Distributed and communication-efficient solutions to linear equations with special sparse structure

In this paper, two distributed and communication-efficient algorithms based on the multi-agent system are proposed to solve a system of linear equations with the Laplacian sparse system matrix. One algorithm is based on the gradient descent method in optimization. In this algorithm, the agents only share partial information instead of all of their collective state vectors to save significant communication. The other algorithm is obtained by approximating Newton's method for a faster convergence rate. Although it requires twice as much communication as the first one, it is still communication-efficient given the low dimension of the information shared among agents. The convergence at a linear rate is proved for both algorithms, and a comprehensive comparison of their convergence rate, communication burden, and computation costs is also performed. The proposed algorithms can be applied to various systems to solve those problems that can be modeled as a system of linear equations with a Laplacian sparse system matrix. Simulation results with the electric power system illustrate their effectiveness. (C)& nbsp;2021 Published by Elsevier B.V.

Automated summary

Two distributed and communication-efficient algorithms based on the multi-agent system are proposed in this paper to solve a system of linear equations with the Laplacian sparse system matrix. One algorithm is based on the gradient descent method in optimization, while the other algorithm approximates Newton's method for a faster convergence rate. Both algorithms demonstrate linear convergence rates, with considerations given to communication burden and computation costs.