A RNNs-based Algorithm for Decentralized-partial-consensus Constrained Optimization
Optimization and Control
2021-03-23 v1
Abstract
This technical note proposes the decentralized-partial-consensus optimization with inequality constraints, and a continuous-time algorithm based on multiple interconnected recurrent neural networks (RNNs) is derived to solve the obtained optimization problems. First, the partial-consensus matrix originating from Laplacian matrix is constructed to tackle the partial-consensus constraints. In addition, using the non-smooth analysis and Lyapunov-based technique, the convergence property about the designed algorithm is further guaranteed. Finally, the effectiveness of the obtained results is shown while several examples are presented.
Cite
@article{arxiv.2103.11659,
title = {A RNNs-based Algorithm for Decentralized-partial-consensus Constrained Optimization},
author = {Zicong Xia and Yang Liu and Jianlong Qiu and Qihua Ruan and