and output it into an array because I hate working with cells.  And there's a lot of data so it had better be fast!\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":58951,"title":"Neural Net: Convolutional NN wK Evolve Matrices Determination for LED Digit images; Variant Images Scored","description":"This challenge is to return final WH, WP, and K matrices, given initial XImgset, WH, WP, K, EPY using convolution(Ximg,K), ReLU on the hidden layer and Softmax on the output layer. Training images are LED binary matrices of digits 1:9 and 0. Sets of variants will be tested to show success beyond the training set. (Scale, Offset, Noise, zeroed node(s), intra-shifts).\r\nBack Propagation on WH and WP. Custom K evolve algorithm provided. Standard Back Propagation for K in future. \r\n\r\nTest sets 2 thru 5 show various Pixelations of numbers and the Neural Net success rate. Just fun to look at the data.\r\nAppears more training sets required and possibly more nodes to have high success. One translate not good.\r\n[WP,WH,dK]=ConvNN_Process(XImgset,K,WH,WP,EPY)\r\n \r\nThe matlab Latex code for making the CNN Processing chart included in template.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 781.5px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 390.75px; transform-origin: 407px 390.75px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 383px 8px; transform-origin: 383px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis challenge is to return final WH, WP, and K matrices, given initial XImgset, WH, WP, K, EPY using convolution(Ximg,K), ReLU on the hidden layer and Softmax on the output layer. Training images are LED binary matrices of digits 1:9 and 0. Sets of variants will be tested to show success beyond the training set. (Scale, Offset, Noise, zeroed node(s), intra-shifts).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 362.5px 8px; transform-origin: 362.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eBack Propagation on WH and WP. Custom K evolve algorithm provided. Standard Back Propagation for K in future. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 8px; transform-origin: 0px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 363px 8px; transform-origin: 363px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTest sets 2 thru 5 show various Pixelations of numbers and the Neural Net success rate. Just fun to look at the data.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 342px 8px; transform-origin: 342px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAppears more training sets required and possibly more nodes to have high success. One translate not good.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 173px 8px; transform-origin: 173px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"border-block-end-style: solid; border-block-end-width: 1px; border-bottom-style: solid; border-bottom-width: 1px; perspective-origin: 2px 8.5px; transform-origin: 2px 8.5px; \"\u003e[\u003c/span\u003e\u003cspan style=\"perspective-origin: 33.5px 8px; transform-origin: 33.5px 8px; \"\u003eWP,WH,dK\u003c/span\u003e\u003cspan style=\"border-block-end-style: solid; border-block-end-width: 1px; border-bottom-style: solid; border-bottom-width: 1px; perspective-origin: 2px 8.5px; transform-origin: 2px 8.5px; \"\u003e]\u003c/span\u003e\u003cspan style=\"perspective-origin: 135.5px 8px; transform-origin: 135.5px 8px; \"\u003e=ConvNN_Process(XImgset,K,WH,WP,EPY)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 529.5px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 264.75px; text-align: left; transform-origin: 384px 264.75px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline;width: 875px;height: 524px\" 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\" data-image-state=\"image-loaded\" width=\"875\" height=\"524\"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 8px; transform-origin: 0px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 257px 8px; transform-origin: 257px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe matlab Latex code for making the CNN Processing chart included in template.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function [WP,WH,dK]=ConvNN_Process(XImg,Kbase,WH,WP,EPY)\r\n %K=Kbase; % Required to calc dK\r\n %nX=size(XImg,3); %Number training images\r\n %nK=size(K,3); % Number kernels\r\n %XC=zeros(5,3,nK); %Pre-alloc for speed\r\n %nClasses=size(EPY,2);\r\n \r\n for epoch=1:2000 % Number of cycles to evolve WH and WP\r\n  dWH=0*WH;\r\n  dWP=0*WP;\r\n  for iCase=1:nX  %Cycle thru all Images while accumulating dWH and dWP\r\n %Forward Pass  X(7,5,nX)-Ximg,Conv-XC(5,3,nK),ReLU-XCY3(5,3,nK),XCY(1,nK*15+1)(1,91)\r\n % XCYV*WH-H(1,21), ReLU(H)-HY(1,21), HY*WP-P(1,10), Softmax(P)-PY\r\n   for k=1:nK\r\n    %XC(:,:,k)=(conv2(XImg(:,:,iCase),K(:,:,k),'valid')); %Use rot90 Kernel to make output align\r\n   end\r\n\r\n   %XCY3=max(0,XC); %ReLU  Not needed with all K coeff positive; safer to leave in for K offset case\r\n   %XCY=[XCY3(:)' 1]; % 1,nK*15+1  (1,91)  add a 1 for offset term\r\n \r\n   %[dWPi,dWHi]=Neural_ReLU_Back_Propagation(XCY,WH,WP,EPY(iCase,:));\r\n \r\n %Accumulate across all sample cases\r\n   %dWP=dWP+dWPi;\r\n   %dWH=dWH+dWHi;\r\n \r\n  end % iCase\r\n  \r\n  dKi=evolve_Kraz(epoch,XImg,WH,WP,K,nX,nK,nClasses);\r\n  K=K+dKi;\r\n  \r\n\r\n  %WP=WP+dWP/nX; % dWP/nX  Valid\r\n  %WH=WH+dWH/nX; % Usage /nX^2 requires 10K epochs for \u003e.8 PYs\r\n\r\n end % epoch\r\n dK=Kbase-K;\r\nend %ConvNN_Process\r\n\r\nfunction [dWP,dWH]=Neural_ReLU_Back_Propagation(X,WH,WP,EPY)\r\n% X will be single case [X1 X2 ... XN 1]\r\n  LR=1; % Learning Rate\r\n  \r\n  H=X*WH;\r\n  HY=max(0,H); % ReLU\r\n  \r\n  P=HY*WP ; %\r\n  PY=exp(P)./sum(exp(P),2); %Softmax [PY1 PY2 ... PYM]\r\n  \r\n  %Back Propagation Calculations\r\n  ErrPY=EPY-PY;\r\n  \r\n  dPY=PY.*(1-PY).*ErrPY;\r\n  dWP=LR*HY'.*dPY;\r\n  \r\n  dHY=HY.*(1-HY).*sum(WP.*dPY,2)';\r\n  dWH=LR*X'.*dHY;\r\nend % Neural_ReLU_Back_Propagation\r\n\r\nfunction [dK]=evolve_Kraz(epoch,XImgset,WH,WP,K,nX,nK,nClasses)\r\n%This is not standard back propagation for K.\r\n%This is a much slower raz original K evolve of 1 pixel per kernel by small delta\r\n%selected based upon full PY matrix of training\r\n%Random start does better than pre-defined features\r\n%Singular pixel required for stability. Small step size .0001\r\n%Kernel Forward sensitivity and Update based on +/- Sum prediction direction\r\n%Harder to make work than expected\r\n%Have not solved standard back propagation for defined X,WH,WP,K dimensions\r\n\r\n dK=0*K;\r\n XC=zeros(5,3,nK);\r\n if ~mod(epoch,10)==0,return;end % Option to reduce K update rate due to Slowness\r\n PYmask=-ones(nX,nClasses)+10*eye(nClasses); % One sample per class, Know it is 10\r\n        %Wt factor of 9 for truth diag and -1 for all non-truth answers;\r\n mXCY=zeros(nX,91);\r\n%Create Baseline mPY (nX,10)\r\n for iCase=1:nX  %Cycle thru all Images create mXCY\r\n  for k=1:nK\r\n   XC(:,:,k)=(conv2(XImgset(:,:,iCase),K(:,:,k),'valid')); %Need to rot90 Kernel to make output align\r\n  end\r\n  XCY3=max(0,XC); %ReLU  Not needed with all K coeff positive\r\n  mXCY(iCase,:)=[XCY3(:)' 1]; % 1,nK*15  1,91  add a 1 for offset term\r\n end\r\n mPY=Neural_ReLU_Forward(mXCY,WH,WP);\r\n\r\n for pK=1:nK %For each Kernel tweak one pixel +.1 then -.1 to see PY response ALL Images\r\n  for rK=1:3\r\n   for cK=1:3\r\n    eK=K;\r\n   \r\n    eK(rK,cK,pK)=K(rK,cK,pK)+.1;\r\n    for iCase=1:nX  %Cycle thru all Images create mXCY\r\n     for k=1:nK\r\n      XC(:,:,k)=(conv2(XImgset(:,:,iCase),eK(:,:,k),'valid')); %Need to rot90 Kernel to make output align\r\n     end\r\n     XCY3=max(0,XC); %ReLU  Not needed with all K coeff positive\r\n     mXCY(iCase,:)=[XCY3(:)' 1]; % 1,nK*15  1,91  add a 1 for offset term\r\n    end\r\n    \r\n    mPYK=Neural_ReLU_Forward(mXCY,WH,WP); %Performance All Training for single Pixel Tweaked\r\n    mPYD=mPYK-mPY;\r\n    dsump=sum(sum(PYmask.