Rasmus Kyng: Catalogue data in Autumn Semester 2020 |
Name | Prof. Dr. Rasmus Kyng |
Field | Theoretical Computer Science |
Address | Professur Theoretische Informatik ETH Zürich, OAT Z 19.2 Andreasstrasse 5 8092 Zürich SWITZERLAND |
Telephone | +41 44 632 73 30 |
kyng@inf.ethz.ch | |
Department | Computer Science |
Relationship | Assistant Professor (Tenure Track) |
Number | Title | ECTS | Hours | Lecturers | |
---|---|---|---|---|---|
252-0209-00L | Algorithms, Probability, and Computing ![]() | 8 credits | 4V + 2U + 1A | B. Gärtner, M. Ghaffari, R. Kyng, D. Steurer | |
Abstract | Advanced 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). | ||||
Learning objective | Studying and understanding of fundamental advanced concepts in algorithms, data structures and complexity theory. | ||||
Lecture notes | Will be handed out. | ||||
Literature | Introduction 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. | ||||
263-4410-00L | Seminar on Advanced Graph Algorithms and Optimization ![]() ![]() 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 credits | 2S | R. Kyng | |
Abstract | This seminar aims to familiarize students with current research topics in fast graph algorithms and optimization. | ||||
Learning objective | Read papers on cutting edge research topics; learn how to give a scientific talk. | ||||
Content | We will study recent papers that made significant contributions in the areas in fast graph algorithms and optimization. | ||||
Prerequisites / Notice | As 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. |