An RNN-Based Algorithm for Decentralized-Partial-Consensus Constrained Optimization.
basic_science · Level V
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- Record sourced from PubMed, PMID 34464262.
- Also identified by DOI 10.1109/TNNLS.2021.3098668.
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Abstract
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.