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Combinatorial Optimisationen (formerly: Optimisation B)

German Title: Kombinatorische Optimierung
Cycle: Every fall term
Regular Courses: Data Science M.Sc.
Workload: 9 CP (6 SWS)

Recommended Prerequisites: Graph theoretical optimization problems (spanning tree problem, matching problems) from an algorithmic point of view, network flow problems, basics of integer linear optimization (branch-and- bound, total unimodularity, total dual integrality), computational complexity theory (classes P and NP, NP-complete problems), approximation algorithms, matroids.

Learning Outcomes: Knowledge of the most important algorithmic methods and structural insights of combinatorial optimization problems. Capability of classification of optimization problems according their complexity.

Exam Type: Oral or written examination