Rachel Friederich, Matthew Graham, Anirban Ghosh, Brian Hicks, Ronald Shevchenko
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引用次数: 4
摘要
给定平面上的n个点,单位磁盘覆盖(Unit Disk Cover, UDC)问题要求计算覆盖这些点所需的最小单位磁盘数量,以及磁盘的位置。这个问题是np困难的,在过去的三十年里已经设计了几种近似算法。在本文中,我们设计并实验比较了其中一些算法在大量点集上的实际性能。目的是研究哪些算法在实践中运行速度快并给出良好的近似。我们为UDC提供了一个简单的7近似算法,该算法在O (n)个预期时间内运行,并使用O (s)个额外空间,其中s表示生成的覆盖的大小。在我们的实验中,它被证明是最快的。我们还提出了两种启发式方法来减少由它生成的覆盖的大小,而不会减慢它的速度。据我们所知,这是第一个实验比较几何覆盖算法的工作。使用大量的点集(数以百万计)对它们进行的实验揭示了它们的实际用途。我们通过GitHub 1分享工程算法,以便在几何优化领域进行更广泛的应用和未来的研究。
Experiments with Unit Disk Cover Algorithms for Covering Massive Pointsets
Given a set of n points in the plane, the Unit Disk Cover (UDC) problem asks to compute the minimum number of unit disks required to cover the points, along with a placement of the disks. The problem is NP-hard and several approximation algorithms have been designed over the last three decades. In this paper, we have engineered and experimentally compared practical performances of some of these algorithms on massive pointsets. The goal is to investigate which algorithms run fast and give good approximation in practice. We present a simple 7-approximation algorithm for UDC that runs in O ( n ) expected time and uses O ( s ) extra space, where s denotes the size of the generated cover. In our experiments, it turned out to be the speediest of all. We also present two heuristics to reduce the sizes of covers generated by it without slowing it down by much. To our knowledge, this is the first work that experimentally compares geometric covering algorithms. Experiments with them using massive pointsets (in the order of millions) throw light on their practical uses. We share the engineered algorithms via GitHub 1 for broader uses and future research in the domain of geometric optimization.