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A \(o(n)\)-Competitive Deterministic Algorithm for Online Matching on a Line

  • Antonios Antoniadis
  • Neal Barcelo
  • Michael Nugent
  • Kirk Pruhs
  • Michele Scquizzato
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8952)

Abstract

Online matching on a line involves matching an online stream of items of various sizes to stored items of various sizes, with the objective of minimizing the average discrepancy in size between matched items. The best previously known upper and lower bounds on the optimal deterministic competitive ratio are linear in the number of items, and constant, respectively. We show that online matching on a line is essentially equivalent to a particular search problem, that we call \(k\) -lost cows. We then obtain the first deterministic sub-linearly competitive algorithm for online matching on a line by giving such an algorithm for the \(k\)-lost cows problem.

Keywords

Competitive Ratio Online Algorithm Search Problem Deterministic Algorithm Optimal Match 
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 2015

Authors and Affiliations

  • Antonios Antoniadis
    • 2
  • Neal Barcelo
    • 1
  • Michael Nugent
    • 1
  • Kirk Pruhs
    • 1
  • Michele Scquizzato
    • 3
  1. 1.Department of Computer ScienceUniversity of PittsburghPittsburghUSA
  2. 2.Max-Planck Institut für InformatikSaarbrückenGermany
  3. 3.Department of Computer ScienceUniversity of HoustonHoustonUSA

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