Combinatorial Optimisation in Scientific Practise

Kombinatorische Optimierung in der wissenschaftlichen Praxis

Cycle:

Winter

Language:

German

Credits:

9

Contact:

copra@combi.rwth-aachen.de

Regular Courses:

  • Mathematics M.Sc.
  • Computational Engineering Science M.Sc.
  • Simulation Science M.Sc.

Exam:

  • Exam Requirements: Refer to the module handbook

Team

Christina Büsing
buesing@combi.rwth-aachen.de
  • Robust Optimisation
  • Combinatorial Optimisation
  • Healthcare Applications
Felix Engelhardt
engelhardt@combi.rwth-aachen.de
  • Optimisation Under Uncertainty
  • Robust Optimisation Of Energy Systems
  • Hospital Bed Management

Content

Students learn how to work scientifically in combinatorial optimization. This contains, on top of proving or disproving statements, the abilities to work with literature, describe a problem mathematically and to present results to a subject audience. Based on this, students create their own scientific works and they practice the publication process and conference participation.

Learning Targets

  • Students know the procedures of working scientifically. They know the differences between various types of publications.
  • Students can search for, read and classify scientific publications.
  • They are able to identify open research questions and to formulate such questions mathematically.
  • They can write scientifically and they know how to edit given texts.
  • Students can evaluate when and how statements can be verified using either formal, mathematical proof and/or empirical results.
  • Students can perform simple computational studies on the RWTH HPC-Cluster and they present corresponding results clearly and appropriately in oral and written form.

Recommended Prerequisites

  • basics of formal, mathematical proofs
  • combinatorial optimization (e.g. Optimisation B)
  • proficient in at least one advanced lecture/topic in discrete/combinatorial optimisation
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