Solve problems using a modeling approach. Describe objective and constraints using symbolic variable expressions. For the steps to take, see Problem-Based Workflow.
Example of nonlinear programming with constraints using the Optimization app.
Example of nonlinear programming with nonlinear inequality constraints.
Example of nonlinear programming with derivative information.
Example of nonlinear programming with all derivative information.
This example shows how to solve an optimization problem that has a linear or quadratic objective and quadratic inequality constraints.
Nonlinear programming with both types of nonlinear constraints.
Example showing all constraints.
Example showing efficiency gains possible with structured nonlinear problems.
Example showing nonlinear programming with only linear equality constraints.
Example showing how to save memory in nonlinear programming with a structured Hessian and only linear equality constraints or only bounds.
Example showing how to calculate derivatives symbolically for optimization solvers.
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 with various types of constraints.
Describes optimization options.
Explains why solvers might not find the smallest minimum.
Lists published materials that support concepts implemented in the solver algorithms.