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.
Authors
- Mohammad Roghani
- Sepideh Mahabadi (ORCID: https://orcid.org/0000-0001-5004-8991)
- Jakub Tarnawski (ORCID: https://orcid.org/0000-0001-6175-5827)
Institutions
- Microsoft (United States) (US)
- Stanford University (US)
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