Problem-Based Nonlinear Optimization - MATLAB & Simulink (original) (raw)

Solve nonlinear optimization problems in serial or parallel using the problem-based approach

Before you begin to solve an optimization problem, you must choose the appropriate approach: problem-based or solver-based. For details, see First Choose Problem-Based or Solver-Based Approach.

Formulate your objective and nonlinear constraint functions as expressions in optimization variables, or convert MATLAB® functions using fcn2optimexpr. For problem setup, see Problem-Based Optimization Setup.

Functions

Live Editor Tasks

Topics

Unconstrained Problem-Based Applications

• Rational Objective Function, Problem-Based
  This example shows how to create a rational objective function using optimization variables and solve the resulting unconstrained problem.

Constrained Problem-Based Applications

• 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.
• Convert Nonlinear Function to Optimization Expression
  Convert nonlinear functions, whether expressed as function files or anonymous functions, by using fcn2optimexpr.
• 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-basedOptimize Live Editor task and several solvers.
• Obtain Solution Using Feasibility Mode
  Solve a problem with difficult constraints using fmincon feasibility 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.

Parallel Computing

• 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.

Simulation or ODE

• Optimizing a Simulation or Ordinary Differential Equation
  Special considerations in optimizing simulations, black-box objective functions, or ODEs.

Algorithms and Other Theory

• 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 that fminsearch takes 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.
• Optimization Bibliography
  Lists published materials that support concepts implemented in the optimization solver algorithms.

Related Information

• Problem-Based Nonlinear Programming