Optimización no lineal basada en problemas
Resuelva problemas de optimización no lineales en serie o en paralelo utilizando el enfoque basado en problemas
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.
Formule sus funciones objetivo y de restricción no lineales como expresiones en variables de optimización o convierta funciones de MATLAB® utilizando fcn2optimexpr
. Para la configuración de problemas, consulte Configuración de optimización basada en problemas.
Funciones
evaluate | Evaluar una expresión de optimización |
fcn2optimexpr | Convert function to optimization expression |
infeasibility | Constraint violation at a point |
optimproblem | Crear un problema de optimización |
optimvar | Crear variables de optimización |
prob2struct | Convert optimization problem or equation problem to solver form |
solve | Resolver un problema de optimización o un problema de ecuación |
Tareas de Live Editor
Optimize | Optimizar o resolver ecuaciones en Live Editor |
Temas
Aplicaciones no restringidas basadas en problemas
- Rational Objective Function, Problem-Based
This example shows how to create a rational objective function using optimization variables and solve the resulting unconstrained problem.
Aplicaciones restringidas basadas en problemas
- Resolver optimización no lineal restringida, basada en problemas
Este ejemplo muestra cómo resolver un problema no lineal restringido basado en expresiones de optimización. El ejemplo también muestra cómo convertir una función no lineal en una expresión de optimización. - Convert Nonlinear Function to Optimization Expression
Convert nonlinear functions, whether expressed as function files or anonymous functions, by usingfcn2optimexpr
. - Constrained Electrostatic Nonlinear Optimization Using Optimization Variables
Define objective and constraint functions for a structured nonlinear optimization in the problem-based approach. - Discretized Optimal Trajectory, Problem-Based
This example shows how to solve a discretized optimal trajectory problem using the problem-based approach. - Problem-Based Nonlinear Minimization with Linear Constraints
Shows how to use optimization variables to create linear constraints, andfcn2optimexpr
to convert a function to an optimization expression. - Effect of Automatic Differentiation in Problem-Based Optimization
Automatic differentiation lowers the number of function evaluations for solving a problem. - Supply Derivatives in Problem-Based Workflow
How to include derivative information in problem-based optimization when automatic derivatives do not apply. - Obtain Generated Function Details
Find the values of extra parameters in nonlinear functions created byprob2struct
. - Objective and Constraints Having a Common Function in Serial or Parallel, Problem-Based
Save time when the objective and nonlinear constraint functions share common computations in the problem-based approach. - Solve Nonlinear Feasibility Problem, Problem-Based
Solve a feasibility problem, which is a problem with constraints only. - Feasibility Using Problem-Based Optimize Live Editor Task
Solve a nonlinear feasibility problem using the problem-based Optimize Live Editor task and several solvers. - Obtain Solution Using Feasibility Mode
Solve a problem with difficult constraints usingfmincon
feasibility mode. - Output Function for Problem-Based Optimization
Use an output function in the problem-based approach to record iteration history and to make a custom plot.
Computación paralela
- What Is Parallel Computing in Optimization Toolbox?
Use multiple processors for optimization. - Using Parallel Computing in Optimization Toolbox
Perform gradient estimation in parallel. - Improving Performance with Parallel Computing
Investigate factors for speeding optimizations.
Simulación u ODE
- Optimizing a Simulation or Ordinary Differential Equation
Special considerations in optimizing simulations, black-box objective functions, or ODEs.
Algoritmos y otra teoría
- Unconstrained Nonlinear Optimization Algorithms
Minimizing a single objective function in n dimensions without constraints. - Constrained Nonlinear Optimization Algorithms
Minimizing a single objective function in n dimensions with various types of constraints. - Algoritmo fminsearch
Pasos que dafminsearch
para minimizar una función. - Referencia de opciones de optimización
Explore opciones de optimización. - Óptimos locales frente a globales
Explica por qué puede que los solvers no encuentren el mínimo más pequeño. - Smooth Formulations of Nonsmooth Functions
Reformulate some nonsmooth functions as smooth functions by using auxiliary variables. - Bibliografía
Enumera el material publicado que respalda los conceptos implementados en los algoritmos de solver.