A Distributed and Scalable Processing Method Based Upon ADMM

Author

Erseghe, Tomaso

Author_Institution

Dipt. di Ing. dell´´Inf., Univ. di Padova, Padova, Italy

Volume

19

Issue

9

fYear

2012

Firstpage

563

Lastpage

566

Abstract

The alternating direction multiplier method (ADMM) was originally devised as an iterative method for solving convex minimization problems by means of parallelization, and was recently used for distributed processing. This letter proposes a modification of state-of-the-art ADMM formulations in order to obtain a scalable version, well suited for a wide range of applications such as cooperative localization and smart grid optimizations. The resulting algorithm is distributed and scalable, it assures fast convergence speed and robustness to errors. Its performance is tested with an application example based upon cooperative localization.

Keywords

convex programming; iterative methods; minimisation; alternating direction multiplier method; convex minimization problems; cooperative localization; distributed processing method; iterative method; scalable processing method; smart grid optimizations; state-of-the-art ADMM formulations; Convergence; Nickel; Optimization; Signal processing; Symmetric matrices; Vectors; Wireless networks; Alternating direction method of multipliers; cooperative localization; distributed processing; optimal power flow; scalable algorithms;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/LSP.2012.2207719

Filename

6236008