Main content

This entry is from Summer semester 2018 and might be obsolete. You can find a current equivalent here.

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): Oral examination
Examination type: Successful completion of at least 50 percent of the points from the weekly exercises as well as at least 2 presentations of the tasks.
Language,
Grading		 German,
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,
Regelmäßig alle 2 Semester
Person in charge of the module's outline Prof. Dr. Christian Komusiewicz

Contents

  • Approximation and online algorithms
  • Parameterized and exact algorithms
  • Randomized algorithms
  • Integer programming
  • Distributed algorithms
  • Algorithmic game theory
  • Streaming algorithms and external memory

Qualification Goals

The graduates of the module can

  • Design algorithms for computational problems from various application domains
  • Select an adequate algorithmic approach from a range of advanced algorithmic techniques for a specific computational problem,
  • Evaluate the quality of algorithms in different analysis models,
  • Prove the algorithmic difficulty of computational problems.

Prerequisites

Translation is missing. Here is the German original:

Keine. Empfohlen werden die Kompetenzen, die in den Modulen Algorithmen und Datenstrukturen sowie Effiziente Algorithmen vermittelt werden.


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 Minor subject Computer Science.


Recommended Reading

  • Kleinberg, Tardos. Algorithm Design. Pearson/Addison-Wesley, 2006.
  • Skiena, Steven S. The Algorithm Design Manual. Springer Verlag, 2008.
  • Cygan et al. Parameterized Algorithms. Springer Verlag, 2015.
  • 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 Summer semester 2018. 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.