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R2011b Documentation → Optimization Toolbox
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Contents

Index

• Getting Started

Optimization Functions

Optimization Tool GUI

Problem Formulation: Rosenbrock's Function

Defining the Problem in Toolbox Syntax

Running the Optimization

Interpreting the Result

Example of Converting to Solver Form

Model Description

Solution Method

• User's Guide

Acknowledgments

• Setting Up an Optimization

• Examining Results

• Steps to Take After Running a Solver

• Graphical Optimization Tool

• Optimization Algorithms and Examples

Optimization Theory Overview

• Unconstrained Nonlinear Optimization Algorithms

Large Scale fminunc Algorithm

Medium Scale fminunc Algorithm

fminsearch Algorithm

• Unconstrained Nonlinear Optimization Examples

Example: fminunc Unconstrained Minimization

Example: Nonlinear Minimization with Gradient and Hessian

Example: Nonlinear Minimization with Gradient and Hessian Sparsity Pattern

• Constrained Nonlinear Optimization Algorithms

fmincon Trust Region Reflective Algorithm

fmincon Active Set Algorithm

fmincon SQP Algorithm

fmincon Interior Point Algorithm

fminbnd Algorithm

fseminf Problem Formulation and Algorithm

• Constrained Nonlinear Optimization Examples

Example: Nonlinear Inequality Constraints

Example: Bound Constraints

Example: Constraints With Gradients

Example: Constrained Minimization Using fmincon's Interior-Point Algorithm with Analytic Hessian

Example: Equality and Inequality Constraints

Example: Nonlinear Minimization with Bound Constraints and Banded Preconditioner

Example: Nonlinear Minimization with Equality Constraints

Example: Nonlinear Minimization with a Dense but Structured Hessian and Equality Constraints

Example: Using Symbolic Math Toolbox Functions to Calculate Gradients and Hessians

Example: One-Dimensional Semi-Infinite Constraints

Example: Two-Dimensional Semi-Infinite Constraint

• Linear Programming Algorithms

Large Scale Linear Programming

Active-Set Medium-Scale linprog Algorithm

Medium-Scale linprog Simplex Algorithm

• Linear Programming Examples

Example: Linear Programming with Equalities and Inequalities

Example: Linear Programming with Dense Columns in the Equalities

• Quadratic Programming Algorithms

interior-point-convex quadprog Algorithm

trust-region-reflective quadprog Algorithm

active-set quadprog Algorithm

• Quadratic Programming Examples

Example: Quadratic Minimization with Bound Constraints

Example: Quadratic Minimization with a Dense but Structured Hessian

Example: Large Sparse Quadratic Program with Interior Point Algorithm

• Binary Integer Programming Algorithms

bintprog Algorithm

• Binary Integer Programming Examples

Example: Investments with Constraints

• Least Squares (Model Fitting) Algorithms

Large-Scale Least Squares

Levenberg-Marquardt Method

• Least Squares (Model Fitting) Examples

Example: Using lsqnonlin With a Simulink Model

Example: Nonlinear Least-Squares with Full Jacobian Sparsity Pattern

Example: Linear Least-Squares with Bound Constraints

Example: Jacobian Multiply Function with Linear Least Squares

Example: Nonlinear Curve Fitting with lsqcurvefit

• Multiobjective Optimization Algorithms

• Multiobjective Optimization Examples

Example: Using fminimax with a Simulink Model

Example: Signal Processing Using fgoalattain

• Equation Solving Algorithms

Trust-Region Dogleg Method

Trust-Region Reflective fsolve Algorithm

Levenberg-Marquardt Method

fzero Algorithm

• Equation Solving Examples

Example: Nonlinear Equations with Analytic Jacobian

Example: Nonlinear Equations with Finite-Difference Jacobian

Example: Nonlinear Equations with Jacobian

Example: Nonlinear Equations with Jacobian Sparsity Pattern

Selected Bibliography

• Parallel Computing for Optimization

• External Interface

• Argument and Options Reference

• Release Notes

Optimization Algorithms and Examples

Optimization Tool Examples

Optimization Tool Examples

Optimization Theory Overview

Optimization Theory Overview

Free Optimization Interactive Kit

Learn how to use optimization to solve systems of equations, fit models to data, or optimize system performance.

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