On the convergence of inexact augmented Lagrangian methods for problems with convex constraints

被引：2

作者：

Galvan, Giulio ^{[1
]}

Lapucci, Matteo ^{[1
]}

机构：

[1] Univ Firenze, DINFO, Via Santa Marta 3, I-50139 Florence, Italy

来源：

OPERATIONS RESEARCH LETTERS | 2019年 / 47卷 / 03期

关键词：

Augmented Lagrangian method; Nonlinear programming; Global convergence; Convex constraints;

D O I：

10.1016/j.orl.2019.03.006

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

We consider the augmented Lagrangian method (ALM) for constrained optimization problems in the presence of convex inequality and convex abstract constraints. We focus on the case where the Lagrangian sub-problems are solved up to approximate stationary points, with increasing accuracy. We analyze two different criteria of approximate stationarity for the sub-problems and we prove the global convergence to stationary points of ALM in both cases. (C) 2019 Elsevier B.V. All rights reserved.

引用

页码：185 / 189

页数：5

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