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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, 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
Students will be able to
- design algorithms for computational problems from a wide variety of application contexts,
- select an adequate algorithmic approach from a range of advanced algorithmic techniques for a concrete computational problem,
- evaluate the goodness of algorithms in different analytical models,
- 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. Data Science, this module can be attended in the study area Free Compulsory Elective Modules.
The module is assigned to Computer Science. Further information on eligibility can be found in the description of the study area.
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 Winter semester 2023/24. 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:
- Winter 2016/17 (no corresponding element)
- Summer 2018
- Winter 2018/19
- Winter 2019/20
- Winter 2020/21
- Summer 2021
- Winter 2021/22
- Winter 2022/23
- Winter 2023/24
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.