Name | Prof. Dr. Angelika Steger |

Field | Informatik (Theoretische Informatik) |

Address | Inst. f. Theoretische Informatik ETH Zürich, OAT Z 27 Andreasstrasse 5 8092 Zürich SWITZERLAND |

steger@inf.ethz.ch | |

URL | http://www.cadmo.ethz.ch/as/people/professor/asteger/index |

Department | Computer Science |

Relationship | Full Professor |

Number | Title | ECTS | Hours | Lecturers | |
---|---|---|---|---|---|

252-0209-00L | Algorithms, Probability, and Computing | 8 credits | 4V + 2U + 1A | E. Welzl, M. Ghaffari, A. Steger, D. Steurer, P. Widmayer | |

Abstract | Advanced design and analysis methods for algorithms and data structures: Random(ized) Search Trees, Point Location, Minimum Cut, Linear Programming, Randomized Algebraic Algorithms (matchings), Probabilistically Checkable Proofs (introduction). | ||||

Learning objective | Studying and understanding of fundamental advanced concepts in algorithms, data structures and complexity theory. | ||||

Lecture notes | Will be handed out. | ||||

Literature | Introduction to Algorithms by T. H. Cormen, C. E. Leiserson, R. L. Rivest; Randomized Algorithms by R. Motwani und P. Raghavan; Computational Geometry - Algorithms and Applications by M. de Berg, M. van Kreveld, M. Overmars, O. Schwarzkopf. | ||||

252-0417-00L | Randomized Algorithms and Probabilistic Methods | 8 credits | 3V + 2U + 2A | A. Steger | |

Abstract | Las Vegas & Monte Carlo algorithms; inequalities of Markov, Chebyshev, Chernoff; negative correlation; Markov chains: convergence, rapidly mixing; generating functions; Examples include: min cut, median, balls and bins, routing in hypercubes, 3SAT, card shuffling, random walks | ||||

Learning objective | After this course students will know fundamental techniques from probabilistic combinatorics for designing randomized algorithms and will be able to apply them to solve typical problems in these areas. | ||||

Content | Randomized Algorithms are algorithms that "flip coins" to take certain decisions. This concept extends the classical model of deterministic algorithms and has become very popular and useful within the last twenty years. In many cases, randomized algorithms are faster, simpler or just more elegant than deterministic ones. In the course, we will discuss basic principles and techniques and derive from them a number of randomized methods for problems in different areas. | ||||

Lecture notes | Yes. | ||||

Literature | - Randomized Algorithms, Rajeev Motwani and Prabhakar Raghavan, Cambridge University Press (1995) - Probability and Computing, Michael Mitzenmacher and Eli Upfal, Cambridge University Press (2005) | ||||

252-0851-00L | Algorithms and Complexity | 4 credits | 2V + 1U | J. Lengler, A. Steger | |

Abstract | Introduction: RAM machine, data structures; Algorithms: sorting, median, matrix multiplication, shortest paths, minimal spanning trees; Paradigms: divide & conquer, dynamic programming, greedy algorithms; Data Structures: search trees, dictionaries, priority queues; Complexity Theory: P and NP, NP-completeness, Cook's theorem, reductions. | ||||

Learning objective | After this course students know some basic algorithms as well as underlying paradigms. They will be familiar with basic notions of complexity theory and can use them to classify problems. | ||||

Content | Die Vorlesung behandelt den Entwurf und die Analyse von Algorithmen und Datenstrukturen. Die zentralen Themengebiete sind: Sortieralgorithmen, Effiziente Datenstrukturen, Algorithmen für Graphen und Netzwerke, Paradigmen des Algorithmenentwurfs, Klassen P und NP, NP-Vollständigkeit, Approximationsalgorithmen. | ||||

Lecture notes | Ja. Wird zu Beginn des Semesters verteilt. | ||||

252-4202-00L | Seminar in Theoretical Computer Science 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 credits | 2S | E. Welzl, B. Gärtner, M. Hoffmann, J. Lengler, A. Steger, B. Sudakov | |

Abstract | Presentation of recent publications in theoretical computer science, including results by diploma, masters and doctoral candidates. | ||||

Learning objective | The goal is to introduce students to current research, and to enable them to read, understand, and present scientific papers. | ||||

263-0006-00L | Algorithms LabOnly for master students, otherwise a special permission by the student administration of D-INFK is required. | 8 credits | 4P + 3A | A. Steger, E. Welzl, P. Widmayer | |

Abstract | Students learn how to solve algorithmic problems given by a textual description (understanding problem setting, finding appropriate modeling, choosing suitable algorithms, and implementing them). Knowledge of basic algorithms and data structures is assumed; more advanced material and usage of standard libraries for combinatorial algorithms are introduced in tutorials. | ||||

Learning objective | The objective of this course is to learn how to solve algorithmic problems given by a textual description. This includes appropriate problem modeling, choice of suitable (combinatorial) algorithms, and implementing them (using C/C++, STL, CGAL, and BGL). | ||||

Literature | T. Cormen, C. Leiserson, R. Rivest: Introduction to Algorithms, MIT Press, 1990. J. Hromkovic, Teubner: Theoretische Informatik, Springer, 2004 (English: Theoretical Computer Science, Springer 2003). J. Kleinberg, É. Tardos: Algorithm Design, Addison Wesley, 2006. H. R. Lewis, C. H. Papadimitriou: Elements of the Theory of Computation, Prentice Hall, 1998. T. Ottmann, P. Widmayer: Algorithmen und Datenstrukturen, Spektrum, 2012. R. Sedgewick: Algorithms in C++: Graph Algorithms, Addison-Wesley, 2001. | ||||

263-4110-00L | Interdisciplinary Algorithms Lab Number of participants limited to 12. In the Master Programme max. 10 credits can be accounted by Labs on top of the Interfocus Courses. Additional Labs will be listed on the Addendum. | 5 credits | 2P | A. Steger, D. Steurer, J. Lengler | |

Abstract | In this course students will develop solutions for algorithmic problems posed by researchers from other fields. | ||||

Learning objective | Students will learn that in order to tackle algorithmic problems from an interdisciplinary or applied context one needs to combine a solid understanding of algorithmic methodology with insights into the problem at hand to judge which side constraints are essential and which can be loosened. | ||||

Prerequisites / Notice | Students will work in teams. Ideally, skills of team members complement each other. Interested Bachelor students can apply for participation by sending an email to steger@inf.ethz.ch explaining motivation and transcripts. |