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Improved Bounds in Stochastic Matching and Optimization

Authors Alok Baveja, Amit Chavan, Andrei Nikiforov, Aravind Srinivasan, Pan Xu

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Alok Baveja
Amit Chavan
Andrei Nikiforov
Aravind Srinivasan
Pan Xu

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Alok Baveja, Amit Chavan, Andrei Nikiforov, Aravind Srinivasan, and Pan Xu. Improved Bounds in Stochastic Matching and Optimization. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2015). Leibniz International Proceedings in Informatics (LIPIcs), Volume 40, pp. 124-134, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)


We consider two fundamental problems in stochastic optimization: approximation algorithms for stochastic matching, and sampling bounds in the black-box model. For the former, we improve the current-best bound of 3.709 due to Adamczyk et al. (2015), to 3.224; we also present improvements on Bansal et al. (2012) for hypergraph matching and for relaxed versions of the problem. In the context of stochastic optimization, we improve upon the sampling bounds of Charikar et al. (2005).
  • stochastic matching
  • approximation algorithms
  • sampling complexity


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