An RNN-Based Algorithm for Decentralized-Partial-Consensus Constrained Optimization

This technical note proposes a decentralized-partial-consensus optimization (DPCO) problem with inequality constraints. The partial-consensus matrix originating from the Laplacian matrix is constructed to tackle the partial-consensus constraints. A continuous-time algorithm based on multiple interco...

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Veröffentlicht in:IEEE transaction on neural networks and learning systems Jg. 34; H. 1; S. 534 - 542
Hauptverfasser: Xia, Zicong, Liu, Yang, Qiu, Jianlong, Ruan, Qihua, Cao, Jinde
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Constraints
Cost function
Decentralized-partial-consensus optimization (DPCO)
Learning systems
Neural networks
Neurodynamics
nonsmooth analysis
Optimization
Optimization methods
partial-consensus matrix
Recurrent neural networks
recurrent neural networks (RNNs)
Task analysis
ISSN:2162-237X, 2162-2388, 2162-2388
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Zusammenfassung:This technical note proposes a decentralized-partial-consensus optimization (DPCO) problem with inequality constraints. The partial-consensus matrix originating from the Laplacian matrix is constructed to tackle the partial-consensus constraints. A continuous-time algorithm based on multiple interconnected recurrent neural networks (RNNs) is derived to solve the optimization problem. In addition, based on nonsmooth analysis and Lyapunov theory, the convergence of continuous-time algorithm is further proved. Finally, several examples demonstrate the effectiveness of main results.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2021.3098668