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Discrete and Combinatorial Optimisation

German Title: Diskrete und Kombinatorische Optimierung
Cycle: Every spring term
Regular Courses: Business Mathematics B.Sc.
Workload: 9 CP (4 SWS)

Recommended Prerequisites: Knowledge of Linear Optimization and Network Algorithms

Content: Minimum spanning tree, matchings in general graphs, foundations of approximation algorithms, foundations of discrete optimisation, foundations and algorithms of machine learning

Learning Outcomes: Participants have a deep understanding of terms, models and methods from linear and network optimisation. They understand algorithms for linear and network flow research and are able to analyse those. Participants are able to formulate real world problems as abstract and well known mathematical problems from discrete and combinatorial optimisation.

Exam Type: Oral or written examination
Exam Requirements: Solving and presentation of several excercises throughout the term