Improved Bounds for Fractional Online Matching Problems

TL;DR

This paper advances the theoretical understanding of fractional online matching by improving bounds and proposing new algorithms for models with general vertex arrival and fully online matching, relevant to dynamic markets like ride-sharing.

Contribution

It introduces improved bounds and a more intuitive algorithm for fractional online matching in generalized models, enhancing previous theoretical results.

Findings

Designed a 0.6-competitive algorithm for fully online matching.

Proved no algorithm can be 0.613-competitive for fully online matching.

Established a 0.526-competitive algorithm for general vertex arrival model.

Abstract

Online bipartite matching with one-sided arrival and its variants have been extensively studied since the seminal work of Karp, Vazirani, and Vazirani (STOC 1990). Motivated by real-life applications with dynamic market structures, e.g. ride-sharing, two generalizations of the classical one-sided arrival model are proposed to allow non-bipartite graphs and to allow all vertices to arrive online. Namely, online matching with general vertex arrival is introduced by Wang and Wong (ICALP 2015), and fully online matching is introduced by Huang et al. (JACM 2020). In this paper, we study the fractional versions of the two models. We improve three out of the four state-of-the-art upper and lower bounds of the two models. For fully online matching, we design a $0.6$-competitive algorithm and prove no algorithm can be $0.613$-competitive. For online matching with general vertex arrival, we prove no algorithm can be $0.584$-competitive. Moreover, we give an arguably more intuitive algorithm for the general vertex arrival model, compared to the algorithm of Wang and Wong, while attaining the same competitive ratio of $0.526$.