mlsanityData to 7 Oct 2026

Bipartite Matching in Nearly-linear Time on Moderately Dense Graphs

arXiv 2009.01802 ↗First posted 3 Sep 2020cs.DS math.OCcited by 11

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Abstract (arXiv)

We present an tilde O(m+n1.5)-time randomized algorithm for maximum cardinality bipartite matching and related problems (e.g. transshipment, negative-weight shortest paths, and optimal transport) on m-edge, n-node graphs. For maximum cardinality bipartite matching on moderately dense graphs, i.e. m = Omega(n1.5), our algorithm runs in time nearly linear in the input size and constitutes the first improvement over the classic O(m√n)-time [Dinic 1970; Hopcroft-Karp 1971; Karzanov 1973] and tilde O(nω)-time algorithms [Ibarra-Moran 1981; Mucha-Sankowski 2004] (where currently ω≈ 2.373). On sparser graphs, i.e. when m = n9/8 + δ for any constant δ>0, our result improves upon the recent advances of [Madry 2013] and [Liu-Sidford 2020b, 2020a] which achieve an tilde O(m4/3+o(1)) runtime. We obtain these results by combining and advancing recent lines of research in interior point methods (IPMs) and dynamic graph algorithms. First, we simplify and improve the IPM of [v.d.Brand-Lee-Sidford-Song 2020], providing a general primal-dual IPM framework and new sampling-based techniques for handling infeasibility induced by approximate linear system solvers. Second, we provide a simple sublinear-time algorithm for detecting and sampling high-energy edges in electric flows on expanders and show that when combined with recent advances in dynamic expander decompositions, this yields efficient data structures for maintaining the iterates of both [v.d.Brand et al.] and our new IPMs. Combining this general machinery yields a simpler tilde O(n √m) time algorithm for matching based on the logarithmic barrier function, and our state-of-the-art tilde O(m+n1.5) time algorithm for matching based on the [Lee-Sidford 2014] barrier (as regularized in [v.d.Brand et al.]).

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Sep 2020.

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Citing papers per year: 2021: 3; 2022: 1; 2023: 2; 2024: 3; 2025: 1; 2026: 1.

11 papers.