# Desired object produced but still receive "out of memory error"

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I encountered something quite odd and was hoping someone could provide some insight.
I have a very large variable ("W") that I need to run through some logical indexing in the form of a for loop. The end result should be a 110000 X 5000 X 401 3D matrix, but 220.5 billion numbers is a bit much for memory to handle, even if I make it a sparse array. You can see I've broken "W" up into 11 parts to see if that works.
This code ran for me and produced the object "b_1", but I still got an out of memory error. The out of memory error suggests to me that the object should have never been produced. I'm not sure that I trust the produced object "b_1" because of the out of memory error. Am I being too paranoid, or would it be wise to look for a different solution?
T = 5000
W = zeros(110000, 5000);
for trial = 1:T
X2ri = X;
X2ri(10001:20000, 46:50) = randi([1,5], 10000, 5);
X3ri = X2ri;
X3ri(20001:30000, 41:50) = randi([1,5], 10000, 10);
X4ri = X3ri;
X4ri(30001:40000, 36:50) = randi([1,5], 10000, 15);
X5ri = X4ri;
X5ri(40001:50000, 31:50) = randi([1,5], 10000, 20);
X6ri = X5ri;
X6ri(50001:60000, 26:50) = randi([1,5], 10000, 25);
X7ri = X6ri;
X7ri(60001:70000, 21:50) = randi([1,5], 10000, 30);
X8ri = X7ri;
X8ri(70001:80000, 16:50) = randi([1,5], 10000, 35);
X9ri = X8ri;
X9ri(80001:90000, 11:50) = randi([1,5], 10000, 40);
X10ri = X9ri;
X10ri(90001:100000, 6:50) = randi([1,5], 10000, 45);
X11ri = X10ri;
X11ri(100001:110000, 1:50) = randi([1,5], 10000, 50);
XFri = X11ri;
W(:,trial) = var(XFri,0,2);
end
W_1 = W(1:10000,:);
W_2 = W(10001:20000,:);
W_3 = W(20001:30000,:);
W_4 = W(30001:40000,:);
W_5 = W(40001:50000,:);
W_6 = W(50001:60000,:);
W_7 = W(60001:70000,:);
W_8 = W(70001:80000,:);
W_9 = W(80001:90000,:);
W_10 = W(90001:100000,:);
W_11 = W(100001:110000,:);
varinc = (0:0.01:4);
b_1 = ndSparse.build([10000,5000,401]);
for i = 1:length(varinc)
b_1(:,:,i) = W_1 >= varinc(i);
end

Walter Roberson on 26 Sep 2020 at 0:11
you are using https://www.mathworks.com/matlabcentral/fileexchange/29832-n-dimensional-sparse-arrays
You need to use nzmax to preallocate the object. Otherwise every time you assign into the object, matlab needs to create a copy of the array with one more value slot and copy the old one over.

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Hi Walter,
I was wondering if you might provide some insight as to how to use the nzmax subcommand. Even prior to today, I've spent a deal of time reading over the materials for ndSparse and sparse/spalloc, but I can't decipher what to do about using nzmax for preallocation.
For example, if I write the following to preallocate space for 300 nonzero values
temp = ndSparse.build([500,100],300)
the returned array is of the correct dimensions, but when I inspect it using full(temp) the above code just placed a "300" in the position temp(500,100) (or the bottom cell of the last column). It doesn't seem to be doing what it should, but that code should follow the given formula from the documentation: S=ndSparse.build(Coordinates,Values,[m,n,p,...],nzmax)
I'm also unsure of what to use for any nzmax value, as my purpose for this specific analysis is that I want to determine how many nonzero values the final array contains after logical indexing.
Thank you again for your insight.
Walter Roberson on 26 Sep 2020 at 2:32
Yes, I figure that at some point when it went to make a copy to be able to expand the array, it ran out of memory, and the result you got was filled in to that point only.
I always recommend using spalloc or providing nzmax when creating an array using sparse. The overhead for expanding a sparse array is not fun.
Also be careful about
A = B operator C
for sparse B and C. MATLAB will strip out any unallocated locations in the result.
Hi Walter,
Thank you again for your insight! This has given me a lot to think about, but it will make my future code much better.

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