发现排列生成算法的统一框架

IF 1.5 4区 计算机科学 Q4 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Computer Journal Pub Date : 2021-10-01 DOI:10.1093/comjnl/bxab181
Pramod Ganapathi;Rezaul Chowdhury
{"title":"发现排列生成算法的统一框架","authors":"Pramod Ganapathi;Rezaul Chowdhury","doi":"10.1093/comjnl/bxab181","DOIUrl":null,"url":null,"abstract":"We present two simple, intuitive and general algorithmic frameworks that can be used to design a wide variety of permutation generation algorithms. The frameworks can be used to produce 19 existing permutation algorithms, including the well-known algorithms of Heap, Wells, Langdon, Zaks, Tompkins and Lipski. We use the frameworks to design two new sorting-based permutation generation algorithms, one of which is optimal.","PeriodicalId":50641,"journal":{"name":"Computer Journal","volume":"66 3","pages":"603-614"},"PeriodicalIF":1.5000,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A Unified Framework to Discover Permutation Generation Algorithms\",\"authors\":\"Pramod Ganapathi;Rezaul Chowdhury\",\"doi\":\"10.1093/comjnl/bxab181\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present two simple, intuitive and general algorithmic frameworks that can be used to design a wide variety of permutation generation algorithms. The frameworks can be used to produce 19 existing permutation algorithms, including the well-known algorithms of Heap, Wells, Langdon, Zaks, Tompkins and Lipski. We use the frameworks to design two new sorting-based permutation generation algorithms, one of which is optimal.\",\"PeriodicalId\":50641,\"journal\":{\"name\":\"Computer Journal\",\"volume\":\"66 3\",\"pages\":\"603-614\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2021-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Journal\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10084362/\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Journal","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10084362/","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 2

摘要

我们提出了两个简单、直观和通用的算法框架,可用于设计各种排列生成算法。这些框架可以用来产生19种现有的排列算法,包括著名的Heap、Wells、Langdon、Zaks、Tompkins和Lipski算法。我们使用这些框架设计了两种新的基于排序的排列生成算法,其中一种是最优的。
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
A Unified Framework to Discover Permutation Generation Algorithms
We present two simple, intuitive and general algorithmic frameworks that can be used to design a wide variety of permutation generation algorithms. The frameworks can be used to produce 19 existing permutation algorithms, including the well-known algorithms of Heap, Wells, Langdon, Zaks, Tompkins and Lipski. We use the frameworks to design two new sorting-based permutation generation algorithms, one of which is optimal.
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
Computer Journal
Computer Journal 工程技术-计算机:软件工程
CiteScore
3.60
自引率
7.10%
发文量
164
审稿时长
4.8 months
期刊介绍: The Computer Journal is one of the longest-established journals serving all branches of the academic computer science community. It is currently published in four sections.
期刊最新文献
Correction to: Automatic Diagnosis of Diabetic Retinopathy from Retinal Abnormalities: Improved Jaya-Based Feature Selection and Recurrent Neural Network Eager Term Rewriting For The Fracterm Calculus Of Common Meadows An Intrusion Detection Method Based on Attention Mechanism to Improve CNN-BiLSTM Model Enhancing Auditory Brainstem Response Classification Based On Vision Transformer Leveraging Meta-Learning To Improve Unsupervised Domain Adaptation
×
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