Distributed alternating direction method of multipliers

Ermin Wei*, Asuman Ozdaglar

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

191 Scopus citations


We consider a network of agents that are cooperatively solving a global unconstrained optimization problem, where the objective function is the sum of privately known local objective functions of the agents. Recent literature on distributed optimization methods for solving this problem focused on subgradient based methods, which typically converge at the rate equation, where k is the number of iterations. In this paper, we introduce a new distributed optimization algorithm based on Alternating Direction Method of Multipliers (ADMM), which is a classical method for sequentially decomposing optimization problems with coupled constraints. We show that this algorithm converges at the rate equation.

Original languageEnglish (US)
Article number6425904
Pages (from-to)5445-5450
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
StatePublished - Dec 1 2012
Event51st IEEE Conference on Decision and Control, CDC 2012 - Maui, HI, United States
Duration: Dec 10 2012Dec 13 2012

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

Fingerprint Dive into the research topics of 'Distributed alternating direction method of multipliers'. Together they form a unique fingerprint.

Cite this