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

  • Mota J
  • Xavier J
  • Aguiar P
 et al. 
  • 32

    Readers

    Mendeley users who have this article in their library.
  • 15

    Citations

    Citations of this article.

Abstract

In this paper we consider a network with $P$ nodes, where each node has exclusive access to a local cost function. Our contribution is a communication-efficient distributed algorithm that finds a vector $x^\star$ 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 $x^\star$, not the entire vector. This allows for improvement in communication-efficiency. We apply our algorithm to model predictive control (MPC) and to network flow problems and show, through experiments on large networks, that our proposed algorithm requires less communications to converge than prior algorithms.

Author-supplied keywords

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

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Authors

  • Joao F.C. Mota

  • Joao M.F. Xavier

  • Pedro M.Q. Aguiar

  • Markus Puschel

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free