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 2023-01, Vol.34 (1), p.534-542
Hauptverfasser:	Xia, Zicong, Liu, Yang, Qiu, Jianlong, Ruan, Qihua, Cao, Jinde
Format:	Artikel
Sprache:	eng
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
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Beschreibung
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.
ISSN:	2162-237X 2162-2388
DOI:	10.1109/TNNLS.2021.3098668