Rico Zenklusen: Catalogue data in Spring Semester 2023 |  |
| 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 |
| 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 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 364-1058-00L | Risk Center Seminar Series | 0 credits | 2S | H. Schernberg, D. Basin, A. Bommier, D. N. Bresch, S. Brusoni, L.‑E. Cederman, P. Cheridito, F. Corman, H. Gersbach, C. Hölscher, K. Paterson, G. Sansavini, B. Stojadinovic, B. Sudret, J. Teichmann, R. Wattenhofer, U. A. Weidmann, S. Wiemer, R. Zenklusen | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | In this series of seminars, invited speakers discuss various topics in the area of risk modelling, governance of complex socio-economic systems, managing risks and crises, and building resilience. Students, PhD students, post-docs, faculty and individuals outside ETH are welcome. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Participants gain insights in a broad range of risk- and resilience-related topics. They expand their knowledge of the field and deepen their understanding of the complexity of our social, economic and engineered systems. For young researchers in particular, the seminars offer an opportunity to learn academic presentation skills and to network with an interdisciplinary scientific audience. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Academic presentations from ETH faculty as well as external researchers. Each seminar is followed by a Q&A session and (when permitted) a networking Apéro. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | The sessions are recorded whenever possible and posted on the ETH Risk Center webpage. If available, presentation slides are shared as well. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Each speaker will provide a literature review. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | In most cases, a quantitative background is required. Depending on the topic, field-specific knowledge may be required. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Competencies |
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| 401-3900-16L | Advanced Topics in Discrete Optimization  Number of participants limited to 12. | 4 credits | 2S | R. Zenklusen, D. E. K. Hershkowitz, R. Santiago Torres | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 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 "Linear & Combinatorial Optimization". | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
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| 401-3902-DRL | Network & Integer Optimization: From Theory to Application Only for ETH D-MATH doctoral students and for doctoral students from the Institute of Mathematics at UZH. The latter need to send an email to Jessica Bolsinger (info@zgsm.ch) with the course number. The email should have the subject „Graduate course registration (ETH)“. | 2 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 Linear & Combinatorial Optimization is a plus. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
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| 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 Linear & Combinatorial Optimization is a plus. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
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| 401-5660-00L | DACO Seminar | 0 credits | 1K | A. Bandeira, R. Weismantel, R. Zenklusen | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Research colloquium | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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