Hands-on coding-based course on using mathematical optimization methods and software to solve a variety of optimization problems.
Learning objective
The goal of this course is to learn how to put mathematical optimization theory into practice, by learning how to write code in python using modern mathematical optimization libraries. At the end of this course, students should be able to implement algorithms that can tackle a wide variety of mathematical optimization problems.
Content
Key topics include: - Modeling computational questions in terms of classical mathematical optimization problems, and implementing algorithms to solve these fast. - Key techniques in practical optimization.
Lecture notes
See moodle page.
Literature
Necessary materials will be provided on moodle.
Prerequisites / Notice
Solid background in Linear Algebra. Preliminary knowledge of Linear Programming and Integer Programming is ideal but not a strict requirement. Prior attendance of more foundational mathematical optimization courses, like Linear & Combinatorial Optimization, Integer Programming, or Convex Optimization is a plus but not necessary.
