This repository provides code examples written in Python in addition to the exercises and introductory course of the practical optimization lecture at TU Dortmund University.
In the optimization of complex systems, especially in engineering sciences, it usually turns out quickly that the range of analytical and exact solution methods is too limited for practice due to idealizing prerequisites. Therefore, practical optimization deals with those solution approaches which have proven to be effective for problem classes relevant in practice, such as non-convex optimization, optimization under uncertainty, multi-objective optimization and finally symbolic optimization. Methodically, direct deterministic search methods as well as evolutionary algorithms are used.
https://ls11-www.cs.tu-dortmund.de/people/rudolph/teaching/lectures/POKS/SS2022/lecture.jsp