Advanced Computerlab Optimization

Course content

The students remember and understand the basic tasks and method of mathematicl algorithms and their praktical appliastion, are able to use mathematical programming tools, are able to apply, analyze and implement mathematical algorithms, are able to document and present mathematical algorithms.

The goal of the Advanced Computerlab Optimization is to combine advanced knowledge in mathematical optimization with practical planing and realization of large-scale optimization problems. To this end algorithms to solve complex mathematical models of mathematical optimization, partly known from the lectures "discrete optimization", continuos optimization" or various advanced courses in mathematical optimization, shall be implemented and tested. Thereby, the possibilities and limits will be explored. A sufficiently wide sub-field of optimization may serve as general theme, e.g. - Algorithms for scheduling, knapsack, coloring or routing problems. - Algorithms for differentiable or non-smooth non-linear optimization problems with or without constraints. As well-tested and highly efficient methods are available for central methods, it is important to be able to use such software (e.g. CPLEX, Gurobi, Matlab) for pertaining applications.

Course information

Code 1297006 + 1297007
Degree programme(s) Mathematics in Finance and Industry, Mathematics, Data Science
Lecturer(s) Prof. Dr. Christian Kirches, Prof. Dr. Maximilian Merkert, Prof. Dr. Sebastian Stiller
Type of course Lecture + exercise course
Semester Winter and summer semester
Language of instruction English
Level of study Master
ECTS credits 5
Contact person mathe-studium@tu-braunschweig.de