A GLOBALLY CONVERGENT AUGMENTED LAGRANGIAN ALGORITHM FOR OPTIMIZATION WITH GENERAL CONSTRAINTS AND SIMPLE BOUNDS

被引：578
作者：
CONN, AR [1 ]
GOULD, NIM [1 ]
TOINT, PL [1 ]
机构：
[1] UKAEA,DIV COMP SCI & SYST,HARWELL OX11 0RA,BERKS,ENGLAND
来源：
SIAM JOURNAL ON NUMERICAL ANALYSIS | 1991年 / 28卷 / 02期
关键词：
CONSTRAINED OPTIMIZATION; AUGMENTED LAGRANGIAN; SIMPLE BOUNDS; GENERAL CONSTRAINTS;
D O I：
10.1137/0728030
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
The global and local convergence properties of a class of augmented Lagrangian methods for solving nonlinear programming problems are considered. In such methods, simple bound constraints are treated separately from more general constraints and the stopping rules for the inner minimization algorithm have this in mind. Global convergence is proved, and it is established that a potentially troublesome penalty parameter is bounded away from zero.
引用
收藏
页码：545 / 572
页数：28
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