Global Optimization Method for Linear Multiplicative Programming

被引：4
|
作者：
Xue-gang ZHOU [1 ,2 ]
Bing-yuan CAO [1 ]
Kun WU [3 ]
机构：
[1] School of Mathematics and Information Science, Key Laboratory of Mathematics and Interdisciplinary Sciences of Guangdong, Higher Education Institutes, Guangzhou University
[2] Department of Applied Mathematics, Guangdong University of Finance
[3] School of Mathematical Science and Computing Technology, Center South University
来源：
Acta Mathematicae Applicatae Sinica | 2015年 / 31卷 / 02期
基金：
中国博士后科学基金;
关键词：
linear multiplicative programming; global optimization; linear programming; branch and bound;
D O I：
暂无
中图分类号：
O221 [规划论（数学规划）];
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.
引用
收藏
页码：325 / 334
页数：10
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