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.
For problem setup, see Solver-Based Optimization Problem Setup.
|Find minimum of single-variable function on fixed interval|
|Find minimum of constrained nonlinear multivariable function|
|Find minimum of unconstrained multivariable function using derivative-free method|
|Find minimum of unconstrained multivariable function|
|Find minimum of semi-infinitely constrained multivariable nonlinear function|
Shows how to solve for the minimum of Rosenbrock's function using different solvers, with or without gradients.
Example of unconstrained nonlinear programming.
Example of unconstrained nonlinear programming including derivatives.
Example of nonlinear programming using some derivative information.
Tutorial example showing how to solve nonlinear problems and pass extra parameters.
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.
Use Symbolic Math Toolbox™ to generate gradients and Hessians.
Generate C code for nonlinear optimization.
Learn the basics of code generation for the
Use static memory allocation in code generation when the problem changes.
Explore techniques for handling real-time requirements in generated code.
Example showing how to use one-dimensional semi-infinite constraints in nonlinear programming.
Example showing how to use two-dimensional semi-infinite constraints in nonlinear programming.
This example shows how to use semi-infinite programming to investigate the effect of uncertainty in the model parameters of an optimization problem.
Use multiple processors for optimization.
Perform gradient estimation in parallel.
Investigate factors for speeding optimizations.
Example showing how to use parallel computing in both Global Optimization Toolbox and Optimization Toolbox™ solvers.
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.