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arXiv 2512.09899cs.CG
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近线性和参数化逼近在盘图最大团问题中的应用

Near-Linear and Parameterized Approximations for Maximum Cliques in Disk Graphs

Rutgers University(罗格斯大学);University of Wrocław(弗罗茨瓦夫大学);City St George’s, University of London(伦敦大学圣乔治学院);Freie Universität Berlin(柏林自由大学);Aalto University(阿尔托大学);Utrecht University(乌得勒支大学);Ben Gurion University of the Negev(内盖夫本古里安大学)

Jie Gao, Pawel Gawrychowski, Panos Giannopoulos, Wolfgang Mulzer, Satyam Singh, Frank Staals, Meirav Zehavi

AI总结:

本文提出基于近似解和随机化的算法,改进了盘图最大团问题的运行时间,适用于单位盘图和具有t个不同半径的盘图。

摘要:

盘图是平面中闭合圆盘的交集图。我们考虑寻找盘图最大团的经典问题。对于一般的盘图,该问题的复杂性仍是一个开放问题,但对于单位盘图,已知其属于P类。目前最快的算法运行时间为O(n^{7/3+o(1)}),其中n表示圆盘的数量。此外,对于具有t个不同半径的盘图,该问题也已最近被证明属于XP类。更具体地说,其解复杂度为O*(n^{2t})。在本文中,我们通过允许近似解和利用随机化方法,提出了具有改进运行时间的算法:对于单位盘图,我们给出一个算法,以常数成功概率计算(1-ε)近似最大团,预期时间为O~(n/ε²);对于具有t个不同半径的盘图,我们给出一个参数化近似方案,以常数成功概率计算(1-ε)近似最大团,预期时间为O~(f(t)·(1/ε)^{O(t)}·n),其中f(t)是一个(指数)函数。

英文摘要:

A \emph{disk graph} is the intersection graph of (closed) disks in the plane. We consider the classic problem of finding a maximum clique in a disk graph. For general disk graphs, the complexity of this problem is still open, but for unit disk graphs, it is well known to be in P. The currently fastest algorithm runs in time $O(n^{7/3+ o(1)})$, where $n$ denotes the number of disks~\cite{EspenantKM23, keil_et_al:LIPIcs.SoCG.2025.63}. Moreover, for the case of disk graphs with $t$ distinct radii, the problem has also recently been shown to be in XP. More specifically, it is solvable in time $O^*(n^{2t})$~\cite{keil_et_al:LIPIcs.SoCG.2025.63}. In this paper, we present algorithms with improved running times by allowing for approximate solutions and by using randomization: - for unit disk graphs, we give an algorithm that, with constant success probability, computes a $(1-\varepsilon)$-approximate maximum clique in expected time $\tilde{O}(n/\varepsilon^2)$; and - for disk graphs with $t$ distinct radii, we give a parameterized approximation scheme that, with a constant success probability, computes a $(1-\varepsilon)$-approximate maximum clique in expected time $\tilde{O}(f(t)\cdot (1/\varepsilon)^{O(t)} \cdot n)$, for some (exponential) function $f(t)$.

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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