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.
Find the values of extra parameters in nonlinear functions created by
Save time when the objective and nonlinear constraint functions share common computations in the problem-based approach.
Solve a feasibility problem, which is a problem with constraints only.
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.
优化仿真、黑盒目标函数或 ODE 时的特殊注意事项。