*mPYD)); %Improvement\r\n    if dsump\u003c0, dsump=0;end\r\n    dK(rK,cK,pK)=dsump; \r\n   \r\n    eK(rK,cK,pK)=K(rK,cK,pK)-.2; % Net -.1\r\n    for iCase=1:nX  %Cycle thru all Images create mXCY\r\n     for k=1:nK\r\n      XC(:,:,k)=(conv2(XImgset(:,:,iCase),eK(:,:,k),'valid')); %Need to rot90 Kernel to make output align\r\n     end\r\n     XCY3=max(0,XC); %ReLU  Not needed with all K coeff positive\r\n     mXCY(iCase,:)=[XCY3(:)' 1]; % 1,nK*15  1,91  add a 1 for offset term\r\n    end\r\n    \r\n    [mPYK]=Neural_ReLU_Forward(mXCY,WH,WP); %Performance All Training for single Pixel Tweaked\r\n    mPYD=mPYK-mPY;\r\n    dsumn=sum(sum(PYmask.*mPYD)); %Improvement\r\n    if dsumn\u003c0, dsumn=0;end\r\n    if dsumn\u003edsump\r\n     dK(rK,cK,pK)=-dsumn;\r\n    end\r\n  end % cK\r\n end%rK\r\nend % pK\r\n\r\n%Adjust only one pixel per kernel\r\n for k=1:nK\r\n  mK=dK(:,:,k);\r\n  [kr,kc]=find(abs(mK)==max(abs(mK(:))),1,'first');\r\n  dK(:,:,k)=0;\r\n  dK(kr,kc,k)=.001*sign(mK(kr,kc));\r\n end\r\n\r\nend %Evolve_K\r\n\r\nfunction [PY]=Neural_ReLU_Forward(X,WH,WP)\r\n%raz ReLU/Softmax\r\n  n=1;\r\n  \r\n  H=X*WH;\r\n%   HY=1./(1+exp(-H)); %[HY1 HY2 1]  .6803 .6637 1 Sigmoid\r\n%   HY(:,end)=1; %Sigmoid\r\n  HY=max(0,H);\r\n  \r\n  P=HY*WP ; %\r\n  PY=exp(P)./sum(exp(P),2); %Softmax [PY1 PY2]\r\n  \r\nend % Neural_ReLU_Forward\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","test_suite":"%%\r\nglobal WH WP K\r\nImgsetstr= ... %5x3 digits 1:9 0\r\n ['010111111101111100111111111111';\r\n  '010001001101100100001101101101';\r\n  '010111111111111111001111111101';\r\n  '010100001001001101001101001101';\r\n  '010111111001111111001111001111'];\r\nImgsetv=Imgsetstr-'0';\r\n\r\nnX=size(Imgsetv,2)/3;\r\nXImgset=zeros(7,5,nX); % Images Zero Ring for conv2(I,K,'valid') where K is 3x3\r\n\r\nnClasses=10;\r\nEPY=eye(nClasses); % One case for each digit from 1 to 9 and then 0 which is class type 10\r\n%One input for each Class\r\n\r\nKsetstr= ... %3x3 kernel mask (will be scaled/offset)\r\n ['000000010010010000'; %Centered corners and vert/horiz\r\n  '011110011110010111'; %Kernels are identifying Features\r\n  '010010000000010000'];\r\n\r\nKsetv=Ksetstr-'0';\r\nKh=size(Ksetv,1);\r\nnK=size(Ksetv,2)/Kh; %Number of Kernels\r\n%Kset=reshape(Ksetv,Kh,Kh,nK);\r\n%Kset=rand(Kh,Kh,nK); % Using Random Start\r\n%Kernel needs normalization either scale or offset; Two large scale slows convergence or possible fail\r\n%Ksetr90=rot90(Kset(:,:,:),2)/3; % Rotate and normalize 3, each kernel sum is 3 Valid  2-Fails\r\n%Ksetr90=(rot90(Kset(:,:,:),2)-.5)/5; % Rotate and offset -0.5  Valid \r\n%Kbase=Ksetr90;\r\nIh=size(Imgsetv,1);\r\nImgset53=reshape(Imgsetv,Ih,3,size(Imgsetv,2)/3);\r\nfor k=1:size(Imgset53,3)\r\n XImgset(2:end-1,2:end-1,k)=Imgset53(:,:,k);\r\nend\r\n\r\n%Stability sensitive to WP and WH values; rand start needs offset and normalization\r\n% X(7,5,nX) XC(5,3,nK) (XCY(1,91) WH(91,21) WP(21,10)\r\nztic=tic;\r\nfor ztry=1:5 %Allow three processing attempts to get a working solution\r\n Kset=rand(Kh,Kh,nK); % Using Random Starts\r\n Ksetr90=(rot90(Kset(:,:,:),2)-.5)/5; % Rotate and offset -0.5  and scaled\r\n Kbase=Ksetr90;\r\n WH=[(rand(90,20)-.5)/sqrt(90+20) zeros(90,1)]; % 90=nK*15, 20 is arbitrary at 2*nCases WH(91,21)  Valid\r\n WH=[WH; zeros(1,20) 1]; % offset row, Start with zero bias\r\n\r\nWP=(rand(21,10)-.5)/sqrt(21+10);  % 10 output states  Valid\r\n\r\n[WP,WH,dK]=ConvNN_Process(XImgset,Kbase,WH,WP,EPY);\r\nK=Kbase-dK;\r\nfprintf('Process Time: %.3f sec\\n\\n',toc(ztic));\r\n\r\n  X=zeros(nX,91); %Forward process all images in one sequence\r\n  for iCase=1:nX\r\n   for k=1:nK\r\n    XC(:,:,k)=(conv2(XImgset(:,:,iCase),K(:,:,k),'valid')); %Need to rot90 Kernel to make output align\r\n   end\r\n   XCY3=max(0,XC); %ReLU  Not needed with all K coeff positive\r\n   XCY=[XCY3(:)' 1]; % 1,nK*15  1,91  add a 1 for offset term\r\n   X(iCase,:)=XCY; \r\n  end\r\n\r\n  H=X*WH; % Process all 10 cases\r\n  HY=max(0,H);\r\n  P=HY*WP ; %\r\n  PY=exp(P)./sum(exp(P),2); %Softmax [PY1 ... PY10]\r\n  [~,pcase]=max(PY,[],2);\r\n  [~,pexp]=max(EPY,[],2);\r\n  \r\n  fprintf('Ideal Input Training Cases\\n');\r\n  for i=1:nX\r\n   fprintf('Exp:%2i Pred:%2i   ',pexp(i),pcase(i));\r\n   fprintf('.%04i  ',round(10000*PY(i,:)));fprintf('\\n');\r\n  end\r\n  fprintf('Accuracy %.0f%s\\n\\n',100*nnz(pcase==pexp)/nX,'%');\r\n  %fprintf('\\n\\n');\r\n  if ~isequal(pcase,pexp),continue;end %Bad random draw, retry\r\n  \r\n  %Scaled-50%\r\n  X=zeros(nX,91); %Forward process all images in one sequence\r\n  XN=XImgset/2; %Scale to 7x5 Image planes\r\n  XNS=XN; %figure variable\r\n  for iCase=1:nX\r\n   for k=1:nK\r\n    XC(:,:,k)=(conv2(XN(:,:,iCase),K(:,:,k),'valid'));\r\n   end\r\n   XCY3=max(0,XC); %ReLU  Not needed with all K coeff positive\r\n   XCY=[XCY3(:)' 1]; % 1,nK*15  1,91  add a 1 for offset term\r\n   X(iCase,:)=XCY; \r\n  end\r\n\r\n  H=X*WH; % Process all 10 cases\r\n  HY=max(0,H);\r\n  P=HY*WP ; %\r\n  PY=exp(P)./sum(exp(P),2); %Softmax [PY1 ... PY10]\r\n  [~,Xpcase]=max(PY,[],2);\r\n  [~,Xpexp]=max(EPY,[],2);\r\n  \r\n  fprintf('50%s Scale - NN Strong Scale Invariance, Worst is 8\\n',char(37));\r\n  for i=1:nX\r\n   fprintf('Exp:%2i Pred:%2i   ',Xpexp(i),Xpcase(i));\r\n   fprintf('.%04i  ',round(10000*PY(i,:)));fprintf('\\n');\r\n  end\r\n  fprintf('Accuracy %.0f%s\\n\\n',100*nnz(Xpcase==Xpexp)/nX,'%');\r\n  \r\n  %Scaled-50% and Noise 60% added\r\n  X=zeros(nX,91); %Forward process all images in one sequence\r\n  XN=XImgset/2+.3*(rand(size(XImgset))-.5); %Scale and add Noise to 7x5 Image planes\r\n  XNSn=XN; %figure variable\r\n  for iCase=1:nX\r\n   for k=1:nK\r\n    XC(:,:,k)=(conv2(XN(:,:,iCase),K(:,:,k),'valid'));\r\n   end\r\n   XCY3=max(0,XC); %ReLU  Not needed with all K coeff positive\r\n   XCY=[XCY3(:)' 1]; % 1,nK*15  1,91  add a 1 for offset term\r\n   X(iCase,:)=XCY; \r\n  end\r\n\r\n  H=X*WH; % Process all 10 cases\r\n  HY=max(0,H);\r\n  P=HY*WP ; %\r\n  PY=exp(P)./sum(exp(P),2); %Softmax [PY1 ... PY10]\r\n  [~,Xpcase]=max(PY,[],2);\r\n  [~,Xpexp]=max(EPY,[],2);\r\n  \r\n  fprintf('50%s Scale and 60%s Noise added - Imperfect Results Expected\\n',char(37),'%');\r\n  %Note: double % is a special Cody sequence so using %s to write % character\r\n  for i=1:nX\r\n   fprintf('Exp:%2i Pred:%2i   ',Xpexp(i),Xpcase(i));\r\n   fprintf('.%04i  ',round(10000*PY(i,:)));fprintf('\\n');\r\n  end\r\n  fprintf('Accuracy %.0f%s\\n\\n',100*nnz(Xpcase==Xpexp)/nX,'%');\r\n  \r\n\r\n  %Offset+50%\r\n  X=zeros(nX,91); %Forward process all images in one sequence\r\n  XN=XImgset-.5; %Offset to 7x5 Image planes\r\n  XNo=XN;\r\n  for iCase=1:nX\r\n   for k=1:nK\r\n    XC(:,:,k)=(conv2(XN(:,:,iCase),K(:,:,k),'valid'));\r\n   end\r\n   XCY3=max(0,XC); %ReLU  Not needed with all K coeff positive\r\n   XCY=[XCY3(:)' 1]; % 1,nK*15  1,91  add a 1 for offset term\r\n   X(iCase,:)=XCY; \r\n  end\r\n\r\n  H=X*WH; % Process all 10 cases\r\n  HY=max(0,H);\r\n  P=HY*WP ; %\r\n  PY=exp(P)./sum(exp(P),2); %Softmax [PY1 ... PY10]\r\n  [~,Xpcase]=max(PY,[],2);\r\n  [~,Xpexp]=max(EPY,[],2);\r\n  \r\n  fprintf('50%s Offset - NN Somewhat Strong Offset Invariance - Weak 1,3,8,9\\n',char(37));\r\n  for i=1:nX\r\n   fprintf('Exp:%2i Pred:%2i   ',Xpexp(i),Xpcase(i));\r\n   fprintf('.