{"title":"利用超图划分高效布尔可满足性","authors":"V. Durairaj, P. Kalla","doi":"10.1109/HLDVT.2004.1431257","DOIUrl":null,"url":null,"abstract":"This paper presents hypergraph partitioning based constraint decomposition procedures to guide Boolean satisfiability search. Variable-constraint relationships are modeled on a hypergraph and partitioning based techniques are employed to decompose the constraints. Subsequently, the decomposition is analyzed to solve the CNF-SAT problem efficiently. The contributions of this research are two-fold: 1) to engineer a constraint decomposition technique using hypergraph partitioning; 2) to engineer a constraint resolution method based on this decomposition. Preliminary experiments show that our approach is fast, scalable and can significantly increase the performance (often orders of magnitude) of the SAT engine.","PeriodicalId":240214,"journal":{"name":"Proceedings. Ninth IEEE International High-Level Design Validation and Test Workshop (IEEE Cat. No.04EX940)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Exploiting hypergraph partitioning for efficient Boolean satisfiability\",\"authors\":\"V. Durairaj, P. Kalla\",\"doi\":\"10.1109/HLDVT.2004.1431257\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents hypergraph partitioning based constraint decomposition procedures to guide Boolean satisfiability search. Variable-constraint relationships are modeled on a hypergraph and partitioning based techniques are employed to decompose the constraints. Subsequently, the decomposition is analyzed to solve the CNF-SAT problem efficiently. The contributions of this research are two-fold: 1) to engineer a constraint decomposition technique using hypergraph partitioning; 2) to engineer a constraint resolution method based on this decomposition. Preliminary experiments show that our approach is fast, scalable and can significantly increase the performance (often orders of magnitude) of the SAT engine.\",\"PeriodicalId\":240214,\"journal\":{\"name\":\"Proceedings. Ninth IEEE International High-Level Design Validation and Test Workshop (IEEE Cat. No.04EX940)\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. Ninth IEEE International High-Level Design Validation and Test Workshop (IEEE Cat. No.04EX940)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HLDVT.2004.1431257\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. Ninth IEEE International High-Level Design Validation and Test Workshop (IEEE Cat. No.04EX940)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HLDVT.2004.1431257","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Exploiting hypergraph partitioning for efficient Boolean satisfiability
This paper presents hypergraph partitioning based constraint decomposition procedures to guide Boolean satisfiability search. Variable-constraint relationships are modeled on a hypergraph and partitioning based techniques are employed to decompose the constraints. Subsequently, the decomposition is analyzed to solve the CNF-SAT problem efficiently. The contributions of this research are two-fold: 1) to engineer a constraint decomposition technique using hypergraph partitioning; 2) to engineer a constraint resolution method based on this decomposition. Preliminary experiments show that our approach is fast, scalable and can significantly increase the performance (often orders of magnitude) of the SAT engine.