Ottimizzazione non lineare basata sui problemi
Risolvere problemi di ottimizzazione non lineare in modalità seriale o parallela utilizzando l'approccio basato sui problemi
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.
Formulare le funzioni obiettivo e le funzioni di vincolo non lineari come espressioni in variabili di ottimizzazione oppure convertire le funzioni di MATLAB® utilizzando fcn2optimexpr. Per l'impostazione del problema, vedere Configurazione dell'ottimizzazione basata sui problemi.
Funzioni
evaluate | Evaluate optimization expression or objectives and constraints in problem |
fcn2optimexpr | Convert function to optimization expression |
infeasibility | Constraint violation at a point |
optimproblem | Create optimization problem |
optimvar | Create optimization variables |
prob2struct | Convert optimization problem or equation problem to solver form |
solve | Solve optimization problem or equation problem |
Attività di Live Editor
| Optimize | Ottimizzare o risolvere equazioni in Live Editor |
App
| Optimization Explorer | Explore multiple solver configurations and their solutions for an optimization problem (Da R2026a) |
Argomenti
Applicazioni basate su problemi senza vincoli
- Rational Objective Function, Problem-Based
This example shows how to create a rational objective function using optimization variables and solve the resulting unconstrained problem.
Applicazioni basate su problemi con vincoli
- Solve Constrained Nonlinear Optimization, Problem-Based
This example shows how to solve a constrained nonlinear problem based on optimization expressions. The example also shows how to convert a nonlinear function to an optimization expression. - Use Optimization Explorer App
Basic example showing the use of the Optimization Explorer App. - 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, andfcn2optimexprto 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 usingfminconfeasibility mode. - Monitor Solution Process with optimplot
View details of the optimization solution process usingoptimplot. - 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.
Calcolo parallelo
- 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.
Simulazione o ODE
- Optimizing a Simulation or Ordinary Differential Equation
Special considerations in optimizing simulations, black-box objective functions, or ODEs.
Algoritmi e altri aspetti teorici
- 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. - fminsearch Algorithm
Steps thatfminsearchtakes to minimize a function. - Optimization Options Reference
Explore optimization options. - Local vs. Global Optima
Explains why solvers might not find the smallest minimum. - Smooth Formulations of Nonsmooth Functions
Reformulate some nonsmooth functions as smooth functions by using auxiliary variables. - Bibliografia sull'ottimizzazione
Elenca i materiali pubblicati a supporto dei concetti implementati negli algoritmi del risolutore di ottimizzazione.
