Rico Zenklusen: Catalogue data in Spring Semester 2021

Name	Prof. Dr. Rico Zenklusen
Field	Mathematics
Address	Institut für Operations Research ETH Zürich, HG G 22.4 Rämistrasse 101 8092 Zürich SWITZERLAND
Telephone	+41 44 633 93 42
E-mail	ricoz@ethz.ch
URL	https://math.ethz.ch/ifor/groups/zenklusen_group/rico-zenklusen.html
Department	Mathematics
Relationship	Full Professor

Number	Title	ECTS	Hours	Lecturers
364-1058-00L	Risk Center Seminar Series	0 credits	2S	G. Sansavini, D. Basin, A. Bommier, D. N. Bresch, L.‑E. Cederman, P. Cheridito, H. Gersbach, F. Schweitzer, D. Sornette, B. Stojadinovic, B. Sudret, U. A. Weidmann, S. Wiemer, M. Zeilinger, R. Zenklusen
Abstract	This 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 and governing complex socio-economic systems, and managing risks and crises. Students and other guests are welcome.
Learning objective	Participants 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 and approaches for open problems, to analyze them with computers or other means, 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.
Content	This 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 seminar. Students and other guests are welcome.
Lecture notes	There is no script, but the sessions will be recorded and be made available. Transparencies of the presentations may be put on the course webpage.
Literature	Literature will be provided by the speakers in their respective presentations.
Prerequisites / Notice	Participants should have relatively good scientific, in particular mathematical skills and some experience of how scientific work is performed.
401-3900-16L	Advanced Topics in Discrete Optimization Number of participants limited to 12.	4 credits	2S	R. Zenklusen, R. Santiago Torres, V. Traub
Abstract	In this seminar we will discuss selected topics in discrete optimization. The main focus is on mostly recent research papers in the field of Combinatorial Optimization.
Learning objective	The goal of the seminar is twofold. First, we aim at improving students' presentation and communication skills. In particular, students are to present a research paper to their peers and the instructors in a clear and understandable way. Second, students learn a selection of recent cutting-edge approaches in the field of Combinatorial Optimization by attending the other students' talks. A very active participation in the seminar helps students to build up the necessary skills for parsing and digesting advanced technical texts on a significantly higher complexity level than usual textbooks. A key goal is that students prepare their presentations in a concise and accessible way to make sure that other participants get a clear idea of the presented results and techniques. Students intending to do a project in optimization are strongly encouraged to participate.
Content	The selected topics will cover various classical and modern results in Combinatorial Optimization. Contrary to prior years, a very significant component of the seminar will be interactive discussions where active participation of the students is required.
Literature	The learning material will be in the form of scientific papers.
Prerequisites / Notice	Requirements: We expect students to have a thorough understanding of topics covered in the course "Mathematical Optimization".
401-3902-21L	Network & Integer Optimization: From Theory to Application	6 credits	3G	R. Zenklusen
Abstract	This course covers various topics in Network and (Mixed-)Integer Optimization. It starts with a rigorous study of algorithmic techniques for some network optimization problems (with a focus on matching problems) and moves to key aspects of how to attack various optimization settings through well-designed (Mixed-)Integer Programming formulations.
Learning objective	Our goal is for students to both get a good foundational understanding of some key network algorithms and also to learn how to effectively employ (Mixed-)Integer Programming formulations, techniques, and solvers, to tackle a wide range of discrete optimization problems.
Content	Key topics include: - Matching problems; - Integer Programming techniques and models; - Extended formulations and strong problem formulations; - Solver techniques for (Mixed-)Integer Programs; - Decomposition approaches.
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. - Vanderbeck François, Wolsey Laurence: Reformulations and Decomposition of Integer Programs. Chapter 13 in: 50 Years of Integer Programming 1958-2008. Springer, 2010. - Alexander Schrijver: Theory of Linear and Integer Programming. John Wiley, 1986.
Prerequisites / Notice	Solid background in linear algebra. Preliminary knowledge of Linear Programming is ideal but not a strict requirement. Prior attendance of the course Mathematical Optimization is a plus.
401-5900-00L	Optimization Seminar	0 credits	1K	A. Bandeira, R. Weismantel, R. Zenklusen
Abstract	Lectures on current topics in optimization.
Learning objective	This lecture series introduces graduate students to ongoing research activities (including applications) in the domain of optimization.
Content	This seminar is a forum for researchers interested in optimization theory and its applications. Speakers, invited from both academic and non-academic institutions, are expected to stimulate discussions on theoretical and applied aspects of optimization and related subjects. The focus is on efficient (or practical) 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.