Solve problems using a modeling approach. Describe objective and constraints using symbolic variable expressions. For the steps to take, see Problem-Based Workflow.
Optimization App with the fmincon Solver
Example of nonlinear programming with constraints using the Optimization app.
Nonlinear Inequality Constraints
Example of nonlinear programming with nonlinear inequality constraints.
Nonlinear Constraints with Gradients
Example of nonlinear programming with derivative information.
fmincon Interior-Point Algorithm with Analytic Hessian
Example of nonlinear programming with all derivative information.
Linear or Quadratic Objective with Quadratic Constraints
This example shows how to solve an optimization problem that has a linear or quadratic objective and quadratic inequality constraints.
Nonlinear Equality and Inequality Constraints
Nonlinear programming with both types of nonlinear constraints.
How to Use All Types of Constraints
Example showing all constraints.
Minimization with Bound Constraints and Banded Preconditioner
Example showing efficiency gains possible with structured nonlinear problems.
Minimization with Linear Equality Constraints
Example showing nonlinear programming with only linear equality constraints.
Minimization with Dense Structured Hessian, Linear Equalities
Example showing how to save memory in nonlinear programming with a structured Hessian and only linear equality constraints or only bounds.
Symbolic Math Toolbox Calculates Gradients and Hessians
Example showing how to calculate derivatives symbolically for optimization solvers.
One-Dimensional Semi-Infinite Constraints
Example showing how to use one-dimensional semi-infinite constraints in nonlinear programming.
Two-Dimensional Semi-Infinite Constraint
Example showing how to use two-dimensional semi-infinite constraints in nonlinear programming.
What Is Parallel Computing in Optimization Toolbox?
Using multiple processors for optimization.
Using Parallel Computing in Optimization Toolbox
Automatic gradient estimation in parallel.
Improving Performance with Parallel Computing
Considerations for speeding optimizations.
Optimizing a Simulation or Ordinary Differential Equation
Special considerations in optimizing simulations, black-box objective functions, or ODEs.
Constrained Nonlinear Optimization Algorithms
Minimizing a single objective function in n dimensions with various types of constraints.
Optimization Options Reference
Describes optimization options.
Explains why solvers might not find the smallest minimum.
Lists published materials that support concepts implemented in the solver algorithms.