Matching with Commitments

TL;DR

This paper studies a stochastic matching problem where edges are uncertain and must be probed, proposing a 0.573-approximation algorithm and establishing an upper bound of 0.898 on achievable performance.

Contribution

It introduces a novel sampling technique for stochastic matching with commitments and provides both an approximation algorithm and a performance upper bound.

Findings

Proposed a 0.573-approximation algorithm for the problem.

Established an upper bound of 0.898 on the approximation ratio.

Compared the algorithm's performance against the optimal omniscient solution.

Abstract

We consider the following stochastic optimization problem first introduced by Chen et al. in \cite{chen}. We are given a vertex set of a random graph where each possible edge is present with probability p_e. We do not know which edges are actually present unless we scan/probe an edge. However whenever we probe an edge and find it to be present, we are constrained to picking the edge and both its end points are deleted from the graph. We wish to find the maximum matching in this model. We compare our results against the optimal omniscient algorithm that knows the edges of the graph and present a 0.573 factor algorithm using a novel sampling technique. We also prove that no algorithm can attain a factor better than 0.898 in this model.