Rico Zenklusen: Catalogue data in Autumn Semester 2019

Award: The Golden Owl
Name Prof. Dr. Rico Zenklusen
FieldMathematics
Address
Institut für Operations Research
ETH Zürich, HG G 22.4
Rämistrasse 101
8092 Zürich
SWITZERLAND
Telephone+41 44 633 93 42
E-mailricoz@ethz.ch
URLhttps://math.ethz.ch/ifor/groups/zenklusen_group/rico-zenklusen.html
DepartmentMathematics
RelationshipFull Professor

NumberTitleECTSHoursLecturers
364-1058-00LRisk Center Seminar Series0 credits2SB. Stojadinovic, D. Basin, A. Bommier, D. N. Bresch, L.‑E. Cederman, P. Cheridito, H. Gersbach, H. R. Heinimann, M. Larsson, G. Sansavini, F. Schweitzer, D. Sornette, B. Sudret, U. A. Weidmann, S. Wiemer, M. Zeilinger, R. Zenklusen
AbstractThis course is a mixture between a seminar primarily for PhD and postdoc students and a colloquium involving invited speakers. It consists of presentations and subsequent discussions in the area of modeling complex socio-economic systems and crises. Students and other guests are welcome.
ObjectiveParticipants should learn to get an overview of the state of the art in the field, to present it in a well understandable way to an interdisciplinary scientific audience, to develop novel mathematical models for open problems, to analyze them with computers, and to defend their results in response to critical questions. In essence, participants should improve their scientific skills and learn to work scientifically on an internationally competitive level.
ContentThis course is a mixture between a seminar primarily for PhD and postdoc students and a colloquium involving invited speakers. It consists of presentations and subsequent discussions in the area of modeling complex socio-economic systems and crises. For details of the program see the webpage of the colloquium. Students and other guests are welcome.
Lecture notesThere is no script, but a short protocol of the sessions will be sent to all participants who have participated in a particular session. Transparencies of the presentations may be put on the course webpage.
LiteratureLiterature will be provided by the speakers in their respective presentations.
Prerequisites / NoticeParticipants should have relatively good mathematical skills and some experience of how scientific work is performed.
401-3901-00LMathematical Optimization Information 11 credits4V + 2UR. Zenklusen
AbstractMathematical treatment of diverse optimization techniques.
ObjectiveThe goal of this course is to get a thorough understanding of various classical mathematical optimization techniques with an emphasis on polyhedral approaches. In particular, we want students to develop a good understanding of some important problem classes in the field, of structural mathematical results linked to these problems, and of solution approaches based on this structural understanding.
ContentKey topics include:
- Linear programming and polyhedra;
- Flows and cuts;
- Combinatorial optimization problems and techniques;
- Equivalence between optimization and separation;
- Brief introduction to Integer Programming.
Literature- Bernhard Korte, Jens Vygen: Combinatorial Optimization. 6th edition, Springer, 2018.
- Alexander Schrijver: Combinatorial Optimization: Polyhedra and Efficiency. Springer, 2003. This work has 3 volumes.
- Ravindra K. Ahuja, Thomas L. Magnanti, James B. Orlin. Network Flows: Theory, Algorithms, and Applications. Prentice Hall, 1993.
- Alexander Schrijver: Theory of Linear and Integer Programming. John Wiley, 1986.
Prerequisites / NoticeSolid background in linear algebra.
401-5900-00LOptimization Seminar Information 0 credits1KA. Bandeira, R. Weismantel, R. Zenklusen
AbstractLectures on current topics in optimization
ObjectiveExpose graduate students to ongoing research acitivites (including applications) in the domain of otimization.
ContentThis seminar is a forum for researchers interested in optimization theory and its applications. Speakers are expected to stimulate discussions on theoretical and applied aspects of optimization and related subjects. The focus is on efficient algorithms for continuous and discrete optimization problems, complexity analysis of algorithms and associated decision problems, approximation algorithms, mathematical modeling and solution procedures for real-world optimization problems in science, engineering, industries, public sectors etc.