多叶或少叶线性拓扑(深度优先生成树)的参数化复杂度

IF 0.9 3区 计算机科学 Q1 BUSINESS, FINANCE
Benjamin Bergougnoux , Nello Blaser , Michael Fellows , Petr Golovach , Frances Rosamond , Emmanuel Sam
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引用次数: 0

摘要

本文考虑了网络设计中可能应用的四个问题:给定一个|G|=n且整数k≥0的图G, G是否存在(i)≤k个叶子,(ii)≥k个叶子,(iii)≤n−k个叶子,(iv)≥n−k个叶子的DFS树?我们证明这四个问题都是np困难的。当用k参数化时,我们证明了(i)是para-NP-hard, (ii)是W - [1]-hard, (iii)和(iv)都承认有O(k3)个顶点的多项式核,这意味着FPT算法在kO(k)⋅nO(1)时间内运行。我们的多项式核基于与这些问题的解相关的O(k)大小的顶点覆盖结构。作为副产品,我们得到了这些问题的多项式核,这些问题由输入图的顶点覆盖数参数化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the parameterized complexity of lineal topologies (depth-first spanning trees) with many or few leaves
This paper considers four problems with possible applications in network design: Given a graph G with |G|=n and an integer k0, does G have a DFS tree with (i) ≤k leaves, (ii) ≥k leaves, (iii) nk leaves, and (iv) nk leaves? We show that all four problems are NP-hard. When parameterized by k, we prove that while (i) is para-NP-hard and (ii) is W[1]-hard, both (iii) and (iv) admit polynomial kernels with O(k3) vertices, implying FPT algorithms running in kO(k)nO(1) time. Our polynomial kernels are based on a O(k)-sized vertex cover structure associated with the solution of these problems. As a byproduct, we obtain polynomial kernels for these problems parameterized by the vertex cover number of the input graph.
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来源期刊
Journal of Computer and System Sciences
Journal of Computer and System Sciences 工程技术-计算机:理论方法
CiteScore
3.70
自引率
0.00%
发文量
58
审稿时长
68 days
期刊介绍: The Journal of Computer and System Sciences publishes original research papers in computer science and related subjects in system science, with attention to the relevant mathematical theory. Applications-oriented papers may also be accepted and they are expected to contain deep analytic evaluation of the proposed solutions. Research areas include traditional subjects such as: • Theory of algorithms and computability • Formal languages • Automata theory Contemporary subjects such as: • Complexity theory • Algorithmic Complexity • Parallel & distributed computing • Computer networks • Neural networks • Computational learning theory • Database theory & practice • Computer modeling of complex systems • Security and Privacy.
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