An augmented Lagrangian method for distributed optimization

• 成果类型: Article
• 署名作者: Chatzipanagiotis, Nikolaos; Dentcheva, Darinka; Zavlanos, Michael M.
• 署名单位: Duke University; Stevens Institute of Technology
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-014-0808-7
• 发表日期: 2015
• 页码: 405-434
• 关键词: decomposition method

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.