A Deterministic Global Optimization Method for Solving Generalized Linear Multiplicative Programming Problem with Multiplicative Constraints

被引：0
|
作者：
Zhang, Bo [1 ]
Gao, Yue-lin [1 ]
机构：
[1] North Minzu Univ, Res Inst Informat & Syst Computat Sci, Yinchuan 750021, Peoples R China
来源：
2018 2ND INTERNATIONAL CONFERENCE ON APPLIED MATHEMATICS, MODELING AND SIMULATION (AMMS 2018) | 2018年 / 305卷
基金：
中国国家自然科学基金;
关键词：
Global optimization; Linear multiplicative programming; Linear relaxation; Branch and bound;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
This paper presents a deterministic global optimization algorithm for solving generalized linear multiplicative programming problem with multiplicative constraints (GLMP). By utilizing equivalent transformation and linear relaxation method, a linear relaxation programming (LRP) of equivalent problem (GLMPH) is established. In the algorithm, lower and upper bounds are simultaneously obtained by solving some linear relaxation programming problems (LRP). Global convergence has been proved and results of some sample examples and a small random experiment show that the proposed algorithm is feasible and efficient.
引用
收藏
页码：7 / 14
页数：8
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