Navigating Central Path with Electrical Flows: from Flows to Matchings, and Back
Not on the map. Why?
Abstract (arXiv)
We present an tildeO(m10/7)=tildeO(m1.43)-time algorithm for the maximum s-t flow and the minimum s-t cut problems in directed graphs with unit capacities. This is the first improvement over the sparse-graph case of the long-standing O(m min(√m,n2/3)) time bound due to Even and Tarjan [EvenT75]. By well-known reductions, this also establishes an tildeO(m10/7)-time algorithm for the maximum-cardinality bipartite matching problem. That, in turn, gives an improvement over the celebrated celebrated O(m √n) time bound of Hopcroft and Karp [HK73] whenever the input graph is sufficiently sparse.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jul 2013.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 3; 2023: 0; 2024: 2; 2025: 1.
7 papers.
- 2022Training Overparametrized Neural Networks in Sublinear Timecited by 22
- 2022Dynamic Tensor Product Regressioncited by 17
- 2021Decomposable Submodular Function Minimization via Maximum Flowcited by 3
- 2022The Complexity of Dynamic Least-Squares Regressioncited by 2
- 2024Quantum Speedup for Spectral Approximation of Kronecker Productscited by 1
- 2024Binary Hypothesis Testing for Softmax Models and Leverage Score Modelscited by 1
- 2025Hierarchical Forecast Reconciliation on Networks: A Network Flow Optimization Formulationcited by 0
- 2025Hierarchical Forecast Reconciliation on Networks: A Network Flow Optimization Formulation6 May 2025
- 2024Binary Hypothesis Testing for Softmax Models and Leverage Score Models9 May 2024
- 2024Quantum Speedup for Spectral Approximation of Kronecker Products10 Feb 2024
- 2022Dynamic Tensor Product Regression8 Oct 2022
- 2022Training Overparametrized Neural Networks in Sublinear Time9 Aug 2022
- 2022The Complexity of Dynamic Least-Squares Regression1 Jan 2022
- 2021Decomposable Submodular Function Minimization via Maximum Flow5 Mar 2021