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Course Highlights
An introduction to applied optimization in the MATLAB environment, focusing on using the Optimization Toolbox and the Genetic Algorithm and Direct Search Toolbox. The course introduces students to formulating and implementing optimization problems in the MATLAB environment. Emphasis is on problem identification, formulation, and choosing the appropriate optimization function for the problem at hand. 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.
Prerequisites
MATLAB Fundamentals or equivalent experience using MATLAB. Knowledge of linear algebra and multivariate calculus is helpful.
Course Outline
Introduction
Objective: Obtain a quick overview of The MathWorks and its family of products, discuss course set-up, materials, and logistics, and provide a "big picture" view of the course ahead.
- "Big picture" view of what you can do with the optimization tools provide by The MathWorks.
Optimization Fundamentals
Objective: We'll introduce the student to the fundamentals of applied optimization with a focus on realizing optimization in the MATLAB® environment. The student will learn how to formulate an optimization problem and be introduced to the Optimization Tool in MATLAB® via a hands-on example.
- What is optimization?
- Mathematical problem formulation
- Visual illustration of the problem
- Run an optimization using Optimtool
- Interpret the results
Writing Objective Functions
Objective: Implementing objective functions in various ways for later use in optimization functions
- The objective function interface
- Coding guidelines
- Objective functions as input
- Function handle data type
- Handles to m-files
- Anonymous functions
Expressing Constraints
Objective: Working with the various constraints in defining an optimization problem
- 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: The students will gain knowledge of the Optimization Toolbox algorithms to positively influence their performance by choosing appropriate solvers and options
- Algorithm background
- Choosing the toolbox function
- Optimization parameters and options
- Command line functionality
- Understanding the output
Genetic Algorithms and Direct Search
Objective: Introduce the students to alternative framework for solving optimization problems provided by the Genetic Algorithms and Direct Search Toolbox.
- Limits of the Optimization Toolbox product
- Introduction to algorithms in the Genetic Algorithms and Direct Search Toolbox product
- Genetic algorithms in depth
- Interpretation of results
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