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arXiv 2401.05085cs.DS
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最小和顶点覆盖的参数化算法

Parameterized Algorithms for Minimum Sum Vertex Cover

Shubhada Aute, Fahad Panolan

AI总结:

本文从参数化算法角度研究NP-hard的最小和顶点覆盖(MSVC)问题,基于最小顶点覆盖大小和最小团调制器大小两个参数,分别给出了对应的固定参数可解算法。

摘要:

$n$顶点图$G$的最小和顶点覆盖是一个双射$ϕ: V(G) \to [n]$,它使成本$\sum_{\{u,v\} \in E(G)} \min \{ϕ(u), ϕ(v) \}$最小化。寻找图的最小和顶点覆盖(MSVC问题)是NP-hard的。MSVC在近似算法领域得到了充分研究。该问题在多项式时间内已知的最佳近似因子是$16/9$ [Bansal, Batra, Farhadi, and Tetali, SODA 2021]。最近,Stankovic [APPROX/RANDOM 2022]证明,假设Unique Games Conjecture成立,为MSVC实现优于$1.014$的近似比是NP-hard的。我们从参数化算法的角度研究MSVC问题。我们考虑的参数是输入图的最小顶点覆盖的大小和最小团调制器的大小。我们获得以下结果。1. MSVC可以在$2^{2^{O(k)}} n^{O(1)}$时间内解决,其中$k$是最小顶点覆盖的大小。2. MSVC可以在$f(k)\cdot n^{O(1)}$时间内解决,其中$f$是某个可计算函数,$k$是最小团调制器的大小。

英文摘要:

Minimum sum vertex cover of an $n$-vertex graph $G$ is a bijection $ϕ: V(G) \to [n]$ that minimizes the cost $\sum_{\{u,v\} \in E(G)} \min \{ϕ(u), ϕ(v) \}$. Finding a minimum sum vertex cover of a graph (the MSVC problem) is NP-hard. MSVC is studied well in the realm of approximation algorithms. The best-known approximation factor in polynomial time for the problem is $16/9$ [Bansal, Batra, Farhadi, and Tetali, SODA 2021]. Recently, Stankovic [APPROX/RANDOM 2022] proved that achieving an approximation ratio better than $1.014$ for MSVC is NP-hard, assuming the Unique Games Conjecture. We study the MSVC problem from the perspective of parameterized algorithms. The parameters we consider are the size of a minimum vertex cover and the size of a minimum clique modulator of the input graph. We obtain the following results. 1. MSVC can be solved in $2^{2^{O(k)}} n^{O(1)}$ time, where $k$ is the size of a minimum vertex cover. 2. MSVC can be solved in $f(k)\cdot n^{O(1)}$ time for some computable function $f$, where $k$ is the size of a minimum clique modulator.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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