A 0.51-Approximation of Maximum Matching in Sublinear $n^{1.5}$ Time

We study the problem of estimating the size of a maximum matching in sublinear time. The problem has been studied extensively in the literature and various algorithms and lower bounds are known for it. Our result is a $0.5109$-approximation algorithm with a running time of $\tilde{O}(n\sqrt{n})$. All previous algorithms either provide only a marginal improvement (e.g., $2^{-280}$) over the $0.5$-approximation that arises from estimating a \emph{maximal} matching, or have a running time that is nearly $n^2$. Our approach is also arguably much simpler than other algorithms beating $0.5$-approximation.

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Publication Details

Journal
ArXiv.org
Published
2025-01-01
DOI
https://doi.org/10.4230/lipics.icalp.2025.116
Primary Topic
Matrix Theory and Algorithms
Type
article
Field-Weighted Citation Impact
0.00
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article

A 0.51-Approximation of Maximum Matching in Sublinear $n^{1.5}$ Time

Mohammad Roghani, Sepideh Mahabadi, Jakub Tarnawski
ArXiv.org
Matrix Theory and Algorithms
article

A 0.51-Approximation of Maximum Matching in Sublinear $n^{1.5}$ Time

Mohammad Roghani, Sepideh Mahabadi, Jakub Tarnawski
article en

Abstract

We study the problem of estimating the size of a maximum matching in sublinear time. The problem has been studied extensively in the literature and various algorithms and lower bounds are known for it. Our result is a $0.5109$-approximation algorithm with a running time of $\tilde{O}(n\sqrt{n})$. All previous algorithms either provide only a marginal improvement (e.g., $2^{-280}$) over the $0.5$-approximation that arises from estimating a \emph{maximal} matching, or have a running time that is nearly $n^2$. Our approach is also arguably much simpler than other algorithms beating $0.5$-approximation.

ArXiv.org
Microsoft (United States) (US), Stanford University (US)
Openalex Percentile: Top 71%
Matrix Theory and Algorithms
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