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Optimization Techniques in MATLAB
Learn to use optimization and global optimization techniques in MATLAB to solve problems with many variables
Course
Highlights
This one-day course introduces applied optimization in the MATLAB® environment, focusing on using Optimization Toolbox™ and Global Optimization Toolbox. Topics include:
- Running optimization problems in MATLAB
- Specifying objective functions and constraints
- Choosing solvers and improving performance
- Global and multiobjective optimization
Who Should
Attend
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
Prerequisites
MATLAB Fundamentals . Knowledge of linear algebra and multivariate calculus is helpful.
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 - Write objective function files
- Add constraints
- Select appropriate solver and algorithm
- Interpret the output from solver
- Increase accuracy
Partners
Upcoming Program
- Please keep me posted on the next schedule
- Please contact me to arrange customized/ in-house training
Course Outline
Running an Optimization Problem
Objective: Understand the basic structure and process of solving optimization problems effectively. Use interactive tools to define and solve optimization problems.
- Identifying the problem components
- Running an optimization using the Live Editor Optimization Task
- Applying the optimization process
- Using optimization functions
Specifying Objective Functions and Constraints
Objective: Write an optimization problem. Use problem-based workflow to arrive at a solution.
- Using the problem-based workflow
- Specifying objective functions and constraints
- Identifying different types of constraints
Choosing a Solver and Improving Performance
Objective: Select an appropriate solver and algorithm by considering the type of optimization problem to be solved. Interpret the output from the solver and diagnose the progress of an optimization.
- Classifying the objective
- Choosing a solver and algorithm
- Examining and interpreting the result
- Providing derivative information
Global and Multiobjective Optimization
Objective: Use Global Optimization Toolbox functionality to solve problems where classical algorithms fail or work inefficiently. Solve problems with many objectives.
- Finding the global minimum
- Using genetic algorithms, direct search methods and surrogate optimization
- Use multiobjective solvers