A General Framework for Matching with Delays

Abstract

We present a simple deterministic $O(\log n)$-competitive algorithm for the online minimum-cost perfect matching with delays (MPMD) problem on arbitrary, unknown metric spaces, where $n$ is the (unknown) number of requests. This improves upon the recent $O({\log }^{5}n)$-competitive algorithm of Dufay and Wattenhofer (SODA 2026) and nearly matches the $\Omega (\log n/\log \log n)$ randomized lower bound of Ashlagi et al. (APPROX 2017). Our algorithmic framework rests on two ingredients: an online moat-growing process that grows active dual variables at a deliberately slowed constant rate $\alpha <1$ to control temporal asymmetry, and a delayed eligibility rule that matches requests in the same dual component only after their waiting time is proportional to their time-expanded distance. The framework extends naturally to arbitrary concave delay functions, maintaining an $O(\log n)$ competitive ratio, and to $k$-way matching with delays, yielding an $O(k\log n)$-competitive deterministic algorithm.