{"title":"Some examples related to duality theorem in linear programming","authors":"Michio Yoshida","doi":"10.32917/HMJ/1206139187","DOIUrl":null,"url":null,"abstract":"The duality problems in linear programming may read as follows. Suppose an m x n matrix A —{aij), a column vector 6 = (όi, • •-, bm) and a row vector c — (ci, , cn) are given. The primal problem: Find a column vector u = (uu •••,un) which maximizes the linear form cu subject to the conditions Au<,b and M^>0. The dual problem: Find a row vector v — (vu ,vn) which minimizes the linear form vb subject to the conditions vA^>c and v^>0. In each problem a vector satisfying the required conditions is called feasible, and if it attains the maximum or minimum it is called optimal. These problems can be represented by the following tableau:","PeriodicalId":17080,"journal":{"name":"Journal of science of the Hiroshima University Ser. A Mathematics, physics, chemistry","volume":"42 1","pages":"41-43"},"PeriodicalIF":0.0000,"publicationDate":"1966-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of science of the Hiroshima University Ser. A Mathematics, physics, chemistry","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32917/HMJ/1206139187","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
The duality problems in linear programming may read as follows. Suppose an m x n matrix A —{aij), a column vector 6 = (όi, • •-, bm) and a row vector c — (ci, , cn) are given. The primal problem: Find a column vector u = (uu •••,un) which maximizes the linear form cu subject to the conditions Au<,b and M^>0. The dual problem: Find a row vector v — (vu ,vn) which minimizes the linear form vb subject to the conditions vA^>c and v^>0. In each problem a vector satisfying the required conditions is called feasible, and if it attains the maximum or minimum it is called optimal. These problems can be represented by the following tableau: