## Hans-Joachim Böckenhauer: Catalogue data in Spring Semester 2020 |

Name | Dr. Hans-Joachim Böckenhauer |

Consultation hours | By appointment |

Address | Professur Algorithmen und Didaktik ETH Zürich, CAB F 11 Universitätstrasse 6 8092 Zürich SWITZERLAND |

Telephone | +41 44 632 81 83 |

Fax | +41 44 632 13 90 |

hjb@inf.ethz.ch | |

URL | http://www.ite.ethz.ch/people/hjb/ |

Department | Computer Science |

Relationship | Lecturer |

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

252-4910-00L | Algorithmics for Hard Problems 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. Number of participants limited to 24. | 2 credits | 2S | H.‑J. Böckenhauer, R. Kralovic | |

Abstract | This seminar looks into modern algorithmic approaches for solving computationally hard problems; e.g., approximation algorithms, moderately exponential-time algorithms, parameterized algorithms and combinations thereof. The focus will be on approaches with provable performance guarantees. | ||||

Objective | To systematically acquire an overview of the methods for solving hard problems with provable performance guarantees. To get deeper knowledge of both approximation algorithms and exact and parameterized algorithms. | ||||

Content | In this seminar, we will discuss algorithmic approaches for solving computationally hard problems. In the kick-off meeting, we will give a brief overview of these approaches, including approximation algorithms and parameterizations. Then, each participant will study one aspect of this topic, following a specific scientific publication, and will give a presentation about this topic. The topics will include basic design techniques for approximation algorithms as well as exponential and parameterized algorithms, and some modern approaches of combining these techniques. We will focus on techniques for which certain worst-case performance guarantees can be proven. | ||||

Literature | The literature will consist of textbook chapters and original research papers and will be provided during the kick-off meeting. | ||||

Prerequisites / Notice | The participants should be familiar with the content of the lectures "Algorithmen und Datenstrukturen" (252-0026-00) and "Theoretische Informatik" (252-0057-00). The presentations will be given in the form of a block course in the first week of June 2020. The language can be mixed in German and English in the following sense: The teaching material will be in English, but it will be possible for at least half of the participants to give their presentations and hand in their written summaries in German. | ||||

272-0302-00L | Approximation and Online Algorithms | 5 credits | 2V + 1U + 1A | H.‑J. Böckenhauer, D. Komm | |

Abstract | This lecture deals with approximative algorithms for hard optimization problems and algorithmic approaches for solving online problems as well as the limits of these approaches. | ||||

Objective | Get a systematic overview of different methods for designing approximative algorithms for hard optimization problems and online problems. Get to know methods for showing the limitations of these approaches. | ||||

Content | Approximation algorithms are one of the most succesful techniques to attack hard optimization problems. Here, we study the so-called approximation ratio, i.e., the ratio of the cost of the computed approximating solution and an optimal one (which is not computable efficiently). For an online problem, the whole instance is not known in advance, but it arrives pieceweise and for every such piece a corresponding part of the definite output must be given. The quality of an algorithm for such an online problem is measured by the competitive ratio, i.e., the ratio of the cost of the computed solution and the cost of an optimal solution that could be given if the whole input was known in advance. The contents of this lecture are - the classification of optimization problems by the reachable approximation ratio, - systematic methods to design approximation algorithms (e.g., greedy strategies, dynamic programming, linear programming relaxation), - methods to show non-approximability, - classic online problem like paging or scheduling problems and corresponding algorithms, - randomized online algorithms, - the design and analysis principles for online algorithms, and - limits of the competitive ratio and the advice complexity as a way to do a deeper analysis of the complexity of online problems. | ||||

Literature | The lecture is based on the following books: J. Hromkovic: Algorithmics for Hard Problems, Springer, 2004 D. Komm: An Introduction to Online Computation: Determinism, Randomization, Advice, Springer, 2016 Additional literature: A. Borodin, R. El-Yaniv: Online Computation and Competitive Analysis, Cambridge University Press, 1998 |