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 optimization expression|
|Convert function to optimization expression|
|Constraint violation at a point|
|Create optimization problem|
|Create optimization variables|
|Convert optimization problem or equation problem to solver form|
|Solve optimization problem or equation problem|
This example shows how to create a rational objective function using optimization variables and solve the resulting unconstrained problem.
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 functions, whether expressed as function files or anonymous
functions, by using
Shows how to define objective and constraint functions for a structured nonlinear optimization in the problem-based approach.
Shows how to use optimization variables to create linear constraints, and
fcn2optimexpr to convert a function to an optimization
Automatic differentiation lowers the number of function evaluations for solving a problem.
How to include derivative information in problem-based optimization when automatic derivatives do not apply.
How to find the values of extra parameters in nonlinear functions created by
Save time when your objective and nonlinear constraint functions share common computations in the problem-based approach.
Solve a feasibility problem, which is a problem with constraints only.
Shows how to use an output function in the problem-based approach to record iteration history and to make a custom plot.
Use multiple processors for optimization.
Perform gradient estimation in parallel.
Investigate factors for speeding optimizations.
Special considerations in optimizing simulations, black-box objective functions, or ODEs.
Minimizing a single objective function in n dimensions without constraints.
Minimizing a single objective function in n dimensions with various types of constraints.
fminsearch takes to
minimize a function.
Explore optimization options.
Explains why solvers might not find the smallest minimum.
Lists published materials that support concepts implemented in the solver algorithms.