arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.00134cs.DS
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最小$k$ -割的参数化逼近格式

A Parameterized Approximation Scheme for Min $k$-Cut

Daniel Lokshtanov, Saket Saurabh, Vaishali Surianarayanan

摘要:

在最小$k$-Cut问题中,输入是边加权图$G$和整数$k$,任务是将顶点集划分为$k$非空集,使得端点在不同部分的边的总权重最小化。当$k$是输入的一部分时,问题是NP完全的,并且很难在任何小于$2$的因子内近似。最近,这个问题已经从参数化逼近的角度受到了极大的关注。古普塔等人~ [SODA 2018]开创了最小$k$ -割问题的FPT-近似研究,并给出了一个在时间上运行的$1.9997$ -近似算法$2^{\mathcal{O}(k^6)}n^{\mathcal{O}(1)}$。后来,同一组作者~[FOCS 2018]设计了在时间上运行的$(1 +ε)$ -近似算法$(k/ε)^{\mathcal{O}(k)}n^{k+\mathcal{O}(1)}$和在时间上运行的$1.81$ -近似算法$2^{\mathcal{O}(k^2)}n^{\mathcal{O}(1)}$。最近,Kawarabayashi和Lin~[SODA 2020]给出了在时间上运行的Min $k$ -Cut的$(5/3 + ε)$ -近似$2^{\mathcal{O}(k^2 \log k)}n^{\mathcal{O}(1)}$ .在本文中,我们给出了一个参数化的近似算法与最佳可能的近似保证,和最佳可能的运行时间依赖于所述保证(指数时间假设(ETH)和常数的指数)。特别地,对于每个$ε> 0$,算法获得时间上的$(1 +ε)$近似解$(k/ε)^{\mathcal{O}(k)}n^{\mathcal{O}(1)}$。我们的算法的主要成分是:一个简单的稀疏化过程中,一个新的多项式时间算法分解成高连通部分的图,和一个新的精确算法与运行时间$s^{\mathcal{O}(k)}n^{\mathcal{O}(1)}$的未加权(多)图。这里,$s$表示最小$k$ -切割中的边的数量。后两者是独立的利益。

英文摘要:

In the Min $k$-Cut problem, input is an edge weighted graph $G$ and an integer $k$, and the task is to partition the vertex set into $k$ non-empty sets, such that the total weight of the edges with endpoints in different parts is minimized. When $k$ is part of the input, the problem is NP-complete and hard to approximate within any factor less than $2$. Recently, the problem has received significant attention from the perspective of parameterized approximation. Gupta et al.~[SODA 2018] initiated the study of FPT-approximation for the Min $k$-Cut problem and gave an $1.9997$-approximation algorithm running in time $2^{\mathcal{O}(k^6)}n^{\mathcal{O}(1)}$. Later, the same set of authors~[FOCS 2018] designed an $(1 +ε)$-approximation algorithm that runs in time $(k/ε)^{\mathcal{O}(k)}n^{k+\mathcal{O}(1)}$, and a $1.81$-approximation algorithm running in time $2^{\mathcal{O}(k^2)}n^{\mathcal{O}(1)}$. More, recently, Kawarabayashi and Lin~[SODA 2020] gave a $(5/3 + ε)$-approximation for Min $k$-Cut running in time $2^{\mathcal{O}(k^2 \log k)}n^{\mathcal{O}(1)}$. In this paper we give a parameterized approximation algorithm with best possible approximation guarantee, and best possible running time dependence on said guarantee (up to Exponential Time Hypothesis (ETH) and constants in the exponent). In particular, for every $ε> 0$, the algorithm obtains a $(1 +ε)$-approximate solution in time $(k/ε)^{\mathcal{O}(k)}n^{\mathcal{O}(1)}$. The main ingredients of our algorithm are: a simple sparsification procedure, a new polynomial time algorithm for decomposing a graph into highly connected parts, and a new exact algorithm with running time $s^{\mathcal{O}(k)}n^{\mathcal{O}(1)}$ on unweighted (multi-) graphs. Here, $s$ denotes the number of edges in a minimum $k$-cut. The latter two are of independent interest.

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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