Global minimization using an Augmented Lagrangian method with variable lower-level constraints

被引：101

作者：

Birgin, E. G. ^{[1
]}

Floudas, C. A. ^{[2
]}

Martinez, J. M. ^{[3
]}

机构：

[1] Univ Sao Paulo, IME, Dept Comp Sci, BR-05508090 Sao Paulo, Brazil

[2] Princeton Univ, Dept Chem Engn, Princeton, NJ 08544 USA

[3] Univ Estadual Campinas, Dept Appl Math, IMECC, BR-13081970 Campinas, SP, Brazil

来源：

MATHEMATICAL PROGRAMMING | 2010年 / 125卷 / 01期

基金：

美国国家科学基金会; 巴西圣保罗研究基金会;

关键词：

OPTIMIZATION ALGORITHM GOP; RLT-BASED APPROACH; ALPHA-BB; NONCONVEX NLPS; DUALITY;

D O I：

10.1007/s10107-009-0264-y

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

A novel global optimization method based on an Augmented Lagrangian framework is introduced for continuous constrained nonlinear optimization problems. At each outer iteration k the method requires the epsilon(k)-global minimization of the Augmented Lagrangian with simple constraints, where epsilon(k) -> epsilon. Global convergence to an epsilon-global minimizer of the original problem is proved. The subproblems are solved using the alpha BB method. Numerical experiments are presented.

引用

页码：139 / 162

页数：24

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