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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个顶点和m个边上的图G,顶点T的子集(称为终端)和整数k。目的是确定是否存在与包含一个终端的每个循环相交的至多k个顶点的集合。在过去的几年中,用于反馈顶点集问题泛化的参数化算法的研究受到了广泛的关注。实际上,这个问题的参数化复杂性一直持续到2011年,当时两组独立显示该问题是固定参数易处理的(FPT)。使用未成年人的工具Kawarabayashi和Kobayashi获得了时间为O(f(k)-n〜2m)的子集FVS运行算法[SODA 2012,JCTB 2012]。独立地,Cygan等。 [ICALP 2011,SIDMA 2013]设计了一种在时间2〜(O(klogk))·n〜(O(1))中运行的子集FVS的算法。最近,Wahlstroem获得了第一个用于子集FVS的单指数时间算法,该算法在时间4〜k·n〜(O(1))中运行[SODA 2014]。虽然在指数时间假设(ETH)下对参数k的2〜(O(k))依赖关系是最佳的,但此算法及其之前的算法对输入大小的依赖关系远非线性。在本文中,我们为子集FVS设计了第一个线性时间参数化算法。更准确地说,我们为子集FVS获得了两种新算法。 1.在时间O(25.6〜kk〜(O(1))(n + m))中运行的子集FVS的随机算法。 2.用于子集FVS的及时运行的确定性算法特别是,第一个算法获得对参数以及输入大小的最佳依赖,直到指数中的常数。我们的两种算法都基于“切割中心点”,从某种意义上来说,解决方案顶点可能会出现在从精心选择的分布采样的顶点之间的最小尺寸切割中。

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