结合MaxSAT推理和增量上界的最大团问题

Chu Min Li, Zhiwen Fang, Ke Xu
{"title":"结合MaxSAT推理和增量上界的最大团问题","authors":"Chu Min Li, Zhiwen Fang, Ke Xu","doi":"10.1109/ICTAI.2013.143","DOIUrl":null,"url":null,"abstract":"Recently, MaxSAT reasoning has been shown to be powerful in computing upper bounds for the cardinality of a maximum clique of a graph. However, existing upper bounds based on MaxSAT reasoning have two drawbacks: (1)at every node of the search tree, MaxSAT reasoning has to be performed from scratch to compute an upper bound and is time-consuming, (2) due to the NP-hardness of the MaxSAT problem, MaxSAT reasoning generally cannot be complete at anode of a search tree, and may not give an upper bound tight enough for pruning search space. In this paper, we propose an incremental upper bound and combine it with MaxSAT reasoning to remedy the two drawbacks. The new approach is used to develop an efficient branch-and-bound algorithm for MaxClique, called IncMaxCLQ. We conduct experiments to show the complementarity of the incremental upper bound and MaxSAT reasoning and to compare IncMaxCLQ with several state-of-the-art algorithms for MaxClique.","PeriodicalId":140309,"journal":{"name":"2013 IEEE 25th International Conference on Tools with Artificial Intelligence","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"76","resultStr":"{\"title\":\"Combining MaxSAT Reasoning and Incremental Upper Bound for the Maximum Clique Problem\",\"authors\":\"Chu Min Li, Zhiwen Fang, Ke Xu\",\"doi\":\"10.1109/ICTAI.2013.143\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recently, MaxSAT reasoning has been shown to be powerful in computing upper bounds for the cardinality of a maximum clique of a graph. However, existing upper bounds based on MaxSAT reasoning have two drawbacks: (1)at every node of the search tree, MaxSAT reasoning has to be performed from scratch to compute an upper bound and is time-consuming, (2) due to the NP-hardness of the MaxSAT problem, MaxSAT reasoning generally cannot be complete at anode of a search tree, and may not give an upper bound tight enough for pruning search space. In this paper, we propose an incremental upper bound and combine it with MaxSAT reasoning to remedy the two drawbacks. The new approach is used to develop an efficient branch-and-bound algorithm for MaxClique, called IncMaxCLQ. We conduct experiments to show the complementarity of the incremental upper bound and MaxSAT reasoning and to compare IncMaxCLQ with several state-of-the-art algorithms for MaxClique.\",\"PeriodicalId\":140309,\"journal\":{\"name\":\"2013 IEEE 25th International Conference on Tools with Artificial Intelligence\",\"volume\":\"50 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"76\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE 25th International Conference on Tools with Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICTAI.2013.143\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE 25th International Conference on Tools with Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTAI.2013.143","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 76

摘要

最近,MaxSAT推理在计算图的最大团的基数上界方面被证明是强大的。然而,现有的基于MaxSAT推理的上界存在两个缺点:(1)在搜索树的每个节点上,MaxSAT推理都必须从头开始计算上界,并且非常耗时;(2)由于MaxSAT问题的np -硬度,MaxSAT推理通常不能在搜索树的正极完成,并且可能无法给出足够紧的上界来修剪搜索空间。在本文中,我们提出了一个增量上界,并将其与MaxSAT推理相结合,以弥补这两个缺点。该方法被用于开发一种高效的MaxClique分支定界算法,称为IncMaxCLQ。我们进行了实验来证明增量上界和MaxSAT推理的互补性,并将IncMaxCLQ与MaxClique的几种最先进算法进行了比较。
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
Combining MaxSAT Reasoning and Incremental Upper Bound for the Maximum Clique Problem
Recently, MaxSAT reasoning has been shown to be powerful in computing upper bounds for the cardinality of a maximum clique of a graph. However, existing upper bounds based on MaxSAT reasoning have two drawbacks: (1)at every node of the search tree, MaxSAT reasoning has to be performed from scratch to compute an upper bound and is time-consuming, (2) due to the NP-hardness of the MaxSAT problem, MaxSAT reasoning generally cannot be complete at anode of a search tree, and may not give an upper bound tight enough for pruning search space. In this paper, we propose an incremental upper bound and combine it with MaxSAT reasoning to remedy the two drawbacks. The new approach is used to develop an efficient branch-and-bound algorithm for MaxClique, called IncMaxCLQ. We conduct experiments to show the complementarity of the incremental upper bound and MaxSAT reasoning and to compare IncMaxCLQ with several state-of-the-art algorithms for MaxClique.
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
An Automatic Algorithm Selection Approach for Planning Learning Useful Macro-actions for Planning with N-Grams Optimizing Dynamic Ensemble Selection Procedure by Evolutionary Extreme Learning Machines and a Noise Reduction Filter Motion-Driven Action-Based Planning Assessing Procedural Knowledge in Free-Text Answers through a Hybrid Semantic Web Approach
×
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