Distributed algorithms to determine eigenvectors of matrices on spatially distributed networks
Signal Processing(2022)
Abstract
Eigenvectors of matrices on a network have been used for understanding influence of a vertex and spectral clustering. For matrices with small geodesic-width and their given eigenvalues, we propose preconditioned gradient descent algorithms in this paper to find eigenvectors. We also consider synchronous implementation of the proposed algorithms at vertex/agent level in a spatially distributed network in which each agent has limited data processing capability and confined communication range. (c) 2022 Elsevier B.V. All rights reserved.
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Key words
Eigenvector,Matrix on graphs,Preconditioned gradient descent algorithm,Spatially distributed network
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