Optimization for Industrial Applications with MATLAB

Course Highlight

This one-day course introduces applied optimization in the MATLAB environment, focusing on using Optimization Toolbox and Global Optimization Toolbox.  Topics include:

  • Running optimization problems in MATLAB
  • Specifying objective functions
  • Specifying constraints
  • Choosing solvers and algorithms
  • Evaluating results and improving performance
  • Using global optimization methods

Course Objectives

The aim of the course is to introduce optimization in the MATLAB environment and related applications to participant using the functions in the Optimization Toolbox.

Who Must Attend

This course is designed for scientist, engineers, researchers, manager, and analyst from various industries and government bodies who are interested to gain a fundamental understanding of the concepts, applications and techniques of optimization and/or plan to apply optimization in their designs, processes, projects, services and work systems.

Course Benefits

Upon the completion of the course, the participants will be able to utilize the Optimization Toolbox in the MATLAB environment to solve optimization problem in the areas of engineering, signal and image processing, control design, finance, and the physical, biological, and social sciences.

Prerequisites

Attended "Comprehensive MATLAB" or equivalent experience using MATLAB. Knowledge of linear algebra and multivariate calculus is helpful.

Course Outline

Day 1 of 1

Running an Optimization

Objective: Understand the basic structure and process of solving optimization problems effectively. Use interactive tools to define and solve optimization problems.

  • Identifying the problem components
  • Running an optimization using Optimization Tool
  • Applying the optimization process
  • Using optimization functions

Specifying the Objective Function

Objective: Implement an objective function as a function file. Use function handles to specify objective functions and extra data.

  • Using an objective function file
  • Specifying objective functions with function handles
  • Passing extra data to objective functions

Specifying Constraints

Objective: Add different kinds of constraints to an optimization problem in MATLAB.

  • Identifying different types of constraints
  • Defining bounds
  • Defining linear constraints
  • Defining nonlinear constraints

Choosing a Solver

Objective: Select an appropriate solver and algorithm by considering the type of optimization problem to be solved.

  • Classifying the objective
  • Choosing a solver
  • Choosing the algorithm

Evaluating Results and Improving Performance

Objective: Interpret the output from the solver and diagnose the progress of an optimization. Increase accuracy and efficiency of an optimization by changing settings.

  • Examining the optimization
  • Interpreting the result
  • Setting convergence options
  • Providing derivative information

Global Optimization

Objective: Use Global Optimization Toolbox functionality to solve problems where classical algorithms fail or work inefficiently.

  • Finding the global minimum
  • Using genetic algorithms to solve discrete problems

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