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Almost optimal solutions for bin coloring problems

Author

Listed:
  • Mingen Lin

    (University at Buffalo, The State University of New York)

  • Zhiyong Lin

    (University at Buffalo, The State University of New York)

  • Jinhui Xu

    (University at Buffalo, The State University of New York)

Abstract

In this paper we study two interesting bin coloring problems: Minimum Bin Coloring Problem (MinBC) and Online Maximum Bin Coloring Problem (OMaxBC), motivated from several applications in networking. For the MinBC problem, we present two near linear time approximation algorithms to achieve almost optimal solutions, i.e., no more than OPT+2 and OPT+1 respectively, where OPT is the optimal solution. For the OMaxBC problem, we first introduce a deterministic 2-competitive greedy algorithm, and then give lower bounds for any deterministic and randomized (against adaptive offline adversary) online algorithms. The lower bounds show that our deterministic algorithm achieves the best possible competitive ratio.

Suggested Citation

  • Mingen Lin & Zhiyong Lin & Jinhui Xu, 2008. "Almost optimal solutions for bin coloring problems," Journal of Combinatorial Optimization, Springer, vol. 16(1), pages 16-27, July.
    • Handle: RePEc:spr:jcomop:v:16:y:2008:i:1:d:10.1007_s10878-007-9094-0
    DOI: 10.1007/s10878-007-9094-0
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    Cited by:

    1. Leah Epstein & Sven O. Krumke & Asaf Levin & Heike Sperber, 2011. "Selfish bin coloring," Journal of Combinatorial Optimization, Springer, vol. 22(4), pages 531-548, November.

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