An optimal algorithm for on-line bipartite matching
STOC '90 Proceedings of the twenty-second annual ACM symposium on Theory of computing
Theoretical Computer Science
Maximum matchings in sparse random graphs: Karp-Sipser revisited
Random Structures & Algorithms
Perfect matchings in random graphs with prescribed minimal degree
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
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SFCS '81 Proceedings of the 22nd Annual Symposium on Foundations of Computer Science
Approximating Matches Made in Heaven
ICALP '09 Proceedings of the 36th International Colloquium on Automata, Languages and Programming: Part I
Finding a maximum matching in a sparse random graph in O(n) expected time
Journal of the ACM (JACM)
Online Stochastic Matching: Beating 1-1/e
FOCS '09 Proceedings of the 2009 50th Annual IEEE Symposium on Foundations of Computer Science
When LP is the cure for your matching woes: improved bounds for stochastic matchings
ESA'10 Proceedings of the 18th annual European conference on Algorithms: Part II
Randomized greedy matching. II
Random Structures & Algorithms
Online bipartite matching with unknown distributions
Proceedings of the forty-third annual ACM symposium on Theory of computing
Online bipartite matching with random arrivals: an approach based on strongly factor-revealing LPs
Proceedings of the forty-third annual ACM symposium on Theory of computing
Improved analysis of the greedy algorithm for stochastic matching
Information Processing Letters
Online stochastic matching: online actions based on offline statistics
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Harnessing the power of two crossmatches
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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 pe. 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.