A stochastic mirror-descent algorithm for solving A X B = C over an multi-agent system

Yinghui Wang; Songsong Cheng

Kybernetika (2021)

  • Issue: 2, page 256-271
  • ISSN: 0023-5954

Abstract

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In this paper, we consider a distributed stochastic computation of A X B = C with local set constraints over an multi-agent system, where each agent over the network only knows a few rows or columns of matrixes. Through formulating an equivalent distributed optimization problem for seeking least-squares solutions of A X B = C , we propose a distributed stochastic mirror-descent algorithm for solving the equivalent distributed problem. Then, we provide the sublinear convergence of the proposed algorithm. Moreover, a numerical example is also given to illustrate the effectiveness of the proposed algorithm.

How to cite

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Wang, Yinghui, and Cheng, Songsong. "A stochastic mirror-descent algorithm for solving $AXB=C$ over an multi-agent system." Kybernetika (2021): 256-271. <http://eudml.org/doc/297959>.

@article{Wang2021,
abstract = {In this paper, we consider a distributed stochastic computation of $AXB=C$ with local set constraints over an multi-agent system, where each agent over the network only knows a few rows or columns of matrixes. Through formulating an equivalent distributed optimization problem for seeking least-squares solutions of $AXB=C$, we propose a distributed stochastic mirror-descent algorithm for solving the equivalent distributed problem. Then, we provide the sublinear convergence of the proposed algorithm. Moreover, a numerical example is also given to illustrate the effectiveness of the proposed algorithm.},
author = {Wang, Yinghui, Cheng, Songsong},
journal = {Kybernetika},
keywords = {distributed computation of matrix equation; multi-agent system; sublinear convergence; stochastic mirror descent algorithm},
language = {eng},
number = {2},
pages = {256-271},
publisher = {Institute of Information Theory and Automation AS CR},
title = {A stochastic mirror-descent algorithm for solving $AXB=C$ over an multi-agent system},
url = {http://eudml.org/doc/297959},
year = {2021},
}

TY - JOUR
AU - Wang, Yinghui
AU - Cheng, Songsong
TI - A stochastic mirror-descent algorithm for solving $AXB=C$ over an multi-agent system
JO - Kybernetika
PY - 2021
PB - Institute of Information Theory and Automation AS CR
IS - 2
SP - 256
EP - 271
AB - In this paper, we consider a distributed stochastic computation of $AXB=C$ with local set constraints over an multi-agent system, where each agent over the network only knows a few rows or columns of matrixes. Through formulating an equivalent distributed optimization problem for seeking least-squares solutions of $AXB=C$, we propose a distributed stochastic mirror-descent algorithm for solving the equivalent distributed problem. Then, we provide the sublinear convergence of the proposed algorithm. Moreover, a numerical example is also given to illustrate the effectiveness of the proposed algorithm.
LA - eng
KW - distributed computation of matrix equation; multi-agent system; sublinear convergence; stochastic mirror descent algorithm
UR - http://eudml.org/doc/297959
ER -

References

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