Distributed optimization for multi-agent system over unbalanced graphs with linear convergence rate

Songsong Cheng; Shu Liang

Kybernetika (2020)

  • Volume: 56, Issue: 3, page 559-577
  • ISSN: 0023-5954

Abstract

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Distributed optimization over unbalanced graphs is an important problem in multi-agent systems. Most of literatures, by introducing some auxiliary variables, utilize the Push-Sum scheme to handle the widespread unbalance graph with row or column stochastic matrix only. But the introduced auxiliary dynamics bring more calculation and communication tasks. In this paper, based on the in-degree and out-degree information of each agent, we propose an innovative distributed optimization algorithm to reduce the calculation and communication complexity of the conventional Push-Sum scheme. Furthermore, with the aid of small gain theory, we prove the linear convergence rate of the proposed algorithm.

How to cite

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Cheng, Songsong, and Liang, Shu. "Distributed optimization for multi-agent system over unbalanced graphs with linear convergence rate." Kybernetika 56.3 (2020): 559-577. <http://eudml.org/doc/297233>.

@article{Cheng2020,
abstract = {Distributed optimization over unbalanced graphs is an important problem in multi-agent systems. Most of literatures, by introducing some auxiliary variables, utilize the Push-Sum scheme to handle the widespread unbalance graph with row or column stochastic matrix only. But the introduced auxiliary dynamics bring more calculation and communication tasks. In this paper, based on the in-degree and out-degree information of each agent, we propose an innovative distributed optimization algorithm to reduce the calculation and communication complexity of the conventional Push-Sum scheme. Furthermore, with the aid of small gain theory, we prove the linear convergence rate of the proposed algorithm.},
author = {Cheng, Songsong, Liang, Shu},
journal = {Kybernetika},
keywords = {multi-agent systems; distributed optimization; unbalanced graph; small gain theory; linear convergence rate},
language = {eng},
number = {3},
pages = {559-577},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Distributed optimization for multi-agent system over unbalanced graphs with linear convergence rate},
url = {http://eudml.org/doc/297233},
volume = {56},
year = {2020},
}

TY - JOUR
AU - Cheng, Songsong
AU - Liang, Shu
TI - Distributed optimization for multi-agent system over unbalanced graphs with linear convergence rate
JO - Kybernetika
PY - 2020
PB - Institute of Information Theory and Automation AS CR
VL - 56
IS - 3
SP - 559
EP - 577
AB - Distributed optimization over unbalanced graphs is an important problem in multi-agent systems. Most of literatures, by introducing some auxiliary variables, utilize the Push-Sum scheme to handle the widespread unbalance graph with row or column stochastic matrix only. But the introduced auxiliary dynamics bring more calculation and communication tasks. In this paper, based on the in-degree and out-degree information of each agent, we propose an innovative distributed optimization algorithm to reduce the calculation and communication complexity of the conventional Push-Sum scheme. Furthermore, with the aid of small gain theory, we prove the linear convergence rate of the proposed algorithm.
LA - eng
KW - multi-agent systems; distributed optimization; unbalanced graph; small gain theory; linear convergence rate
UR - http://eudml.org/doc/297233
ER -

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Citations in EuDML Documents

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  1. Kui Zhu, Yichen Zhang, Yutao Tang, Distributed optimization with inexact oracle
  2. Yinghui Wang, Songsong Cheng, A stochastic mirror-descent algorithm for solving A X B = C over an multi-agent system
  3. Ziqin Chen, Shu Liang, Distributed aggregative optimization with quantized communication
  4. Xianlin Zeng, Lihua Dou, Jinqiang Cui, Distributed accelerated Nash equilibrium learning for two-subnetwork zero-sum game with bilinear coupling
  5. Yikun Zeng, Yiheng Wei, Shuaiyu Zhou, Dongdong Yue, Distributed optimization via active disturbance rejection control: A nabla fractional design

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