# Optimization Using Pattern Search Method: MATLAB Programming

A Quick Way to Learn and Solve Optimization Problems in MATLAB. A Course for Beginners.

Language: english

Note: 3.9/5 (137 notes) 18,976 students

Instructor(s): Karthik K

Last update: 2020-06-08

## What you’ll learn

• Running direct search optimization problems in MATLAB
• Specifying objective functions
• Specifying constraints
• Vectorizing objective function and constraints
• Obtaining local and global optima
• Parallel computing

## Requirements

• MATLAB installed in your laptop/desktop computer

## Description

This course introduces applied direct search optimization in the MATLAB environment, focusing on using Global Optimization Toolbox. Various kinds of optimization problems are solved in this course. At the end of this course, you will be able to solve the optimization problems using the MATLAB. The complete MATLAB programs included in the class are also available for download.  Happy learning.

NB: This course is designed most straightforwardly to utilize your time wisely.

## Who this course is for

• Anyone who is interested to solve optimization problems.
• Researchers who want to publish ISI papers in this field.
• Students who are working on optimization problems.

## Course content

• Introduction to Optimization
• Welcome to the course
• Local optima and Global optima
• Single local solution, Multiple local solutions and Single global solution
• Objective or Fitness Functions
• What is Objective Function ?
• MATLAB Script For Single Objective Function
• MATLAB Script For Vectorized Function Call
• Passing Extra Parameters, Fixed Variables, or Data in the Objective Functions
• Direct/Pattern Search with MATLAB
• How Pattern Search Works?
• What is Direct/Pattern Search ?
• Unconstrained Pattern Search Minimization
• Pattern Search with a Linear Inequality Constraint
• Pattern Search with a Linear Equality Constraint
• Pattern Search with Bounds
• Pattern Search with Nonlinear Constraints
• Obtain Function Value And Minimizing Point
• Using a Complete Poll in a Generalized Pattern Search
• Vectorize the Objective and Constraint Functions
• Compute in Parallel
• Maximizing an Objective Function
• Stopping Criteria
• Exit Flag and Output
• Quiz on Optimization
• Practice test on optimization

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