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arXiv 2005.01359cs.DS
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关于无$\mathcal{H}$强连通区删除的参数化复杂性

On the Parameterized Complexity of Deletion to $\mathcal{H}$-free Strong Components

Rian Neogi, M. S. Ramanujan, Saket Saurabh, Roohani Sharma

摘要:

{ \sc Directed Feedback Vertex Set(DFVS)}是参数化复杂度中一个受到广泛关注的基本计算问题。在本文中,我们开始研究一个广泛的推广,{ \sc ${\cal H}$ -免费SCC删除}的问题。在这里,给定一个有向图$D$,一个整数$k$和目标是决定是否有一个顶点集的大小最多$k$的删除离开一个有向图,其中每个强组件排除图中的固定有限家庭${\cal H}$作为(不一定是诱导)子图。当${\cal H}$仅包含具有单弧的有向图时,则该问题精确地为DFVS。我们的主要结果是证明了如果${\cal H}$只包含根图或如果${\cal H}$至少包含一条有向路,则该问题是由删除集的大小参数化的固定参数易处理的。我们的结果还推广了Göke等人[CIAC 2019]关于{ \sc 1-外正则顶点删除}和{ \sc有界大小强分量顶点删除}的最近结果问题此外,我们为上述两个问题设计了算法,其运行时间更好,并且与{ \sc DFVS}的最佳界限相匹配,而不使用Göke等人所做的阴影去除的重型机械。[CIAC 2019]。

英文摘要:

{\sc Directed Feedback Vertex Set (DFVS)} is a fundamental computational problem that has received extensive attention in parameterized complexity. In this paper, we initiate the study of a wide generalization, the {\sc ${\cal H}$-free SCC Deletion} problem. Here, one is given a digraph $D$, an integer $k$ and the objective is to decide whether there is a vertex set of size at most $k$ whose deletion leaves a digraph where every strong component excludes graphs in the fixed finite family ${\cal H}$ as (not necessarily induced) subgraphs. When ${\cal H}$ comprises only the digraph with a single arc, then this problem is precisely DFVS. Our main result is a proof that this problem is fixed-parameter tractable parameterized by the size of the deletion set if ${\cal H}$ only contains rooted graphs or if ${\cal H}$ contains at least one directed path. Along with generalizing the fixed-parameter tractability result for DFVS, our result also generalizes the recent results of Göke et al. [CIAC 2019] for the {\sc 1-Out-Regular Vertex Deletion} and {\sc Bounded Size Strong Component Vertex Deletion} problems. Moreover, we design algorithms for the two above mentioned problems, whose running times are better and match with the best bounds for {\sc DFVS}, without using the heavy machinery of shadow removal as is done by Göke et al. [CIAC 2019].

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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