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Problem-Based Nonlinear Optimization

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

See First Choose Problem-Based or Solver-Based Approach for choosing between problem-based optimization and solver-based optimization.

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.


fcn2optimexprConvert function to optimization expression
prob2structConvert optimization problem to solver form
solveSolve optimization problem


Unconstrained Problem-Based Applications

Rational Objective Function, Problem-Based

Shows how to create a rational objective function using optimization variables.

Constrained Problem-Based Applications

Solve Constrained Nonlinear Optimization, Problem-Based

This example shows how to convert a MATLAB function to an optimization expression and use a rational expression as a nonlinear constraint.

Convert Nonlinear Function to Optimization Expression

Convert nonlinear functions, whether expressed as function files or anonymous functions, by using fcn2optimexpr.

Constrained Electrostatic Nonlinear Optimization, Problem-Based

Shows how to define objective and constraint functions for a structured nonlinear optimization in the problem-based approach.

Problem-Based Nonlinear Minimization with Linear Constraints

Shows how to use optimization variables to create linear constraints, and fcn2optimexpr to convert a function to an optimization expression.

Include Derivatives in Problem-Based Workflow

How to include derivative information in problem-based optimization.

Objective and Constraints Having a Common Function in Serial or Parallel, Problem-Based

Save time when your objective and nonlinear constraint functions share common computations in the problem-based approach.

Output Function for Problem-Based Optimization

Shows how to 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?

Using multiple processors for optimization.

Using Parallel Computing in Optimization Toolbox

Automatic gradient estimation in parallel.

Improving Performance with Parallel Computing

Considerations 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

Describes optimization options.

Local vs. Global Optima

Explains why solvers might not find the smallest minimum.


Lists published materials that support concepts implemented in the solver algorithms.