An augmented Lagrangian method for distributed optimization

被引：84

作者：

Chatzipanagiotis, Nikolaos ^{[1
]}

Dentcheva, Darinka ^{[2
]}

Zavlanos, Michael M. ^{[1
]}

机构：

[1] Duke Univ, Dept Mech Engn & Mat Sci, Durham, NC 27708 USA

[2] Stevens Inst Technol, Dept Math, Hoboken, NJ 07030 USA

来源：

MATHEMATICAL PROGRAMMING | 2015年 / 152卷 / 1-2期

基金：

美国国家科学基金会;

关键词：

Convex optimization; Monotropic programming; Alternating direction method; Network optimization; Diagonal quadratic approximation; Stochastic programming; DECOMPOSITION METHOD;

D O I：

10.1007/s10107-014-0808-7

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

We propose a novel distributed method for convex optimization problems with a certain separability structure. The method is based on the augmented Lagrangian framework. We analyze its convergence and provide an application to two network models, as well as to a two-stage stochastic optimization problem. The proposed method compares favorably to two augmented Lagrangian decomposition methods known in the literature, as well as to decomposition methods based on the ordinary Lagrangian function.

引用

页码：405 / 434

页数：30

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