Maximum Flow by Augmenting Paths in $n^{2+o(1)}$ Time

TL;DR

This paper introduces a nearly optimal combinatorial maximum flow algorithm for dense graphs, improving upon longstanding bounds without relying on continuous optimization or complex data structures.

Contribution

The authors develop a novel augmenting-path algorithm using edge weights from a directed expander hierarchy, achieving faster maximum flow computation in dense graphs.

Findings

Breaks previous $O(m\,\min\{\sqrt{m},n^{2/3}\})$ time bound for dense graphs.

Matches the $n^{2+o(1)}$ time complexity of recent algorithms for maximum bipartite matching.

Does not depend on heavy dynamic graph data structures or continuous optimization.

Abstract

We present a combinatorial algorithm for computing exact maximum flows in directed graphs with $n$ vertices and edge capacities from $\{1,\dots,U\}$ in $n^{2+o(1)}\log U$ time, which is almost optimal in dense graphs. Our algorithm is a novel implementation of the classical augmenting-path framework; we list augmenting paths more efficiently using a new variant of the push-relabel algorithm that uses additional edge weights to guide the algorithm, and we derive the edge weights by constructing a directed expander hierarchy. Even in unit-capacity graphs, this breaks the long-standing $O(m\cdot\min\{\sqrt{m},n^{2/3}\})$ time bound of the previous combinatorial algorithms by Karzanov (1973) and Even and Tarjan (1975) when the graph has $m=\omega(n^{4/3})$ edges. Notably, our approach does not rely on continuous optimization nor heavy dynamic graph data structures, both of which are crucial in the recent developments that led to the almost-linear time algorithm by Chen et al. (FOCS 2022). Our running time also matches the $n^{2+o(1)}$ time bound of the independent combinatorial algorithm by Chuzhoy and Khanna (STOC 2024) for computing the maximum bipartite matching, a special case of maximum flow.