An augmented Lagrangian method for distributed optimization


Journal Article

© 2014, Springer-Verlag Berlin Heidelberg and Mathematical Optimization Society. 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.

Full Text

Duke Authors

Cited Authors

  • Chatzipanagiotis, N; Dentcheva, D; Zavlanos, MM

Published Date

  • August 24, 2015

Published In

Volume / Issue

  • 152 / 1-2

Start / End Page

  • 405 - 434

Electronic International Standard Serial Number (EISSN)

  • 1436-4646

International Standard Serial Number (ISSN)

  • 0025-5610

Digital Object Identifier (DOI)

  • 10.1007/s10107-014-0808-7

Citation Source

  • Scopus