English

Faster Parameterized Vertex Multicut

Data Structures and Algorithms 2026-02-17 v1

Abstract

In the {\sc Vertex Multicut} problem the input consists of a graph GG, integer kk, and a set T={(s1,t1),,(sp,tp)}\mathbf{T} = \{(s_1, t_1), \ldots, (s_p, t_p)\} of pairs of vertices of GG. The task is to find a set XX of at most kk vertices such that, for every (si,ti)T(s_i, t_i) \in \mathbf{T}, there is no path from sis_i to tit_i in GXG - X. Marx and Razgon [STOC 2011 and SICOMP 2014] and Bousquet, Daligault, and Thomass\'{e} [STOC 2011 and SICOMP 2018] independently and simultaneously gave the first algorithms for {\sc Vertex Multicut} with running time f(k)nO(1)f(k)n^{O(1)}. The running time of their algorithms is 2O(k3)nO(1)2^{O(k^3)}n^{O(1)} and 2O(kO(1))nO(1)2^{O(k^{O(1)})}n^{O(1)}, respectively. As part of their result, Marx and Razgon introduce the {\em shadow removal} technique, which was subsequently applied in algorithms for several parameterized cut and separation problems. The shadow removal step is the only step of the algorithm of Marx and Razgon which requires 2O(k3)nO(1)2^{O(k^3)}n^{O(1)} time. Chitnis et al. [TALG 2015] gave an improved version of the shadow removal step, which, among other results, led to a kO(k2)nO(1)k^{O(k^2)}n^{O(1)} time algorithm for {\sc Vertex Multicut}. We give a faster algorithm for the {\sc Vertex Multicut} problem with running time kO(k)nO(1)k^{O(k)}n^{O(1)}. Our main technical contribution is a refined shadow removal step for vertex separation problems that only introduces an overhead of kO(k)lognk^{O(k)}\log n time. The new shadow removal step implies a kO(k2)nO(1)k^{O(k^2)}n^{O(1)} time algorithm for {\sc Directed Subset Feedback Vertex Set} and a kO(k)nO(1)k^{O(k)}n^{O(1)} time algorithm for {\sc Directed Multiway Cut}, improving over the previously best known algorithms of Chitnis et al. [TALG 2015].

Keywords

Cite

@article{arxiv.2602.13981,
  title  = {Faster Parameterized Vertex Multicut},
  author = {Huairui Chu and Yuxi Liu and Daniel Lokshtanov and Junqiang Peng and Kangyi Tian and Mingyu Xiao},
  journal= {arXiv preprint arXiv:2602.13981},
  year   = {2026}
}
R2 v1 2026-07-01T10:37:16.625Z