Preface
Using This Guide
Related Products
Typographical Conventions
Introduction
What Is the Optimization Toolbox?
New Features in Version 2.2
New fsolve Default Algorithm
Configuration Information
Technical Conventions
Matrix, Vector, and Scalar Notation
Acknowledgments
Examples
Tutorial
Introduction
Problems Covered by the Toolbox
Using the Optimization Functions
Examples that Use Standard Algorithms
Unconstrained Minimization Example
Nonlinear Inequality Constrained Example
Constrained Example with Bounds
Constrained Example with Gradients
Gradient Check: Analytic Versus Numeric
Equality Constrained Example
Maximization
Greater-Than-Zero Constraints
Additional Arguments: Avoiding Global Variables
Nonlinear Equations with Analytic Jacobian
Nonlinear Equations with Finite-Difference Jacobian
Multiobjective Examples
Large-Scale Examples
Problems Covered by Large-Scale Methods
Nonlinear Equations with Jacobian
Nonlinear Equations with Jacobian Sparsity Pattern
Nonlinear Least-Squares with Full Jacobian Sparsity Pattern
Nonlinear Minimization with Gradient and Hessian
Nonlinear Minimization with Gradient and Hessian Sparsity Pattern
Nonlinear Minimization with Bound Constraints and Banded Preconditioner
Nonlinear Minimization with Equality Constraints
Nonlinear Minimization with a Dense but Structured Hessian and Equality Constraints
Quadratic Minimization with Bound Constraints
Quadratic Minimization with a Dense but Structured Hessian
Linear Least-Squares with Bound Constraints
Linear Programming with Equalities and Inequalities
Linear Programming with Dense Columns in the Equalities
Default Parameter Settings
Changing the Default Settings
Displaying Iterative Output
Output Headings: Medium-Scale Algorithms
Output Headings: Large-Scale Algorithms
Optimization of Inline Objects Instead of M-Files
Typical Problems and How to Deal with Them
Converting Your Code to Version 2 Syntax
Using optimset and optimget
New Calling Sequences
Example of Converting from constr to fmincon
Selected Bibliography
Standard Algorithms
Optimization Overview
Unconstrained Optimization
Quasi-Newton Methods
Line Search
Quasi-Newton Implementation
Least-Squares Optimization
Gauss-Newton Method
Levenberg-Marquardt Method
Nonlinear Least-Squares Implementation
Nonlinear Systems of Equations
Gauss-Newton Method
Trust-Region Dogleg Method
Nonlinear Equations Implementation
Constrained Optimization
Sequential Quadratic Programming (SQP)
Quadratic Programming (QP) Subproblem
SQP Implementation
Multiobjective Optimization
Introduction
Goal Attainment Method
Algorithm Improvements for Goal Attainment Method
Selected Bibliography
Large-Scale Algorithms
Trust-Region Methods for Nonlinear Minimization
Preconditioned Conjugate Gradients
Linearly Constrained Problems
Linear Equality Constraints
Box Constraints
Nonlinear Least-Squares
Quadratic Programming
Linear Least-Squares
Large-Scale Linear Programming
Main Algorithm
Preprocessing
Selected Bibliography
Functions - By Category
Minimization
Equation Solving
Least Squares (Curve Fitting)
Utility
Demos of Large-Scale Methods
Demos of Medium-Scale Methods
Function Arguments
Input Arguments
Output Arguments
Optimization Parameters
Functions - Alphabetical List
Printable Documentation (PDF)
Product Page (Web)