Training - Courses

Objective: Understand the basic structure and process of solving optimization problems effectively. Attendees use a hands-on example that introduces terminology and fundamental concepts, with a focus on realizing optimization in the MATLAB environment.

• Optimization
• Example: Designing a soup can
• Mathematical problem formulation
• Visual illustration of the problem
• Running an optimization using the Optimization Tool
• Interpreting the results

Objective: Mathematically express the quantity to be optimized 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: 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: Select the most appropriate algorithm for a given problem by considering the different solvers and their associated options available in Optimization Toolbox.

• Algorithm background
• Choosing the toolbox function
• Optimization parameters and options
• Command line functionality
• Understanding the output

Objective: Understand the extra solution methods available in Global Optimization Toolbox and how to work on optimization problems with features that cause classical algorithms to fail or work inefficiently.

• 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