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.
evaluate | Evaluate optimization expression |
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 |
Rational Objective Function, Problem-Based
This example shows how to create a rational objective function using optimization variables and solve the resulting unconstrained problem.
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, 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.
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
How to find the values of extra parameters in nonlinear functions created by
prob2struct.
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.
Solve Nonlinear Feasibility Problem, Problem-Based
Solve a feasibility problem, which is a problem with constraints only.
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.
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.
Optimizing a Simulation or Ordinary Differential Equation
Special considerations in optimizing simulations, black-box objective functions, or ODEs.
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.
Steps that fminsearch takes to
minimize a function.
Optimization Options Reference
Explore optimization options.
Explains why solvers might not find the smallest minimum.
Lists published materials that support concepts implemented in the solver algorithms.