A Two-Layer Distributed Algorithm Using Neurodynamic System for Solving L 1 -Minimization

This brief considers a distributed algorithm for solving [Formula Omitted]-minimization problem based on nonlinear neurodynamic system. Compared with centralized algorithms, distributed algorithms have great potential in data privacy protection, distributed storage and processing of data. In this br...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems. II, Express briefs Vol. 69; no. 8; pp. 3490 - 3494
Main Authors: Xu, Junpeng, He, Xing, Han, Xin, Wen, Hongsong
Format: Journal Article
Language:English
Published: New York The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01.08.2022
Subjects:
Algorithms
Multiagent systems
Optimization
Signal reconstruction
ISSN:1549-7747, 1558-3791
Online Access:Get full text
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Summary:This brief considers a distributed algorithm for solving [Formula Omitted]-minimization problem based on nonlinear neurodynamic system. Compared with centralized algorithms, distributed algorithms have great potential in data privacy protection, distributed storage and processing of data. In this brief, [Formula Omitted]-minimization problem is transformed into a distributed problem by using multiagent consensus theory. For the distributed optimization problem, a two-layer distributed algorithm is designed by utilizing neurodynamic system, projection matrix and derivative feedback technique. Compared with the existing distributed neurodynamic algorithm, the proposed algorithm has a simpler structure and has fewer neurons on the premise that the calculation error does not increase. Besides, the proposed algorithm converges to a minimal point of [Formula Omitted]-minimization problem and is Lyapunov stable. Finally, the comparative examples of sparse signal reconstruction show that the proposed distributed algorithm is effective and superior.
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ISSN:1549-7747
1558-3791
DOI:10.1109/TCSII.2022.3159814