An augmented Lagrangian method for distributed optimization

Nikolaos Chatzipanagiotis, Darinka Dentcheva, Michael M. Zavlanos

Research output: Contribution to journalArticlepeer-review

94 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)405-434
Number of pages30
JournalMathematical Programming
Volume152
Issue number1-2
DOIs
StatePublished - 24 Aug 2015

Keywords

  • Alternating direction method
  • Convex optimization
  • Diagonal quadratic approximation
  • Monotropic programming
  • Network optimization
  • Stochastic programming

Fingerprint

Dive into the research topics of 'An augmented Lagrangian method for distributed optimization'. Together they form a unique fingerprint.

Cite this