Solving Cut-Problems in Quadratic Time for Graphs With Bounded Treewidth

Abstract: In the problem (Unweighted) Max-Cut we are given a graph $G = (V,E)$ and asked for a set $S \subseteq V$ such that the number of edges from $S$ to $V \setminus S$ is maximal. In this paper we consider an even harder problem: (Weighted) Max-Bisection. Here we are given an undirected graph $G = (V,E)$ and a weight function $w \colon E \to \mathbb Q_{&gt;0}$ and the task is to find a set $S \subseteq V$ such that (i) the sum of the weights of edges from $S$ is maximal; and (ii) $S$ contains $\left\lceil{\frac{n}{2}}\right\rceil$ vertices (where $n = \lvert V\rvert$). We design a framework that allows to solve this problem in time $\mathcal O(2^t n<sup>2)$ if a tree decomposition of width $t$ is given as part of the input. This improves the previously best running time for Max-Bisection of [DBLP:journals/tcs/HanakaKS21] by a factor $t<sup>2$. Under common hardness assumptions, neither the dependence on $t$ in the exponent nor the dependence on $n$ can be reduced [DBLP:journals/tcs/HanakaKS21,DBLP:journals/jcss/EibenLM21,DBLP:journals/talg/LokshtanovMS18]. Our framework can be applied to other cut problems like Min-Edge-Expansion, Sparsest-Cut, Densest-Cut, $\beta$-Balanced-Min-Cut, and Min-Bisection. It also works in the setting with arbitrary weights and directed edges.