• Media type: Text; E-Article
  • Title: Analysis of Algorithms Beyond the Worst Case (Dagstuhl Seminar 14372)
  • Contributor: Balcan, Marina-Florina [Author]; Manthey, Bodo [Author]; Röglin, Heiko [Author]; Roughgarden, Tim [Author]
  • imprint: Schloss Dagstuhl – Leibniz-Zentrum für Informatik, 2015
  • Language: English
  • DOI: https://doi.org/10.4230/DagRep.4.9.30
  • Keywords: smoothed analysis ; machine learning ; analysis of algorithms ; approximation stability ; probabilistic analysis
  • Origination:
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  • Description: This report documents the program and the outcomes of Dagstuhl Seminar 14372 "Analysis of Algorithms Beyond the Worst Case". The theory of algorithms has traditionally focused on worst-case analysis. This focus has led to both a deep theory and many beautiful and useful algorithms. However, there are a number of important problems and algorithms for which worst-case analysis does not provide useful or empirically accurate results. This is due to the fact that worst-case inputs are often rather contrived and occur hardly ever in practical applications. Only in recent years a paradigm shift towards a more realistic and robust algorithmic theory has been initiated. The development of a more realistic theory hinges on finding models that measure the performance of an algorithm not only by its worst-case behavior but rather by its behavior on "typical" inputs. In this seminar, we discussed various recent theoretical models and results that go beyond worst-case analysis. The seminar helped to consolidate the research and to foster collaborations among the researchers working in the different branches of analysis of algorithms beyond the worst case.
  • Access State: Open Access