论文标题
k-in-a-tree问题的FPT和内核化算法
FPT and kernelization algorithms for the k-in-a-tree problem
论文作者
论文摘要
三个in-a-tree问题要求一个包含三个强制性顶点的输入图的感应树。在2006年,Chudnovsky和Seymour [Combinatorica,2010年]提出了第一个针对此问题的多项式时间算法,这已成为许多用于检测诱导子读物(例如甲虫,金字塔,thetas,thetas等)的许多算法中的关键亚例子。在2007年,Derhy和Picouleau [离散应用数学,2009年]考虑了对$ k $强制性顶点的自然概括,证明,当$ k $是输入的一部分时,问题是$ \ mathsf {np} $ - 完整的,并且询问四英寸四英寸Tree的复杂性是什么。在这个问题和原始问题的相关性的推动下,我们研究了$ k $ -in-in-a-tree的参数化复杂性。首先,我们表明问题是$ \ mathsf {w [1]} $ - 当通过解决方案的大小和最小倾斜度封面共同参数时,很难,并且在指数时间假设下,不承认$ n^{o(k)} $ time time算法。之后,我们使用Courcelle的定理来证明cliquewidth下的固定参数障碍性,这促使我们调查了哪些参数化允许单个指数算法。我们表明,对于无关的参数化树,群集的距离以及与共群集的距离,存在这种算法。在内核化方面,我们在反馈边缘集合下提出了一个线性内核,并表明除非$ \ m athsf {np} \ subseteq \ subseteq \ mathsf {conp}/\ mathsf {poly} $。除其他备注和先前的工作外,我们的障碍性和内核化结果涵盖了图参数层次结构中许多最常用的参数。
The three-in-a-tree problem asks for an induced tree of the input graph containing three mandatory vertices. In 2006, Chudnovsky and Seymour [Combinatorica, 2010] presented the first polynomial time algorithm for this problem, which has become a critical subroutine in many algorithms for detecting induced subgraphs, such as beetles, pyramids, thetas, and even and odd-holes. In 2007, Derhy and Picouleau [Discrete Applied Mathematics, 2009] considered the natural generalization to $k$ mandatory vertices, proving that, when $k$ is part of the input, the problem is $\mathsf{NP}$-complete, and ask what is the complexity of four-in-a-tree. Motivated by this question and the relevance of the original problem, we study the parameterized complexity of $k$-in-a-tree. We begin by showing that the problem is $\mathsf{W[1]}$-hard when jointly parameterized by the size of the solution and minimum clique cover and, under the Exponential Time Hypothesis, does not admit an $n^{o(k)}$ time algorithm. Afterwards, we use Courcelle's Theorem to prove fixed-parameter tractability under cliquewidth, which prompts our investigation into which parameterizations admit single exponential algorithms; we show that such algorithms exist for the unrelated parameterizations treewidth, distance to cluster, and distance to co-cluster. In terms of kernelization, we present a linear kernel under feedback edge set, and show that no polynomial kernel exists under vertex cover nor distance to clique unless $\mathsf{NP} \subseteq \mathsf{coNP}/\mathsf{poly}$. Along with other remarks and previous work, our tractability and kernelization results cover many of the most commonly employed parameters in the graph parameter hierarchy.