Getting Started with the Optimization Tool :: Graphical Optimization Tool (Optimization Toolbox™)

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Contents

Index

• Getting Started

• User's Guide

Acknowledgments

• Setting Up an Optimization

• Examining Results

• Steps to Take After Running a Solver

• Graphical Optimization Tool

• Getting Started with the Optimization Tool

Opening the Optimization Tool

Steps for Using the Optimization Tool

• Running a Problem in the Optimization Tool

Pausing and Stopping the Algorithm

Viewing Results

Starting a New Problem

Closing the Optimization Tool

• Specifying Certain Options

Output function

Display to Command Window

• Getting Help in the Optimization Tool

Quick Reference

Additional Help

• Importing and Exporting Your Work

Exporting Your Work

Importing Your Work

Generating a File

• Optimization Tool Examples

About Optimization Tool Examples

Optimization Tool with the fmincon Solver

Optimization Tool with the lsqlin Solver

• Optimization Algorithms and Examples

Large Scale fminunc Algorithm

Medium Scale fminunc Algorithm

fminsearch Algorithm

Example: fminunc Unconstrained Minimization

Example: Nonlinear Minimization with Gradient and Hessian

Example: Nonlinear Minimization with Gradient and Hessian Sparsity Pattern

fmincon Trust Region Reflective Algorithm

fmincon Active Set Algorithm

fmincon SQP Algorithm

fmincon Interior Point Algorithm

fminbnd Algorithm

fseminf Problem Formulation and Algorithm

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

Large Scale Linear Programming

Active-Set Medium-Scale linprog Algorithm

Medium-Scale linprog Simplex Algorithm

Example: Linear Programming with Equalities and Inequalities

Example: Linear Programming with Dense Columns in the Equalities

interior-point-convex quadprog Algorithm

trust-region-reflective quadprog Algorithm

active-set quadprog Algorithm

Example: Quadratic Minimization with Bound Constraints

Example: Quadratic Minimization with a Dense but Structured Hessian

Example: Large Sparse Quadratic Program with Interior Point Algorithm

bintprog Algorithm

Example: Investments with Constraints

Large-Scale Least Squares

Levenberg-Marquardt Method

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

Example: Using fminimax with a Simulink Model

Example: Signal Processing Using fgoalattain

Trust-Region Dogleg Method

Trust-Region Reflective fsolve Algorithm

Levenberg-Marquardt Method

fzero Algorithm

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

• Parallel Computing for Optimization

Parallel Optimization Functionality

Parallel Estimation of Gradients

Nested Parallel Functions

Using Parallel Computing with Multicore Processors

Using Parallel Computing with a Multiprocessor Network

Testing Parallel Computations

Factors That Affect Speed

Factors That Affect Results

Searching for Global Optima

• External Interface

What Is ktrlink?

Installation and Configuration

Example Using ktrlink

Setting Options

Sparse Matrix Considerations

• Argument and Options Reference

Input Arguments

Output Arguments

Options Structure

Output Function

• Release Notes

Getting Started with the Optimization Tool

On this page…

Opening the Optimization Tool

Steps for Using the Optimization Tool

Introduction

The Optimization Tool is a GUI for solving optimization problems. With the Optimization Tool, you select a solver from a list and set up your problem visually. If you are familiar with the optimization problem you want to solve, the Optimization Tool lets you select a solver, specify the optimization options, and run your problem. You can also import and export data from the MATLAB workspace, and generate files containing your configuration for the solver and options.

Opening the Optimization Tool

To open the tool, type

optimtool

in the Command Window. This opens the Optimization Tool, as shown in the following figure.

You can also open the Optimization Tool from the main MATLAB window as pictured:

The reference page for the Optimization Tool provides variations for starting the optimtool function.

Steps for Using the Optimization Tool

This is a summary of the steps to set up your optimization problem and view results with the Optimization Tool.

Graphical Optimization Tool

Graphical Optimization Tool

Running a Problem in the Optimization Tool

Running a Problem in the Optimization Tool

Free Optimization Interactive Kit

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

Trials Available

Try the latest version of optimization products.

Get trial software

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