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

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

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

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

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