Online Matching with Stochastic Rewards: Optimal Competitive Ratio via Path-Based Formulation

Vineet Goyal () and Rajan Udwani ()
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Vineet Goyal: Industrial Engineering and Operations Research (IEOR), Columbia University, New York, New York 10027
Rajan Udwani: Industrial Engineering and Operations Research (IEOR), University of California, Berkeley, Berkeley, California 94720

Operations Research, 2023, vol. 71, issue 2, 563-580

Abstract: The problem of online matching with stochastic rewards is a generalization of the online bipartite matching problem where each edge has a probability of success. When a match is made it succeeds with the probability of the corresponding edge. We consider the more general vertex-weighted version of the problem and give two new results. First, we show that a natural generalization of the perturbed-greedy algorithm is ( 1 − 1 / e ) competitive when probabilities decompose as a product of two factors, one corresponding to each vertex of the edge. This is the best achievable guarantee as it includes the case of identical probabilities and, in particular, the classical online bipartite matching problem. Second, we give a deterministic 0.596 competitive algorithm for the previously well-studied case of fully heterogeneous but vanishingly small edge probabilities. A key contribution of our approach is the use of novel path-based formulations and a generalization of the primal-dual scheme. These allow us to compare against the natural benchmarks of adaptive offline algorithms that know the sequence of arrivals and the edge probabilities in advance but not the outcomes of potential matches. These ideas may be of independent interest in other online settings with postallocation stochasticity.

Keywords: Revenue Management and Market Analytics; online matching; stochastic rewards; competitive ratio; path-based analysis; primal-dual (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:71:y:2023:i:2:p:563-580

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