|Algorithm Engineering [2022 Sommer]|
every summer semester
Lecture 4 SWS + Exercise course 2 SWS
90h lectures and tutorials,
15h exam preparations,
135h lecture wrap-up and homework
M.Sc. Angewandte Informatik
M.Sc. Data and Computer Science
M.Sc. Scientific Computing
|Lernziele||Students obtain a systematic understanding of algorithmic questions and solution approaches in the area of algorithm engineering.
The students will be able to transfer the learned techniques onto similar problems and be able to interpret and understand current research topics in the area of algorithm engineering.
Given a real-world problem, students are able to select appropriate algorithms to come up with and implement efficient solutions.
In particular, students know realistic machine models and applications, algorithm design, implementation techniques, experimental methodology and can interpret of measurements.
|Lerninhalte||The listed abilities will be learned by concrete examples. In particular, we will almost always cover the best practical and theoretical methods. This methods often deviate a lot by the algorithms learned in the basic courses. To this end the lecture covers FPT/Kernelization in practice (independent set, vertex cover, (all) minimum cuts (NOI algorithm), clique cover, node ordering), multi-level algorithms (graph partitioning, modularity clustering, dynamic clustering, process mapping, spectral techniques, exact approaches), route planning (contraction hierarchies, arc-flags, hub-label algorithm), dynamic graph algorithms (single-source reachability, transitive closure, matching, minimum cuts, graph generation).|
Einführung in die Praktische Informatik (IPI), Programmierkurs (IPK), Algorithmen und Datenstrukturen (IAD), Mathematik für Informatiker 1 oder Lineare Algebra 1 (MA4), Algorithms and Data Structures 2
|Vergabe der LP und Modulendnote||The module is completed with a graded oral exam. The final grade of the module is determined by the grade of the exam. The requirements for the assignment of credits follows the regulations in section modalities for exams.|
|Nützliche Literatur||Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein: Introduction to Algorithms, 3rd Edition. MIT Press 2009, ISBN 978-0-262-03384-8, pp. I-XIX, 1-1292
Jon M. Kleinberg, Éva Tardos: Algorithm design. Addison-Wesley 2006, ISBN 978-0-321-37291-8, pp. I-XXIII, 1-838
Stefan Näher: LEDA, a Platform for Combinatorial and Geometric Computing. Handbook of Data Structures and Applications 2004