{"title":"紧凑稀疏矩阵表示的数据结构","authors":"P. Di Felice, A. Agnifili, E. Clementini","doi":"10.1016/0141-1195(89)90064-8","DOIUrl":null,"url":null,"abstract":"<div><p>It is frequently necessary to manipulate large sparse matrices by means of a computer. In such cases a lot of CPU time and memory space can be saved if only the non-zero elements are stored. This paper surveys seven different “compact” representations of sparse matrices. The selected implementations will be compared with regard to the running time and the storage requirement.</p></div>","PeriodicalId":100043,"journal":{"name":"Advances in Engineering Software (1978)","volume":"11 2","pages":"Pages 75-83"},"PeriodicalIF":0.0000,"publicationDate":"1989-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0141-1195(89)90064-8","citationCount":"10","resultStr":"{\"title\":\"Data structures for compact sparse matrices representation\",\"authors\":\"P. Di Felice, A. Agnifili, E. Clementini\",\"doi\":\"10.1016/0141-1195(89)90064-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>It is frequently necessary to manipulate large sparse matrices by means of a computer. In such cases a lot of CPU time and memory space can be saved if only the non-zero elements are stored. This paper surveys seven different “compact” representations of sparse matrices. The selected implementations will be compared with regard to the running time and the storage requirement.</p></div>\",\"PeriodicalId\":100043,\"journal\":{\"name\":\"Advances in Engineering Software (1978)\",\"volume\":\"11 2\",\"pages\":\"Pages 75-83\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1989-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/0141-1195(89)90064-8\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Engineering Software (1978)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/0141119589900648\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Engineering Software (1978)","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/0141119589900648","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Data structures for compact sparse matrices representation
It is frequently necessary to manipulate large sparse matrices by means of a computer. In such cases a lot of CPU time and memory space can be saved if only the non-zero elements are stored. This paper surveys seven different “compact” representations of sparse matrices. The selected implementations will be compared with regard to the running time and the storage requirement.
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