DagRep.7.4.1.pdf
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Probabilistic Methods in the Design and Analysis of Algorithms (Dagstuhl Seminar 17141)
Authors and all authors of the abstracts in this report
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Dagstuhl Reports, Volume 7, Issue 4
Volume: Dagstuhl Reports, Volume 7
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 3.0 Unported license
- Publication Date: 2017-12-19
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Bodo Manthey, Claire Mathieu, Heiko Röglin, and Eli Upfal. Probabilistic Methods in the Design and Analysis of Algorithms (Dagstuhl Seminar 17141). In Dagstuhl Reports, Volume 7, Issue 4, pp. 1-22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)
https://doi.org/10.4230/DagRep.7.4.1
Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 17141 "Probabilistic Methods in the Design and Analysis of Algorithms". Probabilistic methods play a central role in theoretical computer science. They are a powerful and widely applied tool used, for example, for designing efficient randomized algorithms and for establishing various lower bounds in complexity theory. They also form the basis of frameworks like average-case and smoothed analysis, in which algorithms are analyzed beyond the classical worst-case perspective. The seminar was on probabilistic methods with a focus on the design and analysis of algorithms. The seminar helped to consolidate the research and to foster collaborations among the researchers who use probabilistic methods in different areas of the design and analysis of algorithms.
Subject Classification
Keywords
- analysis of algorithms
- average-case analysis
- random graphs
- randomized algorithms
- smoothed analysis
- sub-linear algorithms
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