Approximate ADMM algorithms derived from Lagrangian splitting

Article

DOI: 10.1007/s10589-017-9911-z

Cite this article as:
Eckstein, J. & Yao, W. Comput Optim Appl (2017). doi:10.1007/s10589-017-9911-z
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Abstract

This paper presents two new approximate versions of the alternating direction method of multipliers (ADMM) derived by modifying of the original “Lagrangian splitting” convergence analysis of Fortin and Glowinski. They require neither strong convexity of the objective function nor any restrictions on the coupling matrix. The first method uses an absolutely summable error criterion and resembles methods that may readily be derived from earlier work on the relationship between the ADMM and the proximal point method, but without any need for restrictive assumptions to make it practically implementable. It permits both subproblems to be solved inexactly. The second method uses a relative error criterion and the same kind of auxiliary iterate sequence that has recently been proposed to enable relative-error approximate implementation of non-decomposition augmented Lagrangian algorithms. It also allows both subproblems to be solved inexactly, although ruling out “jamming” behavior requires a somewhat complicated implementation. The convergence analyses of the two methods share extensive underlying elements.

Keywords

Alternating direction method of multipliers Convex programming Decomposition methods 

Mathematics Subject Classification

90C25 49M27 

Funding information

Funder NameGrant NumberFunding Note
Division of Computing and Communication Foundations
  • CCF-1115638

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Management Science and Information Systems and RUTCORRutgers UniversityPiscatawayUSA
  2. 2.RUTCORRutgers UniversityPiscatawayUSA

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