Global optimization method for linear multiplicative programming

被引：0
|
作者：
Xue-gang Zhou
Bing-yuan Cao
Kun Wu
机构：
[1] Guangzhou University,School of Mathematics and Information Science, Key Laboratory of Mathematics and Interdisciplinary Sciences of Guangdong, Higher Education Institutes
[2] Guangdong University of Finance,Department of Applied Mathematics
[3] Center South University,School of Mathematical Science and Computing Technology
来源：
Acta Mathematicae Applicatae Sinica, English Series | 2015年 / 31卷
关键词：
linear multiplicative programming; global optimization; linear programming; branch and bound; 90C26; 90C30;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, a new global algorithm is presented to globally solve the linear multiplicative programming (LMP). The problem (LMP) is firstly converted into an equivalent programming problem (LMP (H)) by introducing p auxiliary variables. Then by exploiting structure of (LMP(H)), a linear relaxation programming (LP (H)) of (LMP (H)) is obtained with a problem (LMP) reduced to a sequence of linear programming problems. The algorithm is used to compute the lower bounds called the branch and bound search by solving linear relaxation programming problems (LP(H)). The proposed algorithm is proven that it is convergent to the global minimum through the solutions of a series of linear programming problems. Some examples are given to illustrate the feasibility of the proposed algorithm.
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页码：325 / 334
页数：9
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