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MLOP: MATLAB Based Optimization Techniques

This introduction to applied optimization in the MATLAB^® environment focuses on using Optimization Toolbox ™ and Global Optimization Toolbox. Emphasis is on identifying and formulating a problem and choosing the appropriate optimization function to solve it. General techniques for producing usable output in numerical and graphical form are also discussed. The course includes hands-on examples from a cross-section of application areas to reinforce important concepts.

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	Detailed course outline

Day 1 of 1
Optimization Fundamentals	Objective: Solving optimization problems effectively relies on an understanding of the basic structure and process of such problems. This chapter uses a hands-on example to introduce terminology and fundamental concepts, with a focus on realizing optimization in the MATLAB environment. What is optimization? Example: Designing a soup can Mathematical problem formulation Visual illustration of the problem Run an optimization using the Optimization Tool Interpret the results
Writing Objective Functions	Objective: This chapter focuses on how the quantity to be optimized can be expressed mathematically in MATLAB. Pros and cons of various implementations are highlighted. The objective function interface Coding guidelines Objective functions as input Function handle data type Handles to function files Anonymous functions
Expressing Constraints	Objective: This chapter discusses how to add constraints to an optimization problem in MATLAB. Different types of constraints are considered, as well as guidelines for efficient implementation. Types of constraints Defining linear constraints Bounds and general linear inequalities Linear equations Defining nonlinear constraints Constraint function interface Coding guidelines
Selecting Solvers and Options	Objective: Selecting the most appropriate algorithm for a given problem is critically important to a successful outcome. This chapter considers the different solvers available in Optimization Toolbox, and their associated options. Algorithm background Choosing the toolbox function Optimization parameters and options Command line functionality Understanding the output
Global Optimization	Objective: Many optimization problems have features that cause classical algorithms to fail or work inefficiently. This chapter introduces the extra solution methods available in Global Optimization Toolbox. Limits of the Optimization Toolbox algorithms Introduction to algorithms in Global Optimization Toolbox Example: global optimization Example: shift scheduling Genetic algorithms in depth Interpretation of results

Prerequisites

MATLAB Fundamentals, or equivalent experience using MATLAB. Knowledge of linear algebra and multivariate calculus is helpful.

Course Length - 1 day

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