Rasmus Kyng: Katalogdaten im Frühjahrssemester 2020

NameHerr Prof. Dr. Rasmus Kyng
LehrgebietTheoretische Informatik
Professur Theoretische Informatik
ETH Zürich, OAT Z 19.2
Andreasstrasse 5
8092 Zürich
Telefon+41 44 632 73 30
BeziehungAssistenzprofessor (Tenure Track)

252-4225-00LPresenting Theoretical Computer Science Belegung eingeschränkt - Details anzeigen
Number of participants limited to 24.

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 KP2SB. Gärtner, M. Ghaffari, R. Kyng, A. Steger, D. Steurer, E. Welzl
KurzbeschreibungStudents present current or classical results from theoretical computer science.
LernzielStudents learn to read, understand and present results from theoretical computer science. The main focus and deliverable is a good presentation of 45 minutes that can easily be followed and understood by the audience.
InhaltStudents present current or classical results from theoretical computer science.
Voraussetzungen / BesonderesThe seminar takes place as a block seminar on two Saturdays in April and/or May. Each presentation is jointly prepared and given by two students (procedure according to the seminar website).
All students must attend all presentations.
263-4400-00LAdvanced Graph Algorithms and Optimization Information Belegung eingeschränkt - Details anzeigen
Number of participants limited to 30.
5 KP3G + 1AR. Kyng
KurzbeschreibungThis course will cover a number of advanced topics in optimization and graph algorithms.
LernzielThe course will take students on a deep dive into modern approaches to
graph algorithms using convex optimization techniques.

By studying convex optimization through the lens of graph algorithms,
students should develop a deeper understanding of fundamental
phenomena in optimization.

The course will cover some traditional discrete approaches to various graph
problems, especially flow problems, and then contrast these approaches
with modern, asymptotically faster methods based on combining convex
optimization with spectral and combinatorial graph theory.
InhaltStudents should leave the course understanding key
concepts in optimization such as first and second-order optimization,
convex duality, multiplicative weights and dual-based methods,
acceleration, preconditioning, and non-Euclidean optimization.

Students will also be familiarized with central techniques in the
development of graph algorithms in the past 15 years, including graph
decomposition techniques, sparsification, oblivious routing, and
spectral and combinatorial preconditioning.
Voraussetzungen / BesonderesThis course is targeted toward masters and doctoral students with an
interest in theoretical computer science.

Students should be comfortable with design and analysis of algorithms, probability, and linear algebra.

Having passed the course Algorithms, Probability, and Computing (APC) is highly recommended, but not formally required. If you are not
sure whether you're ready for this class or not, please consult the