A penalty ADMM with quantized communication for distributed optimization over multi-agent systems

Chenyang Liu; Xiaohua Dou; Yuan Fan; Songsong Cheng

Kybernetika (2023)

  • Volume: 59, Issue: 3, page 392-417
  • ISSN: 0023-5954

Abstract

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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 ( 1 k ) convergence rate for general convex objective functions, and with an R-linear rate for strongly convex objective functions.

How to cite

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Liu, Chenyang, et al. "A penalty ADMM with quantized communication for distributed optimization over multi-agent systems." Kybernetika 59.3 (2023): 392-417. <http://eudml.org/doc/299536>.

@article{Liu2023,
abstract = {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.},
author = {Liu, Chenyang, Dou, Xiaohua, Fan, Yuan, Cheng, Songsong},
journal = {Kybernetika},
keywords = {quantized communication; distributed optimization; alternating direction method of multipliers (ADMM); constrained optimization},
language = {eng},
number = {3},
pages = {392-417},
publisher = {Institute of Information Theory and Automation AS CR},
title = {A penalty ADMM with quantized communication for distributed optimization over multi-agent systems},
url = {http://eudml.org/doc/299536},
volume = {59},
year = {2023},
}

TY - JOUR
AU - Liu, Chenyang
AU - Dou, Xiaohua
AU - Fan, Yuan
AU - Cheng, Songsong
TI - A penalty ADMM with quantized communication for distributed optimization over multi-agent systems
JO - Kybernetika
PY - 2023
PB - Institute of Information Theory and Automation AS CR
VL - 59
IS - 3
SP - 392
EP - 417
AB - 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.
LA - eng
KW - quantized communication; distributed optimization; alternating direction method of multipliers (ADMM); constrained optimization
UR - http://eudml.org/doc/299536
ER -

References

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