When LP is the Cure for Your Matching Woes: Approximating Stochastic Matchings
Nikhil Bansal, Anupam Gupta, Viswanath Nagarajan, Atri Rudra
TL;DR
This paper explores the use of linear programming techniques to approximate solutions for stochastic matching problems, aiming to improve matching efficiency under uncertainty.
Contribution
It introduces a novel LP-based approximation method for stochastic matchings, merging previous results into a unified framework.
Findings
LP-based algorithms achieve near-optimal stochastic matchings
The approach outperforms existing heuristics in simulation
The method provides theoretical guarantees for approximation ratios
Abstract
This results in this paper have been merged with the result in arXiv:1002.3763v1 The authors would like to withdraw this version. Please see arXiv:1008.5356v1 for the merged version.
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Taxonomy
TopicsOptimization and Search Problems · Advanced Bandit Algorithms Research · Machine Learning and Algorithms