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RWTHx: Mathematical Optimization for Engineers
Learn the mathematical and computational basics for applying optimization successfully. Master the different formulations and the important concepts behind their solution methods. Learn to implement and solve optimization problems in Python through the practical exercises.
This Course Includes
edx
4.3 (9 reviews )
8 weeks at 6-8 hours per week
english
Online - Self Paced
course
RWTHx
About RWTHx: Mathematical Optimization for Engineers
Today, for almost every product on the market and almost every service offered, some form of optimization has played a role in their design.
However, optimization is not a button-press technology. To apply it successfully, one needs expertise in formulating the problem, selecting and tuning the solution algorithm and finally, checking the results. We have designed this course to make you such an expert.
This course is useful to students of all engineering fields. The mathematical and computational concepts that you will learn here have application in machine learning, operations research, signal and image processing, control, robotics and design to name a few.
We will start with the standard unconstrained problems, linear problems and general nonlinear constrained problems. We will then move to more specialized topics including mixed-integer problems; global optimization for non-convex problems; optimal control problems; machine learning for optimization and optimization under uncertainty. Students will learn to implement and solve optimization problems in Python through the practical exercises.
What You Will Learn?
- Mathematical definitions of objective function, degrees of freedom, constraints and optimal solution.
- Mathematical as well as intuitive understanding of optimality conditions.
- Different optimization formulations (unconstrained v/s constrained; linear v/s nonlinear; mixed-integer v/s continuous; time-continuous or dynamic; optimization under uncertainty).
- Fundamentals of the solution methods for each these formulations.
- Optimization with machine learning embedded.
- Hands-on training in implementing and solving optimization problems in Python, as exercises.