Martin Nägele: Katalogdaten im Frühjahrssemester 2025 |
| Name | Herr Dr. Martin Nägele |
| Adresse | Institut für Operations Research ETH Zürich, HG G 22.2 Rämistrasse 101 8092 Zürich SWITZERLAND |
| Telefon | +41 44 632 97 59 |
| martin.naegele@math.ethz.ch | |
| Departement | Mathematik |
| Beziehung | Dozent |
| Nummer | Titel | ECTS | Umfang | Dozierende | ||||||||||||||||||||||||||
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| 401-3090-25L | Mathematical Optimization Lab  | 5 KP | 3G | M. Nägele | ||||||||||||||||||||||||||
| Kurzbeschreibung | Hands-on coding-based course on using mathematical optimization methods and software to solve a variety of optimization problems. | |||||||||||||||||||||||||||||
| Lernziel | The goal of this course is to learn how to put mathematical optimization theory into practice, by learning how to write code in python using modern mathematical optimization libraries. At the end of this course, students should be able to implement algorithms that can tackle a wide variety of mathematical optimization problems. | |||||||||||||||||||||||||||||
| Inhalt | Key topics include: - Modeling computational questions in terms of classical mathematical optimization problems, and implementing algorithms to solve these fast. - Key techniques in practical optimization. | |||||||||||||||||||||||||||||
| Skript | See moodle page. | |||||||||||||||||||||||||||||
| Literatur | Necessary materials will be provided on moodle. | |||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Solid background in Linear Algebra. Preliminary knowledge of Linear Programming and Integer Programming is ideal but not a strict requirement. Prior attendance of more foundational mathematical optimization courses, like Linear & Combinatorial Optimization, Integer Programming, or Convex Optimization is a plus but not necessary. | |||||||||||||||||||||||||||||
Kompetenzen |
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| 401-3902-21L | Network & Integer Optimization | 5 KP | 3G | M. Nägele | ||||||||||||||||||||||||||
| Kurzbeschreibung | 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. | |||||||||||||||||||||||||||||
| Lernziel | 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. | |||||||||||||||||||||||||||||
| Inhalt | Key topics include: - Matching problems; - Integer Programming techniques and models; - Extended formulations and strong problem formulations; - Solver techniques for (Mixed-)Integer Programs; - Decomposition approaches. | |||||||||||||||||||||||||||||
| Literatur | - 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. | |||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | 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. | |||||||||||||||||||||||||||||
| Kompetenzen |
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