Improved Competitive Ratio for Edge-Weighted Online Stochastic Matching
Abstract
We consider the edge-weighted online stochastic matching problem, in which an edge-weighted bipartite graph G=(I\cup J, E) with offline vertices J and online vertex types I is given. The online vertices have types sampled from I with probability proportional to the arrival rates of online vertex types. The online algorithm must make immediate and irrevocable matching decisions with the objective of maximizing the total weight of the matching. For the problem with general arrival rates, Feldman et al. (FOCS 2009) proposed the Suggested Matching algorithm and showed that it achieves a competitive ratio of 1-1/e \approx 0.632. The ratio has recently been improved to 0.645 by Yan (2022), who proposed the Multistage Suggested Matching (MSM) algorithm. In this paper, we propose the Evolving Suggested Matching (ESM) algorithm, and show that it achieves a competitive ratio of 0.650.
Cite
@article{arxiv.2302.05633,
title = {Improved Competitive Ratio for Edge-Weighted Online Stochastic Matching},
author = {Yilong Feng and Guoliang Qiu and Xiaowei Wu and Shengwei Zhou},
journal= {arXiv preprint arXiv:2302.05633},
year = {2023}
}
Comments
To appear in WINE2023