Beating Greedy for Stochastic Bipartite Matching

TL;DR

This paper develops a new algorithm that achieves a near-optimal approximation for stochastic bipartite matching in models where edges are probabilistic and can be queried, improving over previous greedy approaches.

Contribution

It introduces a $(1-1/e)$-approximation algorithm for weighted stochastic bipartite matching, surpassing the previous greedy method, using a novel LP-based approach.

Findings

Achieves $(1-1/e)$-approximation in weighted query-commit model.

Extends techniques to price-of-information model.

Provides structural LP properties for analysis.

Abstract

We consider the maximum bipartite matching problem in stochastic settings, namely the query-commit and price-of-information models. In the query-commit model, an edge e independently exists with probability $p_e$. We can query whether an edge exists or not, but if it does exist, then we have to take it into our solution. In the unweighted case, one can query edges in the order given by the classical online algorithm of Karp, Vazirani, and Vazirani to get a $(1-1/e)$-approximation. In contrast, the previously best known algorithm in the weighted case is the $(1/2)$-approximation achieved by the greedy algorithm that sorts the edges according to their weights and queries in that order. Improving upon the basic greedy, we give a $(1-1/e)$-approximation algorithm in the weighted query-commit model. We use a linear program (LP) to upper bound the optimum achieved by any strategy. The proposed LP admits several structural properties that play a crucial role in the design and analysis of our algorithm. We also extend these techniques to get a $(1-1/e)$-approximation algorithm for maximum bipartite matching in the price-of-information model introduced by Singla, who also used the basic greedy algorithm to give a $(1/2)$-approximation.