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Probabilistic Analysis of Online Bin Coloring Algorithms Via Stochastic Comparison

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Algorithms - ESA 2008 (ESA 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5193))

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Abstract

This paper proposes a new method for probabilistic analysis of online algorithms. It is based on the notion of stochastic dominance. We develop the method for the online bin coloring problem introduced in [15]. Using methods for the stochastic comparison of Markov chains we establish the result that the performance of the online algorithm \(\textsc{GreedyFit}\) is stochastically better than the performance of the algorithm \(\textsc{OneBin}\) for any number of items processed. This result gives a more realistic picture than competitive analysis and explains the behavior observed in simulations.

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Author information

Authors and Affiliations

  1. Zuse Institute Berlin, Takustraße 7, D–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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Dan Halperin Kurt Mehlhorn

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Hiller, B., Vredeveld, T. (2008). Probabilistic Analysis of Online Bin Coloring Algorithms Via Stochastic Comparison. In: Halperin, D., Mehlhorn, K. (eds) Algorithms - ESA 2008. ESA 2008. Lecture Notes in Computer Science, vol 5193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87744-8_44

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  • DOI: https://doi.org/10.1007/978-3-540-87744-8_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87743-1

  • Online ISBN: 978-3-540-87744-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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