Course
Highlights
This 1-day course is an introduction to applied optimization methods in the MATLAB environment. It emphasizes the use of the Optimization Toolbox. The course introduces participants 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 on 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.
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
Optimization Fundamentals
Objective: We'll introduce the participants to the fundamentals of applied optimization with a focus on realizing optimization in the MATLAB® environment. The participant 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 participants 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
Case Studies
Objective: Working on case studies that are applicable in industries.
- Allocation model
- Covering model
- Blending model
- Transportation model
- General-network model with expanding flows
- General-network model with transformed flows
|
