Analysis of distributed ADMM algorithm for consensus optimization in presence of error
- Creators
- Majzoobi, Layla
- Lahouti, Farshad
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.
Additional Information
© 2016 IEEE. Date Added to IEEE Xplore: 19 May 2016.Attached Files
Submitted - 1901.02436v1.pdf
Files
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Additional details
- Eprint ID
- 73442
- DOI
- 10.1109/ICASSP.2016.7472595
- Resolver ID
- CaltechAUTHORS:20170111-144126288
- Created
-
2017-01-20Created from EPrint's datestamp field
- Updated
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2021-11-11Created from EPrint's last_modified field