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

Title:

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

Creator:

Wang, Yinghui and Cheng, Songsong

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:98411233-3ebf-4a7c-804c-a6ddb08e0cf6
uuid:98411233-3ebf-4a7c-804c-a6ddb08e0cf6
doi:10.14736/kyb-2021-2-0256

Subject:

multi-agent system, sublinear convergence, stochastic mirror descent algorithm, and distributed computation of matrix equation

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

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.

Language:

English

Rights:

http://creativecommons.org/licenses/by-nc-sa/4.0/
policy:public

Coverage:

256-271

Source:

Kybernetika | 2021 Volume:57 | Number:2

Harvested from:

CDK

Metadata only:

false