An optimal algorithm for on-line bipartite matching
STOC '90 Proceedings of the twenty-second annual ACM symposium on Theory of computing
SIAM Journal on Computing
The Power of Two Choices in Randomized Load Balancing
IEEE Transactions on Parallel and Distributed Systems
Rollout Algorithms for Stochastic Scheduling Problems
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On Constraint Sampling in the Linear Programming Approach to Approximate Dynamic Programming
Mathematics of Operations Research
AdWords and Generalized On-line Matching
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Online budgeted matching in random input models with applications to Adwords
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Dynamic Programming and Optimal Control, Vol. II
Dynamic Programming and Optimal Control, Vol. II
The adwords problem: online keyword matching with budgeted bidders under random permutations
Proceedings of the 10th ACM conference on Electronic commerce
Online Ad Assignment with Free Disposal
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Online Stochastic Matching: Beating 1-1/e
FOCS '09 Proceedings of the 2009 50th Annual IEEE Symposium on Foundations of Computer Science
Online primal-dual algorithms for maximizing ad-auctions revenue
ESA'07 Proceedings of the 15th annual European conference on Algorithms
Optimal online assignment with forecasts
Proceedings of the 11th ACM conference on Electronic commerce
Improved bounds for online stochastic matching
ESA'10 Proceedings of the 18th annual European conference on Algorithms: Part I
Online stochastic packing applied to display ad allocation
ESA'10 Proceedings of the 18th annual European conference on Algorithms: Part I
Near optimal online algorithms and fast approximation algorithms for resource allocation problems
Proceedings of the 12th ACM conference on Electronic commerce
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
Online vertex-weighted bipartite matching and single-bid budgeted allocations
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
Approximation algorithms for dynamic resource allocation
Operations Research Letters
Simultaneous approximations for adversarial and stochastic online budgeted allocation
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
Online prophet-inequality matching with applications to ad allocation
Proceedings of the 13th ACM Conference on Electronic Commerce
Asymptotically optimal algorithm for stochastic adwords
Proceedings of the 13th ACM Conference on Electronic Commerce
Budget smoothing for internet ad auctions: a game theoretic approach
Proceedings of the fourteenth ACM conference on Electronic commerce
Whole-page optimization and submodular welfare maximization with online bidders
Proceedings of the fourteenth ACM conference on Electronic commerce
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Motivated by the display ad allocation problem on the Internet, we study the online stochastic weighted matching problem. In this problem, given an edge-weighted bipartite graph, nodes of one side arrive online i.i.d. according to a known probability distribution. Recently, a sequence of results by Feldman et. al [14] and Manshadi et. al [20] result in a 0.702-approximation algorithm for the unweighted version of this problem, aka online stochastic matching, breaking the 1−1 / e barrier. Those results, however, do no hold for the more general online stochastic weighted matching problem. Moreover, all of these results employ the idea of power of two choices. In this paper, we present the first approximation (0.667-competitive) algorithm for the online stochastic weighted matching problem beating the 1−1 / e barrier. Moreover, we improve the approximation factor of the online stochastic matching by analyzing the more general framework of power of multiple choices. In particular, by computing a careful third pseudo-matching along with the two offline solutions, and using it in the online algorithm, we improve the approximation factor of the online stochastic matching for any bipartite graph to 0.7036.