Programmazione quadratica e programmazione conica
Risolvere problemi con obiettivi quadratici e vincoli lineari o con vincoli conici
Prima di iniziare a risolvere un problema di ottimizzazione, è necessario scegliere l'approccio appropriato: basato sui problemi o basato sul risolutore. Per i dettagli, vedere Scelta iniziale sull'adozione dell'approccio basato sui problemi o sul risolutore.
Per l'approccio basato sui problemi, creare delle variabili del problema, quindi esprimere la funzione obiettivo e i vincoli in termini di tali variabili simboliche. Per i passaggi da seguire per l'approccio basato sui problemi, vedere Problem-Based Optimization Workflow. Per risolvere il problema risultante, utilizzare solve.
Per i passaggi da seguire per l'approccio basato sul risolutore, inclusa la definizione della funzione obiettivo e dei vincoli e la scelta del risolutore appropriato, vedere Impostazione di un problema di ottimizzazione basato sul risolutore. Per risolvere il problema risultante, utilizzare quadprog o coneprog.
Funzioni
Attività di Live Editor
| Optimize | Ottimizzare o risolvere equazioni in Live Editor |
Oggetti
SecondOrderConeConstraint | Second-order cone constraint object |
Argomenti
Programmazione quadratica basata sui problemi
- Quadratic Programming with Bound Constraints: Problem-Based
Shows how to solve a problem-based quadratic programming problem with bound constraints using different algorithms. - Large Sparse Quadratic Program, Problem-Based
Shows how to solve a large sparse quadratic program using the problem-based approach. - Bound-Constrained Quadratic Programming, Problem-Based
Example showing large-scale problem-based quadratic programming. - Quadratic Programming for Portfolio Optimization, Problem-Based
Example showing problem-based quadratic programming on a basic portfolio model. - Diversify Portfolios Using Optimization Toolbox
This example shows three techniques of asset diversification in a portfolio using optimization functions.
Programmazione quadratica basata sul risolutore
- Quadratic Minimization with Bound Constraints
Example of quadratic programming with bound constraints and various options. - Quadratic Programming with Many Linear Constraints
This example shows the benefit of the active-set algorithm on problems with many linear constraints. - Warm Start quadprog
Shows that warm start can be effective in a large quadratic program. - Warm Start Best Practices
Describes how best to use warm start for speeding repeated solutions. - Quadratic Minimization with Dense, Structured Hessian
Example showing how to save memory in a structured quadratic program. - Large Sparse Quadratic Program with Interior Point Algorithm
Example showing how to save memory in a quadratic program by using a sparse quadratic matrix. - Bound-Constrained Quadratic Programming, Solver-Based
Example showing solver-based large-scale quadratic programming. - Quadratic Programming for Portfolio Optimization Problems, Solver-Based
Example showing solver-based quadratic programming on a basic portfolio model.
Programmazione conica di secondo ordine basata sui problemi
- Minimize Energy of Piecewise Linear Mass-Spring System Using Cone Programming, Problem-Based
Presents a problem-based example of cone programming. - Discretized Optimal Trajectory, Problem-Based
This example shows how to solve a discretized optimal trajectory problem using the problem-based approach. - Compare Speeds of coneprog Algorithms
This section gives timing information for a sequence of cone programming problems using variousLinearSolveroption settings. - Write Constraints for Problem-Based Cone Programming
Requirements forsolveto useconeprogfor problem solution.
Programmazione conica di secondo ordine basata sul risolutore
- Minimize Energy of Piecewise Linear Mass-Spring System Using Cone Programming, Solver-Based
Solve a mechanical mass-spring problem using cone programming. - Convert Quadratic Constraints to Second-Order Cone Constraints
Convert quadratic constraints intoconeprogform. - Convert Quadratic Programming Problem to Second-Order Cone Program
Convert a quadratic programming problem to a second-order cone problem.
Generazione di codice
- Code Generation for quadprog Background
Prerequisites to generate C code for quadratic optimization. - Generate Code for quadprog
Learn the basics of code generation for thequadprogoptimization solver. - Generate Single-Precision quadprog Code
Generate single-precision code for quadratic programming problems. - Code Generation for coneprog Background
Prerequisites to generate C code for cone programming. - Generate Code for coneprog
Provides an example of code generation inconeprog. - Warm Start Best Practices
Describes how best to use warm start for speeding repeated solutions. - Optimization Code Generation for Real-Time Applications
Explore techniques for handling real-time requirements in generated code.
Algoritmi basati sui problemi
- Problem-Based Optimization Algorithms
Learn how the optimization functions and objects solve optimization problems. - Write Constraints for Problem-Based Cone Programming
Requirements forsolveto useconeprogfor problem solution. - Supported Operations for Optimization Variables and Expressions
Explore the supported mathematical and indexing operations for optimization variables and expressions.
Algoritmi e opzioni
- Quadratic Programming Algorithms
Minimizing a quadratic objective function in n dimensions with only linear and bound constraints. - Second-Order Cone Programming Algorithm
Description of the underlying algorithm. - Optimization Options Reference
Explore optimization options.
