Online Graph Matching Problems with a Worst-Case Reassignment Budget

TL;DR

This paper investigates online bipartite matching with a fixed reassignment budget, demonstrating that a simple algorithm is optimal under various models and improving competitive ratios for weighted problems without the triangle inequality.

Contribution

It introduces a non-amortized reassignment budget model, proving the optimality of a simple deterministic algorithm and improving competitive ratios for weighted matching.

Findings

A simple algorithm is optimal for all models considered.

Achieves a (1 - 2/(k+2))-competitive ratio for unweighted maximum matching.

Provides a 1/2-competitive algorithm for weighted matching with no triangle inequality, surpassing previous results.

Abstract

In the online bipartite matching with reassignments problem, an algorithm is initially given only one side of the vertex set of a bipartite graph; the vertices on the other side are revealed to the algorithm one by one, along with its incident edges. The algorithm is required to maintain a matching in the current graph, where the algorithm revises the matching after each vertex arrival by reassigning vertices. Bernstein, Holm, and Rotenberg showed that an online algorithm can maintain a matching of maximum cardinality by performing amortized $O(\log^2 n)$ reassignments per arrival. In this paper, we propose to consider the general question of how requiring a non-amortized hard budget $k$ on the number of reassignments affects the algorithms' performances, under various models from the literature. We show that a simple, widely-used algorithm is a best-possible deterministic algorithm for all these models. For the unweighted maximum-cardinality problem, the algorithm is a $(1-\frac{2}{k+2})$-competitive algorithm, which is the best possible for a deterministic algorithm both under vertex arrivals and edge arrivals. Applied to the load balancing problem, this yields a bifactor online algorithm. For the weighted problem, which is traditionally studied assuming the triangle inequality, we show that the power of reassignment allows us to lift this assumption and the algorithm becomes a $\frac{1}{2}$-competitive algorithm for $k=4$, improving upon the $\frac{1}{3}$ of the previous algorithm without reassignments. We show that this also is a best-possible deterministic algorithm.