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Online Bounded Analysis

  • Joan Boyar
  • Leah Epstein
  • Lene M. Favrholdt
  • Kim S. Larsen
  • Asaf Levin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9691)

Abstract

Though competitive analysis is often a very good tool for the analysis of online algorithms, sometimes it does not give any insight and sometimes it gives counter-intuitive results. Much work has gone into exploring other performance measures, in particular targeted at what seems to be the core problem with competitive analysis: the comparison of the performance of an online algorithm is made to a too powerful adversary. We consider a new approach to restricting the power of the adversary, by requiring that when judging a given online algorithm, the optimal offline algorithm must perform as well as the online algorithm, not just on the entire final request sequence, but also on any prefix of that sequence. This is limiting the adversary’s usual advantage of being able to exploit that it knows the sequence is continuing beyond the current request. Through a collection of online problems, including machine scheduling, bin packing, dual bin packing, and seat reservation, we investigate the significance of this particular offline advantage.

Keywords

Greedy Algorithm Competitive Ratio Online Algorithm Identical Machine Competitive Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Joan Boyar
    • 1
  • Leah Epstein
    • 2
  • Lene M. Favrholdt
    • 1
  • Kim S. Larsen
    • 1
  • Asaf Levin
    • 3
  1. 1.Department of Mathematics and Computer ScienceUniversity of Southern DenmarkOdenseDenmark
  2. 2.Department of MathematicsUniversity of HaifaHaifaIsrael
  3. 3.Faculty of IE&MThe TechnionHaifaIsrael

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