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Probabilistic alternatives for competitive analysis

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Computer Science - Research and Development

Abstract

In the last 20 years competitive analysis has become the main tool for analyzing the quality of online algorithms. Despite of this, competitive analysis has also been criticized: It sometimes cannot discriminate between algorithms that exhibit significantly different empirical behavior, or it even favors an algorithm that is worse from an empirical point of view. Therefore, there have been several approaches to circumvent these drawbacks. In this survey, we discuss probabilistic alternatives for competitive analysis.

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Authors and Affiliations

  1. Zuse Institute Berlin, Takustraße 7, 14195, Berlin, Germany

    Benjamin Hiller

  2. Department of Quantitative Economics, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands

    Tjark Vredeveld

Authors
  1. Benjamin Hiller
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  2. Tjark Vredeveld
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Correspondence to Benjamin Hiller.

Additional information

Work of B. Hiller partially supported by the DFG research group “Algorithms, Structure, Randomness” (Grant number GR 883/10-3, GR 883/10-4) and the DFG research center Matheon Berlin.

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Hiller, B., Vredeveld, T. Probabilistic alternatives for competitive analysis. Comput Sci Res Dev 27, 189–196 (2012). https://doi.org/10.1007/s00450-011-0149-1

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