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A Two-Layer Distributed Algorithm Using Neurodynamic System for Solving L1-Minimization
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DOI：10.1109/TCSII.2022.3159814.png)
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摘要
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This brief considers a distributed algorithm for solving L-1-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, L-1-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 L-1-minimization problem and is Lyapunov stable. Finally, the comparative examples of sparse signal reconstruction show that the proposed distributed algorithm is effective and superior.
Keyword:
L-1-minimization problem
sparse signal reconstruction
distributed algorithm
neurodynamic system
期刊
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4.9
论文数:
8.8K
被引数:
2.6W
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