Sublinear Algorithms for $(1.5+\epsilon)$-Approximate Matching

TL;DR

This paper introduces sublinear time algorithms achieving a $(1.5+ ext{epsilon})$-approximate maximum matching, significantly improving previous approximation factors in sublinear regimes by leveraging a novel approach based on edge-degree constrained subgraphs.

Contribution

The paper presents the first sublinear algorithms for $(1.5+ ext{epsilon})$-approximate maximum matching, diverging from prior methods by utilizing EDCS in the sublinear setting.

Findings

Achieves $(1.5+ ext{epsilon})$-approximation in $n^{2- ext{Theta}( ext{epsilon}^2)}$ time.

Introduces a simple, novel approach based on sublinear algorithms for EDCS.

Independent work confirms similar results and extends bounds.

Abstract

We study sublinear time algorithms for estimating the size of maximum matching. After a long line of research, the problem was finally settled by Behnezhad [FOCS'22], in the regime where one is willing to pay an approximation factor of $2$. Very recently, Behnezhad et al.[SODA'23] improved the approximation factor to $(2-\frac{1}{2^{O(1/\gamma)}})$ using $n^{1+\gamma}$ time. This improvement over the factor $2$ is, however, minuscule and they asked if even $1.99$-approximation is possible in $n^{2-\Omega(1)}$ time. We give a strong affirmative answer to this open problem by showing $(1.5+\epsilon)$-approximation algorithms that run in $n^{2-\Theta(\epsilon^{2})}$ time. Our approach is conceptually simple and diverges from all previous sublinear-time matching algorithms: we show a sublinear time algorithm for computing a variant of the edge-degree constrained subgraph (EDCS), a concept that has previously been exploited in dynamic [Bernstein Stein ICALP'15, SODA'16], distributed [Assadi et al. SODA'19] and streaming [Bernstein ICALP'20] settings, but never before in the sublinear setting. Independent work: Behnezhad, Roghani and Rubinstein [BRR'23] independently showed sublinear algorithms similar to our Theorem 1.2 in both adjacency list and matrix models. Furthermore, in [BRR'23], they show additional results on strictly better-than-1.5 approximate matching algorithms in both upper and lower bound sides.