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Stochastic Matching with Commitment

  • Kevin P. Costello
  • Prasad Tetali
  • Pushkar Tripathi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7391)

Abstract

We consider the following stochastic optimization problem first introduced by Chen et al. in [7]. We are given a vertex set of a random graph where each possible edge is present with probability p e . We do not know which edges are actually present unless we scan/probe an edge. However whenever we probe an edge and find it to be present, we are constrained to picking the edge and both its end points are deleted from the graph. We wish to find the maximum matching in this model. We compare our results against the optimal omniscient algorithm that knows the edges of the graph and present a 0.573 factor algorithm using a novel sampling technique. We also prove that no algorithm can attain a factor better than 0.898 in this model.

Keywords

Bipartite Graph Random Graph Competitive Ratio Maximum Match Full Version 
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-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kevin P. Costello
    • 2
  • Prasad Tetali
    • 1
  • Pushkar Tripathi
    • 1
  1. 1.Georgia Institute of TechnologyUSA
  2. 2.University of California at RiversideUSA

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