%04i  ',round(10000*PY(i,:)));fprintf('\\n');\r\n  end\r\n  fprintf('Accuracy %.0f%s\\n\\n',100*nnz(Xpcase==Xpexp)/nX,'%');\r\n  \r\n  %Offset-50% Noise-60%\r\n  X=zeros(nX,91); %Forward process all images in one sequence\r\n  XN=XImgset-.5+.6*(rand(size(XImgset))-.5); %Offset to 7x5 Image planes\r\n  XNon=XN;\r\n  for iCase=1:nX\r\n   for k=1:nK\r\n    XC(:,:,k)=(conv2(XN(:,:,iCase),K(:,:,k),'valid'));\r\n   end\r\n   XCY3=max(0,XC); %ReLU  Not needed with all K coeff positive\r\n   XCY=[XCY3(:)' 1]; % 1,nK*15  1,91  add a 1 for offset term\r\n   X(iCase,:)=XCY; \r\n  end\r\n\r\n  H=X*WH; % Process all 10 cases\r\n  HY=max(0,H);\r\n  P=HY*WP ; %\r\n  PY=exp(P)./sum(exp(P),2); %Softmax [PY1 ... PY10]\r\n  [~,Xpcase]=max(PY,[],2);\r\n  [~,Xpexp]=max(EPY,[],2);\r\n  \r\n  fprintf('50%s Offset 60%s Noise - NN Mildly Strong Offset/Noise Invariance - Weak 1,3,8,9\\n',char(37),'%');\r\n  for i=1:nX\r\n   fprintf('Exp:%2i Pred:%2i   ',Xpexp(i),Xpcase(i));\r\n   fprintf('.%04i  ',round(10000*PY(i,:)));fprintf('\\n');\r\n  end\r\n  fprintf('Accuracy %.0f%s\\n\\n',100*nnz(Xpcase==Xpexp)/nX,'%');\r\n  \r\n\r\n  figure(1); imagesc([reshape(XImgset,7,[]);reshape(XNS,7,[]);reshape(XNSn,7,50); ...\r\n                      reshape(XNo,7,[]);reshape(XNon,7,50)]);\r\n  %xticks([]);yticks([]);axis equal;axis tight\r\n  \r\n  %figure(1); imagesc([reshape(XImgset,7,[]);reshape(XNS,7,[]);reshape(XNSn,7,50);reshape(XNo,7,[])]);\r\n  xticks([]);yticks([]);axis equal;axis tight\r\n  \r\n  figure(2); imagesc([reshape(Kbase,3,[]);zeros(1,18);reshape(K,3,[]);zeros(1,18); ...\r\n                      reshape(-dK,3,[])]);\r\n  xticks([]);yticks([]);axis equal;axis tight\r\n  \r\n  if isequal(pcase,pexp),break;end\r\n  \r\nend % ztry  Allowed 5 trials as rand may not always converge\r\n  \r\nvalid=isequal(pcase,pexp);\r\nassert(valid)\r\n\r\n%%\r\nglobal WH WP K\r\nImgsetstr= ... %5x3 digits 1 variants,3 variants\r\n ['100001010010000011110011111011';\r\n  '100001010110010011110001001001';\r\n  '100001010010010011010111011011';\r\n  '100001010010010011010001001001';\r\n  '100001111010010011010111111011'];\r\nImgsetv=Imgsetstr-'0';\r\npexp=[1 1 1 1 1 1 1 3 3 3]';\r\n\r\nnX=size(Imgsetv,2)/3;\r\nXImgset=zeros(7,5,nX); % Images Zero Ring for conv2(I,K,'valid') where K is 3x3\r\n\r\n%nClasses=10;\r\n%EPY=eye(nClasses); % One case for each digit from 1 to 9 and then 0 which is class type 10\r\n%One input for each Class\r\n\r\nnK=size(K,3); %Number of Kernels\r\n\r\nIh=size(Imgsetv,1);\r\nImgset53=reshape(Imgsetv,Ih,3,size(Imgsetv,2)/3);\r\nfor k=1:size(Imgset53,3)\r\n XImgset(2:end-1,2:end-1,k)=Imgset53(:,:,k);\r\nend\r\n\r\nfigure; imagesc(reshape(XImgset,7,[]));\r\nxticks([]);yticks([]);axis equal;axis tight\r\n\r\n  X=zeros(nX,91); %Forward process all images in one sequence\r\n  for iCase=1:nX\r\n   for k=1:nK\r\n    XC(:,:,k)=(conv2(XImgset(:,:,iCase),K(:,:,k),'valid')); %Need to rot90 Kernel to make output align\r\n   end\r\n   XCY3=max(0,XC); %ReLU  Not needed with all K coeff positive\r\n   XCY=[XCY3(:)' 1]; % 1,nK*15  1,91  add a 1 for offset term\r\n   X(iCase,:)=XCY; \r\n  end\r\n\r\n  H=X*WH; % Process all 10 cases\r\n  HY=max(0,H);\r\n  P=HY*WP ; %\r\n  PY=exp(P)./sum(exp(P),2); %Softmax [PY1 ... PY10]\r\n  [~,pcase]=max(PY,[],2);\r\n  %[~,pexp]=max(EPY,[],2);\r\n  \r\n  fprintf('Morphed 1s and 3s; Need more training\\n');\r\n  for i=1:nX\r\n   fprintf('Exp:%2i Pred:%2i   ',pexp(i),pcase(i));\r\n   fprintf('.%04i  ',round(10000*PY(i,:)));fprintf('\\n');\r\n  end\r\n  fprintf('Accuracy %.0f%s\\n',100*nnz(pcase==pexp)/nX,'%');\r\n  fprintf('\\n\\n');\r\n\r\nassert(1)\r\n\r\n\r\n%%\r\nglobal WH WP K\r\nImgsetstr= ... %5x3 digits 2 variants,4 variants\r\n ['011011100111110001000101101001';\r\n  '001001010001001101101111101011';\r\n  '111111001010010111111001101111';\r\n  '100100010100100001001001111001';\r\n  '111110001111011001001001001001'];\r\nImgsetv=Imgsetstr-'0';\r\npexp=[2 2 2 2 2 4 4 4 4 4]';\r\n\r\nnX=size(Imgsetv,2)/3;\r\nXImgset=zeros(7,5,nX); % Images Zero Ring for conv2(I,K,'valid') where K is 3x3\r\n\r\n%nClasses=10;\r\n%EPY=eye(nClasses); % One case for each digit from 1 to 9 and then 0 which is class type 10\r\n%One input for each Class\r\n\r\nnK=size(K,3); %Number of Kernels\r\n\r\nIh=size(Imgsetv,1);\r\nImgset53=reshape(Imgsetv,Ih,3,size(Imgsetv,2)/3);\r\nfor k=1:size(Imgset53,3)\r\n XImgset(2:end-1,2:end-1,k)=Imgset53(:,:,k);\r\nend\r\n\r\nfigure; imagesc(reshape(XImgset,7,[]));\r\nxticks([]);yticks([]);axis equal;axis tight\r\n\r\n  X=zeros(nX,91); %Forward process all images in one sequence\r\n  for iCase=1:nX\r\n   for k=1:nK\r\n    XC(:,:,k)=(conv2(XImgset(:,:,iCase),K(:,:,k),'valid')); %Need to rot90 Kernel to make output align\r\n   end\r\n   XCY3=max(0,XC); %ReLU  Not needed with all K coeff positive\r\n   XCY=[XCY3(:)' 1]; % 1,nK*15  1,91  add a 1 for offset term\r\n   X(iCase,:)=XCY; \r\n  end\r\n\r\n  H=X*WH; % Process all 10 cases\r\n  HY=max(0,H);\r\n  P=HY*WP ; %\r\n  PY=exp(P)./sum(exp(P),2); %Softmax [PY1 ... PY10]\r\n  [~,pcase]=max(PY,[],2);\r\n  %[~,pexp]=max(EPY,[],2);\r\n  \r\n  fprintf('Morphed 1s and 3s; Need more training\\n');\r\n  for i=1:nX\r\n   fprintf('Exp:%2i Pred:%2i   ',pexp(i),pcase(i));\r\n   fprintf('.%04i  ',round(10000*PY(i,:)));fprintf('\\n');\r\n  end\r\n  fprintf('Accuracy %.0f%s\\n',100*nnz(pcase==pexp)/nX,'%');\r\n  fprintf('\\n\\n');\r\n\r\nassert(1)\r\n\r\n\r\n%%\r\n\r\nglobal WH WP K\r\nImgsetstr= ... %5x3 digits 5,6,7,8 variants\r\n ['110110010111110100011111010010';\r\n  '100100100100100100001001101100';\r\n  '111111111111111110001001010010';\r\n  '001001001101101101001010101001';\r\n  '111011010111111110001100010010'];\r\nImgsetv=Imgsetstr-'0';\r\npexp=[5 5 5 6 6 6 7 7  8 5 ]';\r\n\r\nnX=size(Imgsetv,2)/3;\r\nXImgset=zeros(7,5,nX); % Images Zero Ring for conv2(I,K,'valid') where K is 3x3\r\n\r\n%nClasses=10;\r\n%EPY=eye(nClasses); % One case for each digit from 1 to 9 and then 0 which is class type 10\r\n%One input for each Class\r\n\r\nnK=size(K,3); %Number of Kernels\r\n\r\nIh=size(Imgsetv,1);\r\nImgset53=reshape(Imgsetv,Ih,3,size(Imgsetv,2)/3);\r\nfor k=1:size(Imgset53,3)\r\n XImgset(2:end-1,2:end-1,k)=Imgset53(:,:,k);\r\nend\r\n\r\nfigure; imagesc(reshape(XImgset,7,[]));\r\nxticks([]);yticks([]);axis equal;axis tight\r\n\r\n  X=zeros(nX,91); %Forward process all images in one sequence\r\n  for iCase=1:nX\r\n   for k=1:nK\r\n    XC(:,:,k)=(conv2(XImgset(:,:,iCase),K(:,:,k),'valid')); %Need to rot90 Kernel to make output align\r\n   end\r\n   XCY3=max(0,XC); %ReLU  Not needed with all K coeff positive\r\n   XCY=[XCY3(:)' 1]; % 1,nK*15  1,91  add a 1 for offset term\r\n   X(iCase,:)=XCY; \r\n  end\r\n\r\n  H=X*WH; % Process all 10 cases\r\n  HY=max(0,H);\r\n  P=HY*WP ; %\r\n  PY=exp(P)./sum(exp(P),2); %Softmax [PY1 ... PY10]\r\n  [~,pcase]=max(PY,[],2);\r\n  %[~,pexp]=max(EPY,[],2);\r\n  \r\n  fprintf('Morphed 1s and 3s; Need more training\\n');\r\n  for i=1:nX\r\n   fprintf('Exp:%2i Pred:%2i   ',pexp(i),pcase(i));\r\n   fprintf('.