A deterministic global optimization algorithm based on a linearizing method for nonconvex quadratically constrained programs

被引：5

作者：

Qu, Shao-Jian ^{[1
]}

Ji, Ying ^{[2
]}

Zhang, Ke-Cun ^{[2
]}

机构：

[1] Harbin Inst Technol, Harbin 150080, Peoples R China

[2] Xi An Jiao Tong Univ, Fac Sci, Xian 710049, Peoples R China

来源：

MATHEMATICAL AND COMPUTER MODELLING | 2008年 / 48卷 / 11-12期

基金：

美国国家科学基金会;

关键词：

NQP; Linearizing method; Branch and bound; Global Optimization;

D O I：

10.1016/j.mcm.2008.04.004

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this paper a deterministic global optimization algorithm for solving nonconvex quadratically constrained quadratic programs (NQP) is proposed. Utilizing a new linearizing method, the initial nonlinear and nonconvex NQP problem is reduced to a sequence of linear programming problems. The proposed algorithm is proven to be convergent to the global minimum through the solutions of a series of linear programming problems. Several NQP examples in the literatures are tested to demonstrate that the proposed method can systematically solve these examples to find the global optimum within a prespecified error. (C) 2008 Elsevier Ltd. All rights reserved.

引用

页码：1737 / 1743

页数：7

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