Distributed Optimization with Local Domains: Applications in MPC and Network Flows

João F. C. Mota, João M. F. Xavier, Pedro M. Q. Aguiar, Markus Püschel

Research output: Contribution to journalArticlepeer-review

69 Citations (Scopus)


We consider a network where each node has exclusive access to a local cost function. Our contribution is a communication-efficient distributed algorithm that finds a vector star minimizing the sum of all the functions. We make the additional assumption that the functions have intersecting local domains, i.e., each function depends only on some components of the variable. Consequently, each node is interested in knowing only some components of star, not the entire vector. This allows improving communication-efficiency. We apply our algorithm to distributed model predictive control (D-MPC) and to network flow problems and show, through experiments on large networks, that the proposed algorithm requires less communications to converge than prior state-of-the-art algorithms.

Original languageEnglish
Article number6939619
Pages (from-to)2004-2009
Number of pages6
JournalIEEE Transactions on Automatic Control
Issue number7
Publication statusPublished - Jul 2015


  • alternating direction method of multipliers (ADMM)
  • Distributed algorithms
  • model predictive control
  • network flows

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications


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