Programación cuadrática y programación de cono
Antes de comenzar a resolver un problema de optimización, deberá elegir el enfoque adecuado: basado en problemas o basado en solvers. Para obtener más detalles, consulte En primer lugar, elija el enfoque basado en problemas o el enfoque basado en solvers.
Para el enfoque basado en problemas, cree variables de problemas y, posteriormente, represente la función objetivo y las restricciones en términos de estas variables simbólicas. Para saber qué saltos basados en problemas se deben tomar, consulte Flujo de trabajo de optimización basada en problemas. Para resolver el problema resultante, utilice solve
.
Para saber qué saltos basados en solvers se deben tomar, incluyendo la definición de la función objetivo y las restricciones, y la selección del solver adecuado, consulte Configuración de problema de optimización basada en solvers. Para resolver el problema resultante, utilice quadprog
o coneprog
.
Funciones
Tareas de Live Editor
Optimize | Optimizar o resolver ecuaciones en Live Editor |
Objetos
SecondOrderConeConstraint | Second-order cone constraint object |
Temas
Programación cuadrática basada en problemas
- 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.
Programación cuadrática basada en solvers
- 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.
Programación de cono de segundo orden basada en problemas
- 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 variousLinearSolver
option settings. - Write Constraints for Problem-Based Cone Programming
Requirements forsolve
to useconeprog
for problem solution.
Programación de cono de segundo orden basada en solvers
- 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 intoconeprog
form. - Convert Quadratic Programming Problem to Second-Order Cone Program
Convert a quadratic programming problem to a second-order cone problem.
Generación de código
- Code Generation for quadprog Background
Prerequisites to generate C code for quadratic optimization. - Generate Code for quadprog
Learn the basics of code generation for thequadprog
optimization solver. - 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.
Algoritmos basados en problemas
- Problem-Based Optimization Algorithms
Learn how the optimization functions and objects solve optimization problems. - Write Constraints for Problem-Based Cone Programming
Requirements forsolve
to useconeprog
for problem solution. - Supported Operations for Optimization Variables and Expressions
Explore the supported mathematical and indexing operations for optimization variables and expressions.
Algoritmos y opciones
- 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. - Referencia de opciones de optimización
Explore opciones de optimización.