Annual Report 2010 - Fachgruppe Informatik an der RWTH Aachen ...

Overview
Many technological innovations and achievements of the recent decades rely on algorithmic
ideas facilitating new applications in science, medicine, production, logistics, traffic, and
communication. Efficient algorithms do not only enable personal computers to execute the
newest generation of games with features unthinkable only a few years ago, but they were
also the key to several recent scientific breakthroughs. The algorithms and complexity group
works on the design and analysis of algorithms, especially
• randomized algorithms
• approximation and online algorithms
• algorithms for graphs and interconnection networks
• probabilistic analysis of algorithms
• algorithmic game theory
During the last year, we successfully continued our work in algorithmic game theory and
network algorithms. A special focus of this year's work was lying on scheduling and power
assignment in wireless networks, focussing on realistic networks models defined by SINR
constraints. We obtained several results on the approximation ratio achievable by different
power assignments leading to new measures of efficiency and distributed algorithms. We
were able to place results about SINR scheduling on major theoretical computer science and
distributed computing conferences (e.g. SPAA, SODA, DISC). Together with other
researchers working in this field, we organized two workshops on network algorithms and
related areas:
• SWAT satellite workshop on Realistic Models for Algorithms in Wireless Networks
(WRAWN), June 19 to 20, Bergen, Norway. The workshop was organized by Magnús M.
Halldórsson (Reykjavik University) and Berthold Vöcking (RWTH Aachen University).
• Dagstuhl seminar on Flexible Network Design, May 24-28. The seminar was organized by
Anupam Gupta (CMU Pittsburgh), Stefano Leonardi (Università di Roma "La Sapienza"),
Berthold Vöcking (RWTH Aachen University), Roger Wattenhofer (ETH Zürich).
As a new research direction we studied learning algorithms for online buffering processes.
We investigated how well-known no-regret learning algorithms like the Randomized
Weighted Majority Algorithm can be adapted to processes in which the observed experts have
states with the consequence that switching between experts comes at some cost. Our first
article in this interesting research direction was accepted at COLT, the leading conference on
theoretical aspects of machine learning and empirical inference.
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