%04i  ',round(10000*PY(i,:)));fprintf('\\n');\r\n  end\r\n  fprintf('Accuracy %.0f%s\\n',100*nnz(pcase==pexp)/nX,'%');\r\n  fprintf('\\n\\n');\r\n\r\nassert(1)\r\n\r\n\r\n%%\r\nglobal WH WP K\r\nImgsetstr= ... %5x3 digits 7,9,0 variants\r\n ['110111111011111010010111101101';\r\n  '010101101101111101000001101100';\r\n  '010111111111111101010011111111';\r\n  '010001001001111101000001101001';\r\n  '010011111001111010010001111001'];\r\nImgsetv=Imgsetstr-'0';\r\npexp=[1 9 9 9 10 10 1 7  8 4 ]'; %Shape/Missing LEDs\r\n\r\nnX=size(Imgsetv,2)/3;\r\nXImgset=zeros(7,5,nX); % Images Zero Ring for conv2(I,K,'valid') where K is 3x3\r\n\r\n%nClasses=10;\r\n%EPY=eye(nClasses); % One case for each digit from 1 to 9 and then 0 which is class type 10\r\n%One input for each Class\r\n\r\nnK=size(K,3); %Number of Kernels\r\n\r\nIh=size(Imgsetv,1);\r\nImgset53=reshape(Imgsetv,Ih,3,size(Imgsetv,2)/3);\r\nfor k=1:size(Imgset53,3)\r\n XImgset(2:end-1,2:end-1,k)=Imgset53(:,:,k);\r\nend\r\n\r\nfigure; imagesc(reshape(XImgset,7,[]));\r\nxticks([]);yticks([]);axis equal;axis tight\r\n\r\n  X=zeros(nX,91); %Forward process all images in one sequence\r\n  for iCase=1:nX\r\n   for k=1:nK\r\n    XC(:,:,k)=(conv2(XImgset(:,:,iCase),K(:,:,k),'valid')); %Need to rot90 Kernel to make output align\r\n   end\r\n   XCY3=max(0,XC); %ReLU  Not needed with all K coeff positive\r\n   XCY=[XCY3(:)' 1]; % 1,nK*15  1,91  add a 1 for offset term\r\n   X(iCase,:)=XCY; \r\n  end\r\n\r\n  H=X*WH; % Process all 10 cases\r\n  HY=max(0,H);\r\n  P=HY*WP ; %\r\n  PY=exp(P)./sum(exp(P),2); %Softmax [PY1 ... PY10]\r\n  [~,pcase]=max(PY,[],2);\r\n  %[~,pexp]=max(EPY,[],2);\r\n  \r\n  fprintf('More training needed\\n');\r\n  for i=1:nX\r\n   fprintf('Exp:%2i Pred:%2i   ',pexp(i),pcase(i));\r\n   fprintf('.%04i  ',round(10000*PY(i,:)));fprintf('\\n');\r\n  end\r\n  fprintf('Accuracy %.0f%s\\n',100*nnz(pcase==pexp)/nX,'%');\r\n  fprintf('\\n\\n');\r\n\r\nassert(1)\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":3097,"edited_at":"2023-09-03T16:21:37.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2023-09-03T03:05:26.000Z","updated_at":"2026-06-04T20:55:33.000Z","published_at":"2023-09-03T16:21:37.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis challenge is to return final WH, WP, and K matrices, given initial XImgset, WH, WP, K, EPY using convolution(Ximg,K), ReLU on the hidden layer and Softmax on the output layer. Training images are LED binary matrices of digits 1:9 and 0. Sets of variants will be tested to show success beyond the training set. (Scale, Offset, Noise, zeroed node(s), intra-shifts).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBack Propagation on WH and WP. Custom K evolve algorithm provided. Standard Back Propagation for K in future. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTest sets 2 thru 5 show various Pixelations of numbers and the Neural Net success rate. Just fun to look at the data.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAppears more training sets required and possibly more nodes to have high success. One translate not good.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[WP,WH,dK]=ConvNN_Process(XImgset,K,WH,WP,EPY)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"524\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"875\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe matlab Latex code for making the CNN Processing chart included in 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":59541,"title":"Compute the polylogarithm for real arguments","description":"The polylogarithm  appears in quantum statistics and quantum electrodynamics, and for special cases of  and , it connects to the logarithm, ratios of polynomials, the zeta function, the Dirichlet eta function, and other functions. \r\nWrite a function to compute the polylogarithm  for real arguments. ","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 74px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 37px; transform-origin: 407px 37px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 43px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21.5px; text-align: left; transform-origin: 384px 21.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 57.575px 8px; transform-origin: 57.575px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe polylogarithm \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"39\" height=\"20\" alt=\"Li_n(z)\" style=\"width: 39px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 267.225px 8px; transform-origin: 267.225px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e appears in quantum statistics and quantum electrodynamics, and for special cases of \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 15.5583px 8px; transform-origin: 15.5583px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e and \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003ez\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 9.325px 8px; transform-origin: 9.325px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, it connects to the logarithm, ratios of polynomials, the \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/45988\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"text-decoration-line: underline; \"\u003ezeta\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 1.94167px 8px; transform-origin: 1.94167px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/45997\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"text-decoration-line: underline; \"\u003efunction\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 146.242px 8px; transform-origin: 146.242px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, the Dirichlet eta function, and other functions. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 22px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 11px; text-align: left; transform-origin: 384px 11px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 142.233px 8px; transform-origin: 142.233px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to compute the polylogarithm \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"vertical-align:-6px\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"39\" height=\"20\" alt=\"Li_n(z)\" style=\"width: 39px; height: 20px;\"\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 62.6167px 8px; transform-origin: 62.6167px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e for real arguments. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = polylogarithm(n,z)\r\n  y = sum(z^k/k^n);\r\nend","test_suite":"%%\r\nn = 2:2:16;\r\nz = 1;\r\ny = arrayfun(@(m) polylogarithm(m,z),n);\r\ny_correct = pi.^n.*[1/6 1/90 1/945 1/9450 1/93555 691/638512875 2/18243225 3617/325641566250];\r\nassert(all(abs(y-y_correct)\u003c1e-12))\r\n\r\n%%\r\nn = 3;\r\nz = 1;\r\ny = polylogarithm(n,z);\r\ny_correct = 1.202056903159594;\r\nassert(abs(y-y_correct)\u003c1e-12)\r\n\r\n%%\r\nn = 0;\r\nz = 1./(10:-1:2);\r\ny = arrayfun(@(s) polylogarithm(n,s),z);\r\ny_correct = 1./(9:-1:1);\r\nassert(all(abs(y-y_correct)\u003c1e-12))\r\n\r\n%%\r\nn = 1;\r\nz = [-2 -3/2 -1 -1/2 0 1/10 1/7 1/5 1/3 1/2];\r\ny = arrayfun(@(s) polylogarithm(n,s),z);\r\ny_correct = [-1.098612288668110 -0.916290731874155 -0.693147180559945 -0.405465108108164 0 0.105360515657826 0.154150679827258 0.223143551314210 0.405465108108164 0.693147180559945];\r\nassert(all(abs(y-y_correct)\u003c1e-12))\r\n\r\n%%\r\nn = -1;\r\nz = [-2 -3/2 -1 -1/2 1/7 1/5 1/2];\r\ny = arrayfun(@(s) polylogarithm(n,s),z);\r\ny_correct = [-2/9 -6/25 -1/4 -2/9 7/36 5/16 2];\r\nassert(all(abs(y-y_correct)\u003c1e-12))\r\n\r\n%%\r\nn = -2;\r\nz = [-2 -3/2 -1 -1/2 0 1/17 1/13 1/11 1/5];\r\ny = arrayfun(@(s) polylogarithm(n,s),z);\r\ny_correct = [2/27 6/125 0 -2/27 0 153/2048 91/864 33/250 15/32];\r\nassert(all(abs(y-y_correct)\u003c1e-12))\r\n\r\n%%\r\nn = 1:3;\r\nz = 1/2;\r\ny = arrayfun(@(m) polylogarithm(m,z),n);\r\nzeta3 = 1.202056903159594;\r\ny_correct = [log(2) (pi^2-6*log(2)^2)/12 (4*log(2)^3-2*pi^2*log(2)+21*zeta3)/24];\r\nassert(all(abs(y-y_correct)\u003c1e-12))\r\n\r\n%%\r\nn = 2;\r\nz = -1;\r\ny = polylogarithm(n,z);\r\ny_correct = -pi^2/12;\r\nassert(abs(y-y_correct)\u003c1e-12)\r\n\r\n%%\r\nn = -1/pi;\r\nz = exp(-1);\r\ny = polylogarithm(n,z);\r\ny_correct = 0.6541056465726233;\r\nassert(abs(y-y_correct)\u003c1e-12)\r\n\r\n%%\r\nn = 4;\r\nz = 1/2;\r\ny = polylogarithm(n,z);\r\ny_correct = 0.5174790616738994;\r\nassert(abs(y-y_correct)\u003c1e-12)\r\n\r\n%%\r\nn = 1/2;\r\nz = 1/2;\r\ny = polylogarithm(n,z);\r\ny_correct = 0.8061267230428522;\r\nassert(abs(y-y_correct)\u003c1e-12)\r\n\r\n%%\r\nn = -3/2;\r\nz = -1./2.