Improved Approximation Algorithms for Stochastic-Matching Problems

TL;DR

This paper advances approximation algorithms for the stochastic matching problem, significantly improving the best-known ratios for bipartite and general graphs by innovating edge probing strategies.

Contribution

It introduces a novel edge probing method using non-uniform random permutations, enhancing approximation ratios for stochastic matching.

Findings

Achieved 0.39-approximation for bipartite graphs.

Achieved 0.269-approximation for general graphs.

Developed a new edge probing technique with non-uniform permutations.

Abstract

We consider the Stochastic Matching problem, which is motivated by applications in kidney exchange and online dating. In this problem, we are given an undirected graph. Each edge is assigned a known, independent probability of existence and a positive weight (or profit). We must probe an edge to discover whether or not it exists. Each node is assigned a positive integer called a timeout (or a patience). On this random graph we are executing a process, which probes the edges one-by-one and gradually constructs a matching. The process is constrained in two ways. First, if a probed edge exists, it must be added irrevocably to the matching (the query-commit model). Second, the timeout of a node $v$ upper-bounds the number of edges incident to $v$ that can be probed. The goal is to maximize the expected weight of the constructed matching. For this problem, Bansal et al. (Algorithmica 2012) provided a $0.33$-approximation algorithm for bipartite graphs and a $0.25$-approximation for general graphs. We improve the approximation factors to $0.39$ and $0.269$, respectively. The main technical ingredient in our result is a novel way of probing edges according to a not-uniformly-random permutation. Patching this method with an algorithm that works best for large-probability edges (plus additional ideas) leads to our improved approximation factors.