Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Journal articles
  4. An Inertial Parallel and Asynchronous Forward-Backward Iteration for Distributed Convex Optimization
 
research article

An Inertial Parallel and Asynchronous Forward-Backward Iteration for Distributed Convex Optimization

Stathopoulos, Giorgos
•
Jones, Colin N.  
September 1, 2019
Journal Of Optimization Theory And Applications

Two characteristics that make convex decomposition algorithms attractive are simplicity of operations and generation of parallelizable structures. In principle, these schemes require that all coordinates update at the same time, i.e., they are synchronous by construction. Introducing asynchronicity in the updates can resolve several issues that appear in the synchronous case, like load imbalances in the computations or failing communication links. However, and to the best of our knowledge, there are no instances of asynchronous versions of commonly known algorithms combined with inertial acceleration techniques. In this work, we propose an inertial asynchronous and parallel fixed-point iteration, from which several new versions of existing convex optimization algorithms emanate. Departing from the norm that the frequency of the coordinates' updates should comply to some prior distribution, we propose a scheme, where the only requirement is that the coordinates update within a bounded interval. We prove convergence of the sequence of iterates generated by the scheme at a linear rate. One instance of the proposed scheme is implemented to solve a distributed optimization load sharing problem in a smart grid setting, and its superiority with respect to the nonaccelerated version is illustrated.

  • Details
  • Metrics
Type
research article
DOI
10.1007/s10957-019-01542-7
Web of Science ID

WOS:000475949900012

Author(s)
Stathopoulos, Giorgos
Jones, Colin N.  
Date Issued

2019-09-01

Publisher

SPRINGER/PLENUM PUBLISHERS

Published in
Journal Of Optimization Theory And Applications
Volume

182

Issue

3

Start page

1088

End page

1119

Subjects

Operations Research & Management Science

•

Mathematics, Applied

•

Mathematics

•

asynchronous optimization

•

convex optimization

•

proximal operator

•

multi-agent systems

•

smart grid

•

maximal monotone-operators

•

proximal method

•

convergence

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LA  
Available on Infoscience
August 2, 2019
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/159501
Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés