Solve constrained minimization and semi-infinite programming
problems in serial or parallel

**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.

**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.

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