Improved bounds in stochastic matching and optimization

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

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, Grandoni, and Mukherjee [1], to 3.224; we also present improvements on Bansal, Gupta, Li, Mestre, Nagarajan, and Rudra [2] for hypergraph matching and for relaxed versions of the problem. In the context of stochastic optimization, we improve upon the sampling bounds of Charikar, Chekuri, and Pál [3].

Original languageEnglish (US)
Title of host publicationApproximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques - 18th International Workshop, APPROX 2015, and 19th International Workshop, RANDOM 2015
EditorsNaveen Garg, Klaus Jansen, Anup Rao, Jose D. P. Rolim
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
Pages124-134
Number of pages11
ISBN (Electronic)9783939897897
DOIs
StatePublished - Aug 1 2015
Externally publishedYes
Event18th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2015, and 19th International Workshop on Randomization and Computation, RANDOM 2015 - Princeton, United States
Duration: Aug 24 2015Aug 26 2015

Publication series

NameLeibniz International Proceedings in Informatics, LIPIcs
Volume40
ISSN (Print)1868-8969

Conference

Conference18th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2015, and 19th International Workshop on Randomization and Computation, RANDOM 2015
Country/TerritoryUnited States
CityPrinceton
Period8/24/158/26/15

All Science Journal Classification (ASJC) codes

  • Software

Keywords

  • Approximation algorithms
  • Sampling complexity
  • Stochastic matching

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