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CS 627 — Advanced Algorithmics
(dt. Höhere Algorithmik)

Level, degree of commitment Specialization module, compulsory elective module
Forms of teaching and learning,
workload		 Lecture (4 SWS), recitation class (2 SWS),
270 hours (90 h attendance, 180 h private study)
Credit points,
formal requirements		 9 CP
Course requirement(s): Successful completion of at least 50 percent of the points from the weekly exercises as well as at least 2 presentations of the tasks.
Examination type: Oral examination (individual examination)
Language,
Grading		 English,
The grading is done with 0 to 15 points according to the examination regulations for the degree program M.Sc. Computer Science.
Duration,
frequency		 One semester,
irregular
Person in charge of the module's outline Prof. Dr. Sebastian Wild

Contents

  • Approximation algorithms
  • Parameterized and exact algorithms
  • Randomized algorithms
  • Linear Programming, Primal-Dual Algorithms
  • Complexity theory

Qualification Goals

The students

  • can design algorithms for computational problems from a wide range of application contexts,
  • can select an appropriate algorithmic approach from a range of advanced algorithmic techniques for a specific computational problem,
  • can assess the quality of algorithms in different analysis models,
  • can demonstrate the algorithmic difficulty of computational problems.

Prerequisites

None. The competences taught in the following modules are recommended: either Algorithms and Data Structures or Practical Informatics II: Data Structures and Algorithms for Pre-Service-Teachers, Efficient Algorithms.


Applicability

Module imported from M.Sc. Computer Science.

It can be attended at FB12 in study program(s)

  • M.Sc. Data Science
  • M.Sc. Computer Science
  • M.Sc. Mathematics

When studying M.Sc. Mathematics, this module can be attended in the study area Profile Area Computer Science.


Recommended Reading

  • J. Hromkovič: Algorithmics for Hard Problems.
  • Cygan et al. Parameterized Algorithms. Springer Verlag, 2015.
  • J. Flum, M. Grohe: Parametrized Complexity Theory.
  • M. Mitzenmachen, E. Upfal: Probability and Computing - Randomized Algorithms and Probabilistic Analysis.
  • S. Arora, B. Barak: Computation Complexity - A Modern Approach.
  • V. Vazirani: Approximation Algorithms.
  • Kleinberg, Tardos. Algorithm Design. Pearson/Addison-Wesley, 2006.
  • Skiena, Steven S. The Algorithm Design Manual. Springer Verlag, 2008.
  • Williamson, Shmoys. The Design Of Approximation Algorithms. Cambridge University Press, 2011.



Please note:

This page describes a module according to the latest valid module guide in Winter semester 2025/26. Most rules valid for a module are not covered by the examination regulations and can therefore be updated on a semesterly basis. The following versions are available in the online module guide:

The module guide contains all modules, independent of the current event offer. Please compare the current course catalogue in Marvin.

The information in this online module guide was created automatically. Legally binding is only the information in the examination regulations (Prüfungsordnung). If you notice any discrepancies or errors, we would be grateful for any advice.