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Linear Time Parameterized Algorithms for Subset Feedback Vertex Set

机译:子集反馈顶点集的线性时间参数化算法

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In the Subset Feedback Vertex Set (Subset FVS) problem, the input is a graph G on n vertices and m edges, a subset of vertices T, referred to as terminals, and an integer k. The objective is to determine whether there exists a set of at most k vertices intersecting every cycle that contains a terminal. The study of parameterized algorithms for this generalization of the Feedback Vertex Set problem has received significant attention over the last few years. In fact the parameterized complexity of this problem was open until 2011, when two groups independently showed that the problem is fixed parameter tractable (FPT). Using tools from graph minors Kawarabayashi and Kobayashi obtained an algorithm for Subset FVS running in time O(f(k) -n~2m) [SODA 2012, JCTB 2012]. Independently, Cygan et al. [ICALP 2011, SIDMA 2013] designed an algorithm for Subset FVS running in time 2~(O(klogk))·n~(O(1)). More recently, Wahlstroem obtained the first single exponential time algorithm for Subset FVS, running in time 4~k·n~(O(1)) [SODA 2014]. While the 2~(O(k)) dependence on the parameter k is optimal under the Exponential Time Hypothesis (ETH), the dependence of this algorithm as well as those preceding it, on the input size is far from linear. In this paper we design the first linear time parameterized algorithms for Subset FVS. More precisely, we obtain two new algorithms for Subset FVS. 1. A randomized algorithm for Subset FVS running in time O(25.6~kk~(O(1))(n + m)). 2. A deterministic algorithm for Subset FVS running in time In particular, the first algorithm obtains the best possible dependence on both the parameter as well as the input size, up to the constant in the exponent. Both of our algorithms are based on "cut centrality", in the sense that solution vertices are likely to show up in minimum size cuts between vertices sampled from carefully chosen distributions.
机译:在子集反馈顶点集(子集FVS)问题中,输入是N顶点和M边的图G,顶点T的子集,称为终端,以及整数k。目的是确定是否存在一组最多的k顶点,这些顶点交往包含终端的每个循环。对该反馈顶点组问题的这种概括的参数化算法的研究在过去几年中受到了重大关注。事实上,此问题的参数化复杂性在2011年之前开放,当两个组独立地显示问题是固定参数贸易(FPT)。使用Graph Minors Kawarabayashi和Kobayashi的工具获得了在时间O(f(k)-n〜2m)中运行的子集Fvs算法[SOTA 2012,JCTB 2012]。独立,Cygan等人。 [ICALP 2011,SIDMA 2013]设计了一种用于在时间2〜(o(klogk))·n〜(o(1))的子集FVS算法。最近,Wahlstroem获得了子集FVS的第一单指数时间算法,运行时间4〜k·n〜(o(1))[苏打水2014]。虽然在指数时间假设(Eth)下,对参数K的2〜(o(k))依赖性是最佳的,但是该算法的依赖性以及在输入尺寸上的依赖性远离线性。在本文中,我们设计了子集FVS的第一线性时间参数化算法。更确切地说,我们获得了两个用于子集FV的新算法。 1.时间O(25.6〜KK〜(O(1))(N + M))的子集FVS的随机算法。 2.特定地运行的子集FVS的确定性算法,第一算法在参数和输入大小上获得最佳依赖性,直到指数中的常量。我们的两种算法都基于“Cut Centrality”,因此,解决方案顶点可能在从精心选择的分布中采样的顶点之间的最小尺寸切割中出现的意义上。

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