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.
Shows how to create a rational objective function using optimization variables.
This example shows how to convert a MATLAB function to an optimization expression and use a rational expression as a nonlinear constraint.
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
How to include derivative information in problem-based optimization.
Save time when your objective and nonlinear constraint functions share common computations in the problem-based approach.
Shows how to use an output function in the problem-based approach to record iteration history and to make a custom plot.
Using multiple processors for optimization.
Automatic gradient estimation in parallel.
Considerations 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.
Describes optimization options.
Explains why solvers might not find the smallest minimum.
Lists published materials that support concepts implemented in the solver algorithms.