论文标题

连通性问题的紧密界限通过cutwidth参数

Tight Bounds for Connectivity Problems Parameterized by Cutwidth

论文作者

Bojikian, Narek, Chekan, Vera, Hegerfeld, Falko, Kratsch, Stefan

论文摘要

在这项工作中,我们启动了假设强大的指数时间假设(SETH)的CUTWIDTH参数的连通性问题的紧密复杂性范围的研究。 van Geffen等。为奇数循环横向和反馈顶点集提出了这个问题。我们为这两个和四个问题(即连接的顶点盖,连接的Domintaing Set,Steiner树和连接的奇数循环横向)回答。对于后两个问题,足以证明与从树宽参数化继承的运行时间相匹配的下限;对于其他人,我们提供的算法比相对于树宽的算法更快,并证明了匹配的下限。对于上限,我们首先扩展了Groenland等人的想法。此类问题由$ \ Mathbb {F} _2 $由一组颜色索引的对称矩阵$ m $定义。目的是计算图形的数字(Modulo一些Prime $ p $),以便$ m $具有$ 1 $ - 输入,如果由任何边缘的端点的颜色索引。我们表明,如果$ m $的排名超过$ \ m mathbb {f} _p $,则可以更快地解决此问题。我们将此结果应用于连接的顶点盖和连接的主导集。奇数循环横向和反馈顶点集的上限使用细分技巧,以低于矩阵等级产生的边界。

In this work we start the investigation of tight complexity bounds for connectivity problems parameterized by cutwidth assuming the Strong Exponential-Time Hypothesis (SETH). Van Geffen et al. posed this question for odd cycle transversal and feedback vertex set. We answer it for these two and four further problems, namely connected vertex cover, connected domintaing set, steiner tree, and connected odd cycle transversal. For the latter two problems it sufficed to prove lower bounds that match the running time inherited from parameterization by treewidth; for the others we provide faster algorithms than relative to treewidth and prove matching lower bounds. For upper bounds we first extend the idea of Groenland et al.~[STACS~2022] to solve what we call coloring-like problem. Such problems are defined by a symmetric matrix $M$ over $\mathbb{F}_2$ indexed by a set of colors. The goal is to count the number (modulo some prime $p$) of colorings of a graph such that $M$ has a $1$-entry if indexed by the colors of the end-points of any edge. We show that this problem can be solved faster if $M$ has small rank over $\mathbb{F}_p$. We apply this result to get our upper bounds for connected vertex cover and connected dominating set. The upper bounds for odd cycle transversal and feedback vertex set use a subdivision trick to get below the bounds that matrix rank would yield.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源