## Dennis Komm: Catalogue data in Spring Semester 2018 |

Name | Prof. Dr. Dennis Komm |

Field | Algorithms and Didactics |

Address | Professur Algorithmen und Didaktik ETH Zürich, CAB F 10.1 Universitätstrasse 6 8092 Zürich SWITZERLAND |

Telephone | +41 44 632 82 23 |

dennis.komm@inf.ethz.ch | |

URL | https://people.inf.ethz.ch/dkomm/ |

Department | Computer Science |

Relationship | Associate Professor |

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

272-0301-00L | Methods for Design of Random Systems This course d o e s n o t include the Mentored Work Specialised Courses with an Educational Focus in Computer Science B. | 4 credits | 2V + 1U | H.‑J. Böckenhauer, D. Komm, R. Kralovic | |

Abstract | The students should get a deep understanding of the notion of randomness and its usefulness. Using basic elements probability theory and number theory the students will discover randomness as a source of efficiency in algorithmic. The goal is to teach the paradigms of design of randomized algorithms. | ||||

Objective | To understand the computational power of randomness and to learn the basic methods for designing randomized algorithms | ||||

Lecture notes | J. Hromkovic: Randomisierte Algorithmen, Teubner 2004. J.Hromkovic: Design and Analysis of Randomized Algorithms. Springer 2006. J.Hromkovic: Algorithmics for Hard Problems, Springer 2004. | ||||

Literature | J. Hromkovic: Randomisierte Algorithmen, Teubner 2004. J.Hromkovic: Design and Analysis of Randomized Algorithms. Springer 2006. J.Hromkovic: Algorithmics for Hard Problems, Springer 2004. | ||||

272-0302-00L | Approximation and Online Algorithms | 4 credits | 2V + 1U | 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 |