磁盘图中的动态连接性

Pub Date : 2024-01-03 DOI:10.1007/s00454-023-00621-x
Alexander Baumann, Haim Kaplan, Katharina Klost, Kristin Knorr, Wolfgang Mulzer, Liam Roditty, Paul Seiferth
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引用次数: 0

摘要

让(S (subseteq \mathbb {R}^2)是平面上n个点的集合,这样每个点(s (在S中)都有一个相关的半径(r_s >0)。让 \(\mathcal {D}(S)\) 是由 S 定义的圆盘相交图,即具有顶点集 S 的图,当且仅当具有中心 s、t 和半径 \(r_s\)、\(r_t\) 的圆盘相交时,两个不同的站点 \(s, t\in S\) 之间有一条边。我们的目标是设计一种数据结构,当站点在 S 中插入和/或删除时,这种数据结构可以保持 \(\mathcal {D}(S)\) 的连通性结构。首先,我们考虑单位盘图,也就是说,我们为所有站点 \(s\in S\) 固定 \(r_s=1\)。对于这种情况,我们描述了一种数据结构,它具有 \(O(\log ^2 n)\)摊销更新时间和 \(O(\log n/\log \log n)\)查询时间。其次,我们研究的是具有有界半径比的(\Psi \)磁盘图,即对于所有的(s \in S\),我们有(1 \le r_s \le \Psi \),参数(\Psi \)是事先已知的。在这里,我们不仅研究了全动态情况,还研究了增量和减量情况,即只允许插入或只允许删除站点。在全动态情况下,我们实现了预期更新时间(O(\Psi \log ^{4} n)\)和查询时间(O(\log n/\log \log n)\)的摊销。这将当前最佳的更新时间提高了一个系数(\Psi \)。在增量情况下,我们实现了对(\Psi \)的对数依赖,数据结构具有(\(O(\alpha (n))\)摊销查询时间和(\(O(\log \Psi \log ^{4} n)\)摊销预期更新时间,其中(\(\alpha (n)\)表示反阿克曼函数。对于递减设置,我们首先开发了一种高效的递减磁盘揭示数据结构:给定平面中的两个磁盘集合 R 和 B,我们可以从 B 中删除磁盘,每次删除后,我们都会收到 R 中不再与 B 的结合部相交的所有磁盘的列表。使用这种数据结构,我们可以得到查询时间为 \(O(\log n/\log \log n)\)的递减数据结构,对于具有有界半径比 \(\Psi \log ^{4} n)的磁盘图,支持删除的总体预期时间为 \(O(n\log \Psi \log ^{4} n)\),而对于具有任意半径的磁盘图,支持删除的总体预期时间为 \(O(n\log ^{5} n)\)、假设删除序列不考虑数据结构的内部随机选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Dynamic Connectivity in Disk Graphs

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Dynamic Connectivity in Disk Graphs

Let \(S \subseteq \mathbb {R}^2\) be a set of n sites in the plane, so that every site \(s \in S\) has an associated radius \(r_s > 0\). Let \(\mathcal {D}(S)\) be the disk intersection graph defined by S, i.e., the graph with vertex set S and an edge between two distinct sites \(s, t \in S\) if and only if the disks with centers s, t and radii \(r_s\), \(r_t\) intersect. Our goal is to design data structures that maintain the connectivity structure of \(\mathcal {D}(S)\) as sites are inserted and/or deleted in S. First, we consider unit disk graphs, i.e., we fix \(r_s = 1\), for all sites \(s \in S\). For this case, we describe a data structure that has \(O(\log ^2 n)\) amortized update time and \(O(\log n/\log \log n)\) query time. Second, we look at disk graphs with bounded radius ratio \(\Psi \), i.e., for all \(s \in S\), we have \(1 \le r_s \le \Psi \), for a parameter \(\Psi \) that is known in advance. Here, we not only investigate the fully dynamic case, but also the incremental and the decremental scenario, where only insertions or only deletions of sites are allowed. In the fully dynamic case, we achieve amortized expected update time \(O(\Psi \log ^{4} n)\) and query time \(O(\log n/\log \log n)\). This improves the currently best update time by a factor of \(\Psi \). In the incremental case, we achieve logarithmic dependency on \(\Psi \), with a data structure that has \(O(\alpha (n))\) amortized query time and \(O(\log \Psi \log ^{4} n)\) amortized expected update time, where \(\alpha (n)\) denotes the inverse Ackermann function. For the decremental setting, we first develop an efficient decremental disk revealing data structure: given two sets R and B of disks in the plane, we can delete disks from B, and upon each deletion, we receive a list of all disks in R that no longer intersect the union of B. Using this data structure, we get decremental data structures with a query time of \(O(\log n/\log \log n)\) that supports deletions in \(O(n\log \Psi \log ^{4} n)\) overall expected time for disk graphs with bounded radius ratio \(\Psi \) and \(O(n\log ^{5} n)\) overall expected time for disk graphs with arbitrary radii, assuming that the deletion sequence is oblivious of the internal random choices of the data structures.

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