Breaking the O(mn)-Time Barrier for Vertex-Weighted Global Minimum Cut

TL;DR

This paper introduces a randomized algorithm that significantly improves the running time for solving the general weighted global minimum vertex cut problem, breaking a long-standing computational complexity barrier.

Contribution

It presents the first algorithm with sub-quadratic time complexity for the general weighted case, surpassing the 28-year-old bound for this problem.

Findings

Achieves a running time of O(min{mn^{0.99+o(1)}, m^{1.5+o(1)}})

Breaks the longstanding O(mn) time barrier for the problem

Provides a randomized approach with practical efficiency improvements.

Abstract

We consider the Global Minimum Vertex-Cut problem: given an undirected vertex-weighted graph $G$, compute a minimum-weight subset of its vertices whose removal disconnects $G$. The problem is closely related to Global Minimum Edge-Cut, where the weights are on the graph edges instead of vertices, and the goal is to compute a minimum-weight subset of edges whose removal disconnects the graph. Global Minimum Cut is one of the most basic and extensively studied problems in combinatorial optimization and graph theory. While an almost-linear time algorithm was known for the edge version of the problem for awhile (Karger, STOC 1996 and J. ACM 2000), the fastest previous algorithm for the vertex version (Henzinger, Rao and Gabow, FOCS 1996 and J. Algorithms 2000) achieves a running time of $\tilde{O}(mn)$, where $m$ and $n$ denote the number of edges and vertices in the input graph, respectively. For the special case of unit vertex weights, this bound was broken only recently (Li {et al.}, STOC 2021); their result, combined with the recent breakthrough almost-linear time algorithm for Maximum $s$-$t$ Flow (Chen {et al.}, FOCS 2022, van den Brand {et al.}, FOCS 2023), yields an almost-linear time algorithm for Global Minimum Vertex-Cut with unit vertex weights. In this paper we break the $28$ years old bound of Henzinger {et al.} for the general weighted Global Minimum Vertex-Cut, by providing a randomized algorithm for the problem with running time $O(\min\{mn^{0.99+o(1)},m^{1.5+o(1)}\})$.