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

Yinghui Wang;  Songsong Cheng — 2021

Kybernetika

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,...

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

Songsong Cheng;  Shu Liang — 2020

Kybernetika

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...

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