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Title: A penalty ADMM with quantized communication for distributed optimization over multi-agent systems (English)
Author: Liu, Chenyang
Author: Dou, Xiaohua
Author: Fan, Yuan
Author: Cheng, Songsong
Language: English
Journal: Kybernetika
ISSN: 0023-5954 (print)
ISSN: 1805-949X (online)
Volume: 59
Issue: 3
Year: 2023
Pages: 392-417
Summary lang: English
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Category: math
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Summary: In this paper, we design a distributed penalty ADMM algorithm with quantized communication to solve distributed convex optimization problems over multi-agent systems. Firstly, we introduce a quantization scheme that reduces the bandwidth limitation of multi-agent systems without requiring an encoder or decoder, unlike existing quantized algorithms. This scheme also minimizes the computation burden. Moreover, with the aid of the quantization design, we propose a quantized penalty ADMM to obtain the suboptimal solution. Furthermore, the proposed algorithm converges to the suboptimal solution with an $O(\frac{1}{k})$ convergence rate for general convex objective functions, and with an R-linear rate for strongly convex objective functions. (English)
Keyword: quantized communication
Keyword: distributed optimization
Keyword: alternating direction method of multipliers (ADMM)
Keyword: constrained optimization
MSC: 90C33
DOI: 10.14736/kyb-2023-3-0392
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Date available: 2023-07-12T07:19:50Z
Last updated: 2023-07-12
Stable URL: http://hdl.handle.net/10338.dmlcz/151722
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