{"title":"大规模核内线性规划的几个方面","authors":"James E. Kalan","doi":"10.1145/800184.810500","DOIUrl":null,"url":null,"abstract":"Unconventional methods for matricial compression indicate that large linear programming constraint matrices may comfortably remain core-resident during optimization. Minor changes in the computational aspects of the simplex algorithm coupled with efficient inverse matrix representation show that the major portion of the inverse in product form of a basis may be embedded in the constraint matrix. A method for generating a sparse inverse matrix is presented.","PeriodicalId":126192,"journal":{"name":"ACM '71","volume":"373 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"61","resultStr":"{\"title\":\"Aspects of large-scale in-core linear programming\",\"authors\":\"James E. Kalan\",\"doi\":\"10.1145/800184.810500\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Unconventional methods for matricial compression indicate that large linear programming constraint matrices may comfortably remain core-resident during optimization. Minor changes in the computational aspects of the simplex algorithm coupled with efficient inverse matrix representation show that the major portion of the inverse in product form of a basis may be embedded in the constraint matrix. A method for generating a sparse inverse matrix is presented.\",\"PeriodicalId\":126192,\"journal\":{\"name\":\"ACM '71\",\"volume\":\"373 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"61\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM '71\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/800184.810500\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM '71","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/800184.810500","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Unconventional methods for matricial compression indicate that large linear programming constraint matrices may comfortably remain core-resident during optimization. Minor changes in the computational aspects of the simplex algorithm coupled with efficient inverse matrix representation show that the major portion of the inverse in product form of a basis may be embedded in the constraint matrix. A method for generating a sparse inverse matrix is presented.
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