Hans-Joachim Böckenhauer: Catalogue data in Spring Semester 2019
|Name||Dr. Hans-Joachim Böckenhauer|
|Consultation hours||By appointment|
Inform.technologie und Ausbildung
ETH Zürich, CAB F 11.1
|Telephone||+41 44 632 81 83|
|Fax||+41 44 632 13 90|
|272-0300-00L||Algorithmics for Hard Problems |
This course d o e s n o t include the Mentored Work Specialised Courses with an Educational Focus in Computer Science A.
|4 credits||2V + 1U||H.‑J. Böckenhauer, R. Kralovic|
|Abstract||This course unit looks into algorithmic approaches to the solving of hard problems, particularly with moderately exponential-time algorithms and parameterized algorithms.|
The seminar is accompanied by a comprehensive reflection upon the significance of the approaches presented for computer science tuition at high schools.
|Objective||To systematically acquire an overview of the methods for solving hard problems. To get deeper knowledge of exact and parameterized algorithms.|
|Content||First, the concept of hardness of computation is introduced (repeated for the computer science students). Then some methods for solving hard problems are treated in a systematic way. For each algorithm design method, it is discussed what guarantees it can give and how we pay for the improved efficiency. A special focus lies on moderately exponential-time algorithms and parameterized algorithms.|
|Lecture notes||Unterlagen und Folien werden zur Verfügung gestellt.|
|Literature||J. Hromkovic: Algorithmics for Hard Problems, Springer 2004.|
R. Niedermeier: Invitation to Fixed-Parameter Algorithms, 2006.
M. Cygan et al.: Parameterized Algorithms, 2015.
F. Fomin, D. Kratsch: Exact Exponential Algorithms, 2010.
|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
A. Borodin, R. El-Yaniv: Online Computation and Competitive Analysis, Cambridge University Press, 1998