Leveraging parameterized Chernoff bounds for simplified algorithm analyses

IF 0.7 4区 计算机科学 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Michael Dillencourt , Michael T. Goodrich , Michael Mitzenmacher
{"title":"Leveraging parameterized Chernoff bounds for simplified algorithm analyses","authors":"Michael Dillencourt ,&nbsp;Michael T. Goodrich ,&nbsp;Michael Mitzenmacher","doi":"10.1016/j.ipl.2024.106516","DOIUrl":null,"url":null,"abstract":"<div><p>In this paper, we derive parameterized Chernoff bounds and show their applications for simplifying the analysis of some well-known probabilistic algorithms and data structures. The parameterized Chernoff bounds we provide give probability bounds that are powers of two, with a clean formulation of the relation between the constant in the exponent and the relative distance from the mean. In addition, we provide new simplified analyses with these bounds for hash tables, randomized routing, and a simplified, non-recursive adaptation of the Floyd-Rivest selection algorithm.</p></div>","PeriodicalId":56290,"journal":{"name":"Information Processing Letters","volume":"187 ","pages":"Article 106516"},"PeriodicalIF":0.7000,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0020019024000462/pdfft?md5=67dfff867dbb54118accce695281b16f&pid=1-s2.0-S0020019024000462-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processing Letters","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0020019024000462","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

In this paper, we derive parameterized Chernoff bounds and show their applications for simplifying the analysis of some well-known probabilistic algorithms and data structures. The parameterized Chernoff bounds we provide give probability bounds that are powers of two, with a clean formulation of the relation between the constant in the exponent and the relative distance from the mean. In addition, we provide new simplified analyses with these bounds for hash tables, randomized routing, and a simplified, non-recursive adaptation of the Floyd-Rivest selection algorithm.

利用参数化切尔诺夫边界简化算法分析
在本文中,我们推导了参数化的切尔诺夫边界,并展示了它们在简化一些著名概率算法和数据结构分析中的应用。我们提供的参数化切尔诺夫边界给出了二的幂的概率边界,并对指数中的常数和与均值的相对距离之间的关系进行了简洁的表述。此外,我们还利用这些边界为散列表、随机路由和简化的非递归 Floyd-Rivest 选择算法提供了新的简化分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Information Processing Letters
Information Processing Letters 工程技术-计算机:信息系统
CiteScore
1.80
自引率
0.00%
发文量
70
审稿时长
7.3 months
期刊介绍: Information Processing Letters invites submission of original research articles that focus on fundamental aspects of information processing and computing. This naturally includes work in the broadly understood field of theoretical computer science; although papers in all areas of scientific inquiry will be given consideration, provided that they describe research contributions credibly motivated by applications to computing and involve rigorous methodology. High quality experimental papers that address topics of sufficiently broad interest may also be considered. Since its inception in 1971, Information Processing Letters has served as a forum for timely dissemination of short, concise and focused research contributions. Continuing with this tradition, and to expedite the reviewing process, manuscripts are generally limited in length to nine pages when they appear in print.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信