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

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...

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Vydáno v:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 4831 - 4835
Hlavní autoři: Majzoobi, Layla, Lahouti, Farshad
Médium: Konferenční příspěvek Journal Article
Jazyk:angličtina
Vydáno: IEEE 01.03.2016
Témata:
ADMM algorithm
Algorithm design and analysis
Algorithms
Consensus optimization
Convergence
Degradation
Electrical engineering
Electronics
Error analysis
Inference algorithms
Mathematical models
Noise measurement
Optimization
Quantization
Quantization (signal)
ISSN:2379-190X
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Shrnutí: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.
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SourceType-Conference Papers & Proceedings-2
ISSN:2379-190X
DOI:10.1109/ICASSP.2016.7472595