Faster Weak Expander Decompositions and Approximate Max Flow

TL;DR

This paper introduces faster algorithms for weak expander decompositions and approximate max flow in undirected graphs, leveraging improved techniques to reduce computational complexity and enhance efficiency.

Contribution

It presents a novel approach to warm start the cut-matching game and streamlines existing max flow algorithms using new weak expander decomposition primitives.

Findings

Faster weak expander decomposition algorithms achieved.

Approximate max flow algorithms improved to near-optimal efficiency.

Reduced recursion depth and computational costs in graph algorithms.

Abstract

We give faster algorithms for weak expander decompositions and approximate max flow on undirected graphs. First, we show that it is possible to "warm start" the cut-matching game when computing weak expander decompositions, avoiding the cost of the recursion depth. Our algorithm is also flexible enough to support weaker flow subroutines than previous algorithms. Our second contribution is to streamline the recent non-recursive approximate max flow algorithm of Li, Rao, and Wang (SODA, 2025) and adapt their framework to use our new weak expander decomposition primitive. Consequently, we give an approximate max flow algorithm within a few logarithmic factors of the limit of expander decomposition-based approaches.