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Analysis of distributed ADMM algorithm for consensus optimization in presence of error

Majzoobi, Layla and Lahouti, Farshad (2016) Analysis of distributed ADMM algorithm for consensus optimization in presence of error. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016. IEEE , Piscataway, NJ, pp. 4831-4835. ISBN 978-1-4799-9988-0. https://resolver.caltech.edu/CaltechAUTHORS:20170111-144126288

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Abstract

ADMM is a popular algorithm for solving convex optimization problems. Applying this algorithm to distributed consensus optimization problem results in a fully distributed iterative solution which relies on processing at the nodes and communication between neighbors. Local computations usually suffer from different types of errors, due to e.g., observation or quantization noise, which can degrade the performance of the algorithm. In this work, we focus on analyzing the convergence behavior of distributed ADMM for consensus optimization in presence of additive node error. We specifically show that (a noisy) ADMM converges linearly under certain conditions and also examine the associated convergence point. Numerical results are provided which demonstrate the effectiveness of the presented analysis.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1109/ICASSP.2016.7472595DOIArticle
CaltechAUTHORS:20190208-083449991Related ItemJournal Article
https://arxiv.org/abs/1901.02436arXivDiscussion Paper
Additional Information:© 2016 IEEE. Date Added to IEEE Xplore: 19 May 2016.
Subject Keywords:ADMM algorithm, Consensus optimization, Convergence
Record Number:CaltechAUTHORS:20170111-144126288
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20170111-144126288
Official Citation:L. Majzoobi and F. Lahouti, "Analysis of distributed ADMM algorithm for consensus optimization in presence of error," 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, 2016, pp. 4831-4835. doi: 10.1109/ICASSP.2016.7472595
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:73442
Collection:CaltechAUTHORS
Deposited By: Ruth Sustaita
Deposited On:20 Jan 2017 07:05
Last Modified:03 Oct 2019 16:27

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