Augmented Lagrange algorithms for distributed optimization over multi-agent networks via edge-based method

Augmented Lagrange algorithms for distributed optimization over multi-agent networks via... In this paper, the augmented Lagrange (AL) algorithm for distributed optimization is studied. Compared with the existing results, this paper uses different techniques, including the factorization of weighted Laplacian and the spectral decomposition technique, to prove the linear convergence of the AL algorithm, and simultaneously provides a novel description on the convergence rate. First, by using an important factorization of weighted Laplacian, it is proved that the linear convergence of the AL algorithm can be achieved via a simplified analysis procedure. Within this framework, a novel quantitative description on the convergence rate is then provided based on spectral decomposition technique. Meanwhile, by determining the monotonicity of an auxiliary function, a connection between convergence rate, step size and edge weights is established. Finally, simulation examples illustrate the theoretical results. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Automatica Elsevier

Augmented Lagrange algorithms for distributed optimization over multi-agent networks via edge-based method

Loading next page...
 
/lp/elsevier/augmented-lagrange-algorithms-for-distributed-optimization-over-multi-BAbFaYA82U
Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
0005-1098
D.O.I.
10.1016/j.automatica.2018.04.010
Publisher site
See Article on Publisher Site

Abstract

In this paper, the augmented Lagrange (AL) algorithm for distributed optimization is studied. Compared with the existing results, this paper uses different techniques, including the factorization of weighted Laplacian and the spectral decomposition technique, to prove the linear convergence of the AL algorithm, and simultaneously provides a novel description on the convergence rate. First, by using an important factorization of weighted Laplacian, it is proved that the linear convergence of the AL algorithm can be achieved via a simplified analysis procedure. Within this framework, a novel quantitative description on the convergence rate is then provided based on spectral decomposition technique. Meanwhile, by determining the monotonicity of an auxiliary function, a connection between convergence rate, step size and edge weights is established. Finally, simulation examples illustrate the theoretical results.

Journal

AutomaticaElsevier

Published: Aug 1, 2018

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off