# Computing Maximum Flow with Augmenting Electrical Flows

arXiv: https://arxiv.org/abs/1608.06016 · HTML: https://mlsanity.com/paper/1608.06016/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2016.

## Built on by (most cited first, 5 of 5)

- [Training Overparametrized Neural Networks in Sublinear Time](https://mlsanity.com/paper/2208.04508/) (2022)
- [Dynamic Tensor Product Regression](https://mlsanity.com/paper/2210.03961/) (2022)
- [Decomposable Submodular Function Minimization via Maximum Flow](https://mlsanity.com/paper/2103.03868/) (2021)
- [Hierarchical Forecast Reconciliation on Networks: A Network Flow Optimization Formulation](https://mlsanity.com/paper/2505.03955/) (2025)
- [Theoretically and Practically Efficient Resistance Distance Computation on Large Graphs](https://mlsanity.com/paper/2601.11159/) (2026)

## Abstract (arXiv)

We present an tildeOleft(m^frac107U^frac17right)-time algorithm for the maximum s-t flow problem and the minimum s-t cut problem in directed graphs with m arcs and largest integer capacity U. This matches the running time of the tildeOleft((mU)^frac107right)-time algorithm of Mkadry (FOCS 2013) in the unit-capacity case, and improves over it, as well as over the tildeOleft(m √n log Uright)-time algorithm of Lee and Sidford (FOCS 2014), whenever U is moderately large and the graph is sufficiently sparse. By well-known reductions, this also gives similar running time improvements for the maximum-cardinality bipartite b-matching problem. One of the advantages of our algorithm is that it is significantly simpler than the ones presented in Madry (FOCS 2013) and Lee and Sidford (FOCS 2014). In particular, these algorithms employ a sophisticated interior-point method framework, while our algorithm is cast directly in the classic augmenting path setting that almost all the combinatorial maximum flow algorithms use. At a high level, the presented algorithm takes a primal dual approach in which each iteration uses electrical flows computations both to find an augmenting s-t flow in the current residual graph and to update the dual solution. We show that by maintain certain careful coupling of these primal and dual solutions we are always guaranteed to make significant progress.