通过边收缩减少顶点覆盖数

IF 1.1 3区 计算机科学 Q1 BUSINESS, FINANCE Journal of Computer and System Sciences Pub Date : 2023-09-01 DOI:10.1016/j.jcss.2023.03.003
Paloma T. Lima , Vinicius F. dos Santos , Ignasi Sau , Uéverton S. Souza , Prafullkumar Tale
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引用次数: 0

摘要

给定一个图G在n个顶点和两个整数k和d上,收缩(vc)问题询问是否可以收缩最多k条边,以将G的顶点覆盖数减少至少d。最近,Lima等人[JCSS 2021]证明了收缩(vc)允许XP算法在时间f(d)·nO(d)内运行。他们询问在这个参数化条件下这个问题是否是FPT。在本文中,我们证明了:(i)收缩(vc)是由k+d硬参数化的W[1]。此外,除非ETH失败,否则该问题不允许任何函数f的算法在时间f(k+d)-no(k+d)内运行。这否定了Lima等人[JCSS 2021]中提出的开放问题。(ii)收缩(vc)是NP难的,即使当k=d时也是如此。(iii)收缩(vc)可在时间2O(d)·nk−d+O(1)内求解。这改进了Lima等人的算法。[JCSS 2021],并表明当k=d时,收缩(vc)是由d(或k)参数化的FPT。
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Reducing the vertex cover number via edge contractions

Given a graph G on n vertices and two integers k and d, the Contraction(vc) problem asks whether one can contract at most k edges to reduce the vertex cover number of G by at least d. Recently, Lima et al. [JCSS 2021] proved that Contraction(vc) admits an XP algorithm running in time f(d)nO(d). They asked whether this problem is FPT under this parameterization. In this article, we prove that: (i) Contraction(vc) is W[1]-hard parameterized by k+d. Moreover, unless the ETH fails, the problem does not admit an algorithm running in time f(k+d)no(k+d) for any function f. This answers negatively the open question stated in Lima et al. [JCSS 2021]. (ii) Contraction(vc) is NP-hard even when k=d. (iii) Contraction(vc) can be solved in time 2O(d)nkd+O(1). This improves the algorithm of Lima et al. [JCSS 2021], and shows that when k=d, Contraction(vc) is FPT parameterized by d (or by k).

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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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