About News Team Teaching Research Impressum

Theory of Optimization of Graphs and Networks

German Title: Theorie der Graphen- und Netzwerkoptimierung

Cycle: Every fall term

Regular Courses: Mathematics M.Sc., Data Science M.Sc.

Workload: 9 CP (4 SWS)


Recommended Prerequisites: Knowledge in discrete optimization (e.g. Optimization B), especially complexity of algorithms and knowledge in graph theory (e.g. Graph Theory I or Optimization B).

Content: Advanced algorithms for optimization problems on graphs, e.g. resource-limited shortest paths; dynamic flows; network design problems and/or maximum weighted matchings.

Learning Outcomes: Participants learn about extensions of common combinatorial algorithms and their application to optimization problems with resource limitations and time components. They acquire the ability to model complex questions from practice, to assess the limits and possibilities of known methods, to develop new solution methods and to classify the complexity of optimization problems.

Learning Outcomes: Oral Examination.

Exam Requirements: Solving and presentation of several excercises throughout the term.

Comments: Students from the business faculty who wish to take a 5CP exam, please contact the course advisors at the beginning of the term. Furthermore, note that this is an advanced Master's course in mathematics. It is not implossible, but rather challenging to take this with only QM/OR1 as background.