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Stochastic dominance analysis of Online Bin Coloring algorithms

Please always quote using this URN: urn:nbn:de:0297-zib-16502
  • 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 by Krumke et al (2008). Using methods for the stochastic comparison of Markov chains we establish the result that the performance of the online algorithm GreedyFit is stochastically better than the performance of the algorithm 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:Benjamin HillerORCiD, Tjark Vredeveld
Document Type:ZIB-Report
Tag:online algorithms, stochastic dominance, algorithm analysis, Markov chains
MSC-Classification:60-XX PROBABILITY THEORY AND STOCHASTIC PROCESSES (For additional applications, see 11Kxx, 62-XX, 90-XX, 91-XX, 92-XX, 93-XX, 94-XX)
68-XX COMPUTER SCIENCE (For papers involving machine computations and programs in a specific mathematical area, see Section -04 in that area)
Date of first Publication:2012/11/15
Series (Serial Number):ZIB-Report (12-42)
Licence (German):License LogoCreative Commons - Namensnennung-Nicht kommerziell-Keine Bearbeitung
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