^(1:4);\r\ny = arrayfun(@(s) polylogarithm(n,s),z);\r\ny_correct = [-0.1516441361365191 -0.1313580923579523 -0.0892942267818043 -0.05260782861980105];\r\nassert(all(abs(y-y_correct)\u003c1e-12))\r\n\r\n%%\r\nn = 5*randn(1,randi(10));\r\nz = 0;\r\ny = arrayfun(@(m) polylogarithm(m,z),n);\r\ny_correct = zeros(size(n));\r\nassert(all(abs(y-y_correct)\u003c1e-12))\r\n\r\n%%\r\nfiletext = fileread('polylogarithm.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'assert') || contains(filetext,'regexp'); \r\nassert(~illegal)","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":46909,"edited_by":46909,"edited_at":"2024-01-07T03:51:00.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2024-01-06T17:30:34.000Z","updated_at":"2026-06-05T00:31:59.000Z","published_at":"2024-01-06T17:31:22.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe polylogarithm \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"Li_n(z)\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e{\\\\rm Li}_n(z)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e appears in quantum statistics and quantum electrodynamics, and for special cases of \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e and \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003ez\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e, it connects to the logarithm, ratios of polynomials, the \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/45988\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ezeta\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/45997\\\"\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:u/\u003e\u003c/w:rPr\u003e\u003cw:t\u003efunction\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, the Dirichlet eta function, and other functions. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to compute the polylogarithm \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"Li_n(z)\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e{\\\\rm Li}_n(z)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e for real arguments. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":59079,"title":"The Hacker Parole Problem","description":"The hacker parole problem\r\n\r\n100 hackers have been imprisoned, but have been given a way to get parole.\r\nEach of the 100 prisoners has been given a number.  There is also a room with 100 numbered boxes.  Each box contains a number.  Each prisoner goes into the room and has 50 tries to find their number in a box.\r\nIf all 100 prisoners can find their number within 50 tries, then they all get parole.  But, if one prisoner cannot find their number within 50 tries all the hackers go back to prison for another year.\r\nThe hackers meet and devise an algorithm that has better than a 30 percent chance of going free that year.\r\nThe hacker's solution\r\nIt is obvious that having each prisoner randomly open boxes will fail.  Each prisoner has a 0.5 chance of finding their number and so the chance that all 100 are able to do it is 0.5^100.  Essentially giving a 0 probablility.\r\nThe hacker solution is to have each prisoner go into the room and use their number to find a box.  They open the box, and if their number is in it they are done.  If their number is not in it, then they use the number in the box to choose another box.  They continue following the numbers and opening boxes until they find the box with their number on it.\r\nThis solution will free all the prisoners over 30% of the time.  This is because the random numbers in boxes form loops.  For example if we have five boxes and five number we might see this set of numbers stored in boxes.\r\n\r\nThe hacker holding number 3 would open box 3 and find a 4.  Then the hacker would open box 4 and find a 1.  Then the hacker would open box 1 and find a 3. \r\n\r\nThis solution works because numbers stored this way always form at least one loop.  In this case we have the following loops:\r\n\r\n3 -\u003e 4 -\u003e 1 -\u003e 3\r\n5 -\u003e 2 -\u003e 5\r\n\r\nThe hackers know that they will get their freedom if the longest loop among the 100 boxes is 50 boxes or shorter.  This happens 30% of the time, thus they get their freedom 30% of the time.\r\nTesting the solution\r\nWe want to test the hacker's algorithm by running 10,000 trials and seeing how often the hackers won their freedom.  Math says that the number should be greater than or equal to 3000 times in 10,000 trials.\r\nYour assignment is to write a function named runTrial() that takes a vector of boxes in the room and runs a trial of the hacker's algorithm. It returns whether the hackers found the number after looking at only half the boxes. The boxes are a vector that contains randomized numbers from 1:numBoxes.\r\nThe testbench will run your trial 10000 times and capture the number of times the hackers won their freedom. That number should be above 3000.\r\nThe function looks like this:\r\nfunction foundAll = runTrial(boxes)\r\n        %%  Insert your code here. I solved this problem in 11 lines inside the function\r\nend","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(33, 33, 33); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"block-size: 1078.6px; display: block; min-width: 0px; padding-block-start: 0px; padding-inline-start: 2px; padding-left: 2px; padding-top: 0px; perspective-origin: 408px 539.3px; transform-origin: 408px 539.3px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 20px; font-family: Helvetica, Arial, sans-serif; font-size: 20px; font-weight: 700; line-height: 20px; margin-block-end: 5px; margin-block-start: 3px; margin-bottom: 5px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 3px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10px; text-align: left; transform-origin: 384px 10px; white-space-collapse: preserve; margin-left: 4px; margin-top: 3px; margin-bottom: 5px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe hacker parole problem\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e100 hackers have been imprisoned, but have been given a way to get parole.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eEach of the 100 prisoners has been given a number.  There is also a room with 100 numbered boxes.  Each box contains a number.  Each prisoner goes into the room and has 50 tries to find their number in a box.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIf all 100 prisoners can find their number within 50 tries, then they all get parole.  But, if one prisoner cannot find their number within 50 tries all the hackers go back to prison for another year.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe hackers meet and devise an algorithm that has better than a 30 percent chance of going free that year.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20px; font-family: Helvetica, Arial, sans-serif; font-size: 20px; font-weight: 700; line-height: 20px; margin-block-end: 5px; margin-block-start: 20px; margin-bottom: 5px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 20px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10px; text-align: left; transform-origin: 384px 10px; white-space-collapse: preserve; margin-left: 4px; margin-top: 20px; margin-bottom: 5px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe hacker's solution\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eIt is obvious that having each prisoner randomly open boxes will fail.  Each prisoner has a 0.5 chance of finding their number and so the chance that all 100 are able to do it is 0.5^100.  Essentially giving a 0 probablility.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe hacker solution is to have each prisoner go into the room and use their number to find a box.  They open the box, and if their number is in it they are done.  If their number is not in it, then they use the number in the box to choose another box.  They continue following the numbers and opening boxes until they find the box with their number on it.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis solution will free all the prisoners over 30% of the time.  This is because the random numbers in boxes form loops.  For example if we have five boxes and five number we might see this set of numbers stored in boxes.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 44.6px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 22.3px; text-align: left; transform-origin: 384px 22.3px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"vertical-align:-15px\"\u003e\u003cimg 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\" width=\"114\" height=\"44.5\" style=\"width: 114px; height: 44.5px;\"\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe hacker holding number 3 would open box 3 and find a 4.  Then the hacker would open box 4 and find a 1.  Then the hacker would open box 1 and find a 3. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis solution works because numbers stored this way always form at least one loop.  In this case we have the following loops:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e3 -\u0026gt; 4 -\u0026gt; 1 -\u0026gt; 3\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e5 -\u0026gt; 2 -\u0026gt; 5\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe hackers know that they will get their freedom if the longest loop among the 100 boxes is 50 boxes or shorter.  This happens 30% of the time, thus they get their freedom 30% of the time.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 20px; font-family: Helvetica, Arial, sans-serif; font-size: 20px; font-weight: 700; line-height: 20px; margin-block-end: 5px; margin-block-start: 20px; margin-bottom: 5px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 20px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10px; text-align: left; transform-origin: 384px 10px; white-space-collapse: preserve; margin-left: 4px; margin-top: 20px; margin-bottom: 5px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTesting the solution\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWe want to test the hacker's algorithm by running 10,000 trials and seeing how often the hackers won their freedom.  Math says that the number should be greater than or equal to 3000 times in 10,000 trials.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 63px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 31.5px; text-align: left; transform-origin: 384px 31.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYour assignment is to write a function named \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003erunTrial()\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e that takes a vector of boxes in the room and runs a trial of the hacker's algorithm. It returns whether the hackers found the number after looking at only half the boxes. The boxes are a vector that contains randomized numbers from 1:numBoxes.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe testbench will run your trial 10000 times and capture the number of times the hackers won their freedom. That number should be above 3000.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe function looks like this:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 54px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 404px 27px; transform-origin: 404px 27px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.8px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.8px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.8px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.8px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 9px; text-wrap-mode: nowrap; transform-origin: 404px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"border-block-end-color: rgb(14, 0, 255); border-block-start-color: rgb(14, 0, 255); border-bottom-color: rgb(14, 0, 255); border-inline-end-color: rgb(14, 0, 255); border-inline-start-color: rgb(14, 0, 255); border-left-color: rgb(14, 0, 255); border-right-color: rgb(14, 0, 255); border-top-color: rgb(14, 0, 255); caret-color: rgb(14, 0, 255); color: rgb(14, 0, 255); column-rule-color: rgb(14, 0, 255); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(14, 0, 255); text-decoration-color: rgb(14, 0, 255); text-emphasis-color: rgb(14, 0, 255); \"\u003efunction \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003efoundAll = runTrial(boxes)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.8px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.8px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.8px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.8px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 9px; text-wrap-mode: nowrap; transform-origin: 404px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"border-block-end-color: rgb(0, 100, 0); border-block-start-color: rgb(0, 100, 0); border-bottom-color: rgb(0, 100, 0); border-inline-end-color: rgb(0, 100, 0); border-inline-start-color: rgb(0, 100, 0); border-left-color: rgb(0, 100, 0); border-right-color: rgb(0, 100, 0); border-top-color: rgb(0, 100, 0); caret-color: rgb(0, 100, 0); color: rgb(0, 100, 0); column-rule-color: rgb(0, 100, 0); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(0, 100, 0); text-decoration-color: rgb(0, 100, 0); text-emphasis-color: rgb(0, 100, 0); \"\u003e        %%  Insert your code here. I solved this problem in 11 lines inside the function\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 18px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.8px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.8px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.8px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.8px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; line-height: 18.004px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 9px; text-wrap-mode: nowrap; transform-origin: 404px 9px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(33, 33, 33); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(33, 33, 33); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(33, 33, 33); border-left-style: none; border-left-width: 0px; border-right-color: rgb(33, 33, 33); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; \"\u003e\u003cspan style=\"border-block-end-color: rgb(14, 0, 255); border-block-start-color: rgb(14, 0, 255); border-bottom-color: rgb(14, 0, 255); border-inline-end-color: rgb(14, 0, 255); border-inline-start-color: rgb(14, 0, 255); border-left-color: rgb(14, 0, 255); border-right-color: rgb(14, 0, 255); border-top-color: rgb(14, 0, 255); caret-color: rgb(14, 0, 255); color: rgb(14, 0, 255); column-rule-color: rgb(14, 0, 255); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(14, 0, 255); text-decoration-color: rgb(14, 0, 255); text-emphasis-color: rgb(14, 0, 255); \"\u003eend\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function foundAll = runTrial(boxes)\r\n  foundAll = true; % Set true only if the hackers find all the numbers\r\n                   % a number of trials equal to 1/2 the length of `boxes`.\r\nend","test_suite":"%% Test code\r\nnumTrials = 10000;\r\nnumBoxes = 100;\r\ntests = false([1,numTrials]);\r\nfor ii=1:numTrials\r\n   boxes = randperm(numBoxes);\r\n   tests(ii) = runTrial(boxes);\r\nend\r\ndisp([\"Number of times the hackers went free: \" nnz(tests)])\r\nproved_algorithm = nnz(tests) \u003e= numTrials * .30;\r\nassessVariableEqual('proved_algorithm', true);\r\ntoo_many_true = nnz(tests) \u003e= numTrials * .35;\r\nassessVariableEqual('too_many_true', false);","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":2842368,"edited_by":2842368,"edited_at":"2026-01-23T15:14:01.000Z","deleted_by":null,"deleted_at":null,"solvers_count":9,"test_suite_updated_at":"2023-10-08T19:02:17.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2023-10-08T18:58:11.000Z","updated_at":"2026-06-04T21:37:00.000Z","published_at":"2023-10-08T19:02:17.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"heading\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe hacker parole problem\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e100 hackers have been imprisoned, but have been given a way to get parole.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEach of the 100 prisoners has been given a number.  There is also a room with 100 numbered boxes.  Each box contains a number.  Each prisoner goes into the room and has 50 tries to find their number in a box.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf all 100 prisoners can find their number within 50 tries, then they all get parole.  But, if one prisoner cannot find their number within 50 tries all the hackers go back to prison for another year.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe hackers meet and devise an algorithm that has better than a 30 percent chance of going free that year.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"heading\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe hacker's solution\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIt is obvious that having each prisoner randomly open boxes will fail.  Each prisoner has a 0.5 chance of finding their number and so the chance that all 100 are able to do it is 0.5^100.  Essentially giving a 0 probablility.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe hacker solution is to have each prisoner go into the room and use their number to find a box.  They open the box, and if their number is in it they are done.  If their number is not in it, then they use the number in the box to choose another box.  They continue following the numbers and opening boxes until they find the box with their number on it.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis solution will free all the prisoners over 30% of the time.  This is because the random numbers in boxes form loops.  For example if we have five boxes and five number we might see this set of numbers stored in boxes.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"true\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003e\\\\left(\\\\begin{array}{cccccc} 1 \u0026amp; 2 \u0026amp; 3 \u0026amp; 4 \u0026amp; 5\\\\\\\\ 3 \u0026amp; 5 \u0026amp; 4 \u0026amp; 1 \u0026amp; 2 \\\\end{array}\\\\right)'\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe hacker holding number 3 would open box 3 and find a 4.  Then the hacker would open box 4 and find a 1.  Then the hacker would open box 1 and find a 3. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis solution works because numbers stored this way always form at least one loop.  In this case we have the following loops:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e3 -\u0026gt; 4 -\u0026gt; 1 -\u0026gt; 3\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e5 -\u0026gt; 2 -\u0026gt; 5\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe hackers know that they will get their freedom if the longest loop among the 100 boxes is 50 boxes or shorter.  This happens 30% of the time, thus they get their freedom 30% of the time.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"heading\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTesting the solution\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWe want to test the hacker's algorithm by running 10,000 trials and seeing how often the hackers won their freedom.  Math says that the number should be greater than or equal to 3000 times in 10,000 trials.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYour assignment is to write a function named \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003erunTrial()\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e that takes a vector of boxes in the room and runs a trial of the hacker's algorithm. It returns whether the hackers found the number after looking at only half the boxes. The boxes are a vector that contains randomized numbers from 1:numBoxes.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe testbench will run your trial 10000 times and capture the number of times the hackers won their freedom. That number should be above 3000.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe function looks like this:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[function foundAll = runTrial(boxes)\\n        %%  Insert your code here. I solved this problem in 11 lines inside the function\\nend]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":59526,"title":"Count paths between corners of a grid that remain on or below the diagonal","description":"Consider motion from the lower left corner of a square grid to the upper right corner. The motion is constrained in two ways: (1) the only legal moves are (0,1), (1,0), and (1,1)—i.e., right, up, and up-and-right between points of the lattice—and (2) the motion must remain on or below the line connecting the starting and finishing corners. For a 2x2 grid, the number of paths is 6, as shown below. \r\nWrite a function to count the legal paths for an x grid. Return the count as a string. Check the last test for banned functions.\r\n","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 273.867px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 136.933px; transform-origin: 407px 136.933px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 84px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 42px; text-align: left; transform-origin: 384px 42px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 383.125px 8px; transform-origin: 383.125px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eConsider motion from the lower left corner of a square grid to the upper right corner. The motion is constrained in two ways: (1) the only legal moves are (0,1), (1,0), and (1,1)—i.e., right, up, and up-and-right between points of the lattice—and (2) the motion must remain on or below the line connecting the starting and finishing corners. For a 2x2 grid, the number of paths is 6, as shown below. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 144.558px 8px; transform-origin: 144.558px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a function to count the legal paths for an \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 3.5px 8px; transform-origin: 3.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ex\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"font-family: \u0026quot;STIXGeneral\u0026quot;, \u0026quot;STIXGeneral-webfont\u0026quot;, serif; font-style: italic; font-weight: 400; color: rgb(0, 0, 0);\"\u003en\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 202.242px 8px; transform-origin: 202.242px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e grid. Return the count as a string. Check the last test for banned functions.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 129.867px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 64.9333px; text-align: left; transform-origin: 384px 64.9333px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cimg class=\"imageNode\" style=\"vertical-align: baseline\" 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = Schroeder(n)\r\n  y = factorial(n);\r\nend","test_suite":"%%\r\nn = 0;\r\ny = Schroeder(n);\r\ny_correct = '1';\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 2;\r\ny = Schroeder(n);\r\ny_correct = '6';\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 10;\r\ny = Schroeder(n);\r\ny_correct = '1037718';\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 17;\r\ny = Schroeder(n);\r\ny_correct = '111818026018';\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 23;\r\ny = Schroeder(n);\r\ny_correct = '2832923350929742';\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 53;\r\ny = Schroeder(n);\r\ny_correct = '77073779272885977020023254295915628266';\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 113;\r\ny = Schroeder(n);\r\ny_correct = '214645018815655382073160317691190772088909088782578589367620531418920165400447421666';\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 157;\r\ny = Schroeder(n);\r\ny_correct = '635298195989878453594162293551305562386458118544676764232073734715032717517063580963828995662005206580225562745343802';\r\nassert(isequal(y,y_correct))\r\n\r\n%%\r\nn = 88;\r\ny = Schroeder(n);\r\nyy = Schroeder(str2num(y(1:3)));\r\nyy_correct = '24528665763748049389685052543171711960129970876829498326068598388698578380240132802889284459385709363425133815992875744127843722179914611505186848090410102929094478101446';\r\nassert(isequal(yy,yy_correct))\r\n\r\n%%\r\nfiletext = fileread('Schroeder.m');\r\nillegal = contains(filetext, 'assignin') || contains(filetext, 'assert') || contains(filetext,'java') || contains(filetext,'py'); \r\nassert(~illegal)","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":46909,"edited_by":46909,"edited_at":"2024-01-04T04:53:07.000Z","deleted_by":null,"deleted_at":null,"solvers_count":4,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2024-01-04T04:42:17.000Z","updated_at":"2026-06-04T22:55:55.000Z","published_at":"2024-01-04T04:53:07.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eConsider motion from the lower left corner of a square grid to the upper right corner. The motion is constrained in two ways: (1) the only legal moves are (0,1), (1,0), and (1,1)—i.e., right, up, and up-and-right between points of the lattice—and (2) the motion must remain on or below the line connecting the starting and finishing corners. For a 2x2 grid, the number of paths is 6, as shown below. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a function to count the legal paths for an \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"n\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003ex\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:customXml w:element=\\\"equation\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"displayStyle\\\" w:val=\\\"false\\\"/\u003e\u003cw:attr w:name=\\\"altTextString\\\" w:val=\\\"n\\\"/\u003e\u003c/w:customXmlPr\u003e\u003cw:r\u003e\u003cw:t\u003en\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:customXml\u003e\u003cw:r\u003e\u003cw:t\u003e grid. Return the count as a string. 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Sequence","description":"Given a vector x, find the \"counting sequence\" y.\r\n\r\nA counting sequence is formed by \"counting\" the entries in a given sequence.\r\n\r\nFor example, the sequence\r\n\r\n x = 5, 5, 2, 1, 1, 1, 1, 3\r\n\r\ncan be read as\r\n\r\n Two 5's, one 2, four 1's, one 3\r\n\r\nwhich translates to\r\n\r\n y = 2, 5, 1, 2, 4, 1, 1, 3\r\n\r\nSo y is the counting sequence for x.\r\n\r\nFor this problem, all elements in the sequences x and y will be in the range from 1 to 9.\r\n","description_html":"\u003cp\u003eGiven a vector x, find the \"counting sequence\" y.\u003c/p\u003e\u003cp\u003eA counting sequence is formed by \"counting\" the entries in a given sequence.\u003c/p\u003e\u003cp\u003eFor example, the sequence\u003c/p\u003e\u003cpre\u003e x = 5, 5, 2, 1, 1, 1, 1, 3\u003c/pre\u003e\u003cp\u003ecan be read as\u003c/p\u003e\u003cpre\u003e Two 5's, one 2, four 1's, one 3\u003c/pre\u003e\u003cp\u003ewhich translates to\u003c/p\u003e\u003cpre\u003e y = 2, 5, 1, 2, 4, 1, 1, 3\u003c/pre\u003e\u003cp\u003eSo y is the counting sequence for x.\u003c/p\u003e\u003cp\u003eFor this problem, all elements in the sequences x and y will be in the range from 1 to 9.\u003c/p\u003e","function_template":"function y = CountSeq(x)\r\ny = x;\r\nend","test_suite":"%%\r\nx = [5 5 2 1 1 1 1 3];\r\ncorrect = [2 5 1 2 4 1 1 3];\r\nassert(isequal(correct, CountSeq(x)));\r\n\r\n\r\n%%\r\nx = [9];\r\ncorrect = [1 9];\r\nassert(isequal(correct, CountSeq(x)));\r\n\r\n\r\n%%\r\nx = ones(1,9);\r\ncorrect = [9 1];\r\nassert(isequal(correct, CountSeq(x)));\r\n\r\n\r\n%%\r\nx = 1:9;\r\ncorrect = [1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9];\r\nassert(isequal(correct, CountSeq(x)));\r\n\r\n%%\r\nx = [1 2 2 1];\r\ncorrect = [1 1 2 2 1 1];\r\nassert(isequal(correct, CountSeq(x)));\r\n\r\n","published":true,"deleted":false,"likes_count":30,"comments_count":13,"created_by":1,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":2188,"test_suite_updated_at":"2013-03-14T15:22:01.000Z","rescore_all_solutions":false,"group_id":2,"created_at":"2012-01-18T01:00:25.000Z","updated_at":"2026-05-21T21:51:04.000Z","published_at":"2012-01-18T01:00:25.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a vector x, find the \\\"counting sequence\\\" y.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA counting sequence is formed by \\\"counting\\\" the entries in a given sequence.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, the sequence\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ x = 5, 5, 2, 1, 1, 1, 1, 3]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ecan be read as\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Two 5's, one 2, four 1's, one 3]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ewhich translates to\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ y = 2, 5, 1, 2, 4, 1, 1, 3]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSo y is the 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