覆盖大量点集的单位磁盘覆盖算法实验

Rachel Friederich, Matthew Graham, Anirban Ghosh, Brian Hicks, Ronald Shevchenko
{"title":"覆盖大量点集的单位磁盘覆盖算法实验","authors":"Rachel Friederich, Matthew Graham, Anirban Ghosh, Brian Hicks, Ronald Shevchenko","doi":"10.48550/arXiv.2205.01716","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":11245,"journal":{"name":"Discret. Comput. Geom.","volume":"6 1","pages":"101925"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Experiments with Unit Disk Cover Algorithms for Covering Massive Pointsets\",\"authors\":\"Rachel Friederich, Matthew Graham, Anirban Ghosh, Brian Hicks, Ronald Shevchenko\",\"doi\":\"10.48550/arXiv.2205.01716\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":11245,\"journal\":{\"name\":\"Discret. Comput. Geom.\",\"volume\":\"6 1\",\"pages\":\"101925\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Discret. Comput. Geom.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.48550/arXiv.2205.01716\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Discret. Comput. Geom.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.48550/arXiv.2205.01716","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
On Reverse Shortest Paths in Geometric Proximity Graphs Algorithms for Radius-Optimally Augmenting Trees in a Metric Space Augmenting Graphs to Minimize the Radius Linear-Time Approximation Scheme for k-Means Clustering of Axis-Parallel Affine Subspaces Intersecting Disks Using Two Congruent Disks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1