When Stochastic Rewards Reduce to Deterministic Rewards in Online Bipartite Matching

TL;DR

This paper demonstrates that under certain conditions, stochastic rewards in online bipartite matching can be effectively analyzed using deterministic models, extending known guarantees of the Perturbed Greedy algorithm.

Contribution

It provides a simple reduction from stochastic to deterministic rewards, broadening the applicability of existing algorithms and revealing new competitive ratio guarantees.

Findings

Perturbed Greedy achieves (1-1/e) ratio in some stochastic settings

Reduction simplifies analysis of stochastic rewards to deterministic case

Shows limitations of Perturbed Greedy without assumptions

Abstract

We study the problem of vertex-weighted online bipartite matching with stochastic rewards where matches may fail with some known probability and the decision maker has to adapt to the sequential realization of these outcomes. Recent works have studied several special cases of this problem and it was known that the (randomized) Perturbed Greedy algorithm due to Aggarwal et al. (SODA, 2011) achieves the best possible competitive ratio guarantee of $(1-1/e)$ in some cases. We give a simple proof of these results by reducing (special cases of) the stochastic rewards problem to the deterministic setting of online bipartite matching (Karp, Vazirani, Vazirani (STOC, 1990)). More broadly, our approach gives conditions under which it suffices to analyze the competitive ratio of an algorithm for the simpler setting of deterministic rewards in order to obtain a competitive ratio guarantee for stochastic rewards. The simplicity of our approach reveals that the Perturbed Greedy algorithm has a competitive ratio of $(1-1/e)$ even in certain settings with correlated rewards, where no results were previously known. Finally, we show that without any special assumptions, the Perturbed Greedy algorithm has a competitive ratio strictly less than $(1-1/e)$ for vertex-weighted online matching with stochastic rewards.