Rasmus Kyng: Catalogue data in Autumn Semester 2021

Name Prof. Dr. Rasmus Kyng
FieldTheoretical Computer Science
Professur Theoretische Informatik
ETH Zürich, OAT Z 19.2
Andreasstrasse 5
8092 Zürich
Telephone+41 44 632 73 30
DepartmentComputer Science
RelationshipAssistant Professor (Tenure Track)

252-0209-00LAlgorithms, Probability, and Computing Information 8 credits4V + 2U + 1AB. Gärtner, M. Ghaffari, R. Kyng, A. Steger, D. Steurer
AbstractAdvanced design and analysis methods for algorithms and data structures: Random(ized) Search Trees, Point Location, Minimum Cut, Linear Programming, Randomized Algebraic Algorithms (matchings), Probabilistically Checkable Proofs (introduction).
ObjectiveStudying and understanding of fundamental advanced concepts in algorithms, data structures and complexity theory.
Lecture notesWill be handed out.
LiteratureIntroduction to Algorithms by T. H. Cormen, C. E. Leiserson, R. L. Rivest;
Randomized Algorithms by R. Motwani und P. Raghavan;
Computational Geometry - Algorithms and Applications by M. de Berg, M. van Kreveld, M. Overmars, O. Schwarzkopf.
263-4410-00LSeminar on Advanced Graph Algorithms and Optimization Information Restricted registration - show details
Number of participants limited to 6!
The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.
2 credits2SR. Kyng
AbstractThis seminar aims to familiarize students with current research topics in fast graph algorithms and optimization.
ObjectiveRead papers on cutting edge research topics; learn how to give a scientific talk.
ContentWe will study recent papers that made significant contributions in the areas in fast graph algorithms and optimization.
Prerequisites / NoticeAs prerequisite we require that you passed the course "Advanced Graph Algorithms and Optimization". In exceptional cases, students who passed one of the courses "Randomized Algorithms and Probabilistic Methods", "Optimization for Data Science", or "Advanced Algorithms" may also participate, at the discretion of the lecturer.