$(1-\epsilon)$-Approximate Maximum Weighted Matching in Distributed, Parallel, and Semi-Streaming Settings

TL;DR

This paper presents the first deterministic poly(1/ε, log n)-round distributed algorithm for computing a (1-ε)-approximate maximum weighted matching in general graphs within the CONGEST model, resolving a long-standing open problem.

Contribution

It introduces a deterministic distributed algorithm for (1-ε)-approximate MWM in general graphs with poly(1/ε, log n) rounds, extending previous work and improving efficiency.

Findings

Achieves a poly(1/ε, log n)-round deterministic algorithm in the CONGEST model.

Implements a semi-streaming algorithm with poly(1/ε) passes.

Provides a CREW PRAM algorithm with poly(1/ε, log n) span.

Abstract

The maximum weighted matching (MWM) problem is one of the most well-studied combinatorial optimization problems in distributed graph algorithms. Despite a long development on the problem, and the recent progress of Fischer, Mitrovic, and Uitto [FMU22] who gave a $\text{poly}(1/\epsilon, \log n)$-round algorithm for obtaining a $(1-\epsilon)$-approximate solution for unweighted maximum matching, it had been an open problem whether a $(1-\epsilon)$-approximate MWM can be obtained in $\text{poly}(1/\epsilon, \log n)$ rounds in the CONGEST model. Algorithms with such running times were only known for special graph classes such as bipartite graphs [AKO18] and minor-free graphs [CS22]. For general graphs, the previously known algorithms require exponential in $(1/\epsilon)$ rounds for obtaining a $(1-\epsilon)$-approximate solution [FFK21] or achieve an approximation factor of at most 2/3 [AKO18]. In this work, we settle this open problem by giving a deterministic $\text{poly}(1/\epsilon, \log n)$-round algorithm for computing a $(1-\epsilon)$-approximate MWM for general graphs in the CONGEST model. Our proposed solution extends the algorithm of Fischer, Mitrovic, and Uitto [FMU22], blends in the sequential algorithm from Duan and Pettie [DP14] and the work of Faour, Fuchs, and Kuhn [FFK21]. Interestingly, this solution also implies a CREW PRAM algorithm with $\text{poly}(1/\epsilon, \log n)$ span using only $O(m)$ processors. In addition, with the reduction from Gupta and Peng [GP13], we further obtain a semi-streaming algorithm with $\text{poly}(1/\epsilon)$ passes. When $\epsilon$ is smaller than a constant $o(1)$ but at least $1/\log^{o(1)} n$, our algorithm is more efficient than both Ahn and Guha's $\text{poly}(1/\epsilon, \log n)$-passes algorithm [AG13] and Gamlath, Kale, Mitrovic, and Svensson's $(1/\epsilon)^{O(1/\epsilon^2)}$-passes algorithm [GKMS19].