Incremental Updates of Generalized Hypertree Decompositions

Q2 Mathematics Journal of Experimental Algorithmics Pub Date : 2022-09-21 DOI:10.1145/3578266
G. Gottlob, Matthias Lanzinger, David M Longo, Cem Okulmus
{"title":"Incremental Updates of Generalized Hypertree Decompositions","authors":"G. Gottlob, Matthias Lanzinger, David M Longo, Cem Okulmus","doi":"10.1145/3578266","DOIUrl":null,"url":null,"abstract":"Structural decomposition methods, such as generalized hypertree decompositions, have been successfully used for solving constraint satisfaction problems (CSPs). As decompositions can be reused to solve CSPs with the same constraint scopes, investing resources in computing good decompositions is beneficial, even though the computation itself is hard. Unfortunately, current methods need to compute a completely new decomposition, even if the scopes change only slightly. In this article, we make the first steps toward solving the problem of updating the decomposition of a CSP P so that it becomes a valid decomposition of a new CSP P' produced by some modification of P. Even though the problem is hard in theory, we propose and implement a framework for effectively updating generalized hypertree decompositions. The experimental evaluation of our algorithm strongly suggests practical applicability.","PeriodicalId":53707,"journal":{"name":"Journal of Experimental Algorithmics","volume":" ","pages":"1 - 28"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Experimental Algorithmics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3578266","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Mathematics","Score":null,"Total":0}
引用次数: 0

Abstract

Structural decomposition methods, such as generalized hypertree decompositions, have been successfully used for solving constraint satisfaction problems (CSPs). As decompositions can be reused to solve CSPs with the same constraint scopes, investing resources in computing good decompositions is beneficial, even though the computation itself is hard. Unfortunately, current methods need to compute a completely new decomposition, even if the scopes change only slightly. In this article, we make the first steps toward solving the problem of updating the decomposition of a CSP P so that it becomes a valid decomposition of a new CSP P' produced by some modification of P. Even though the problem is hard in theory, we propose and implement a framework for effectively updating generalized hypertree decompositions. The experimental evaluation of our algorithm strongly suggests practical applicability.
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
广义超树分解的增量更新
结构分解方法,如广义超树分解,已成功地用于解决约束满足问题。由于分解可以重复使用来解决具有相同约束范围的CSP,因此在计算好的分解方面投入资源是有益的,即使计算本身很困难。不幸的是,当前的方法需要计算一个全新的分解,即使作用域只有轻微的变化。在本文中,我们朝着解决更新CSP P的分解的问题迈出了第一步,使其成为由P的一些修改产生的新CSP P’的有效分解。尽管这个问题在理论上很难解决,但我们提出并实现了一个有效更新广义超树分解的框架。对我们算法的实验评估有力地表明了它的实用性。
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 去求助
来源期刊
Journal of Experimental Algorithmics
Journal of Experimental Algorithmics Mathematics-Theoretical Computer Science
CiteScore
3.10
自引率
0.00%
发文量
29
期刊介绍: The ACM JEA is a high-quality, refereed, archival journal devoted to the study of discrete algorithms and data structures through a combination of experimentation and classical analysis and design techniques. It focuses on the following areas in algorithms and data structures: ■combinatorial optimization ■computational biology ■computational geometry ■graph manipulation ■graphics ■heuristics ■network design ■parallel processing ■routing and scheduling ■searching and sorting ■VLSI design
期刊最新文献
Random projections for Linear Programming: an improved retrieval phase SAT-Boosted Tabu Search for Coloring Massive Graphs An Experimental Evaluation of Semidefinite Programming and Spectral Algorithms for Max Cut A constructive heuristic for the uniform capacitated vertex k-center problem Algorithms for Efficiently Computing Structural Anonymity in Complex Networks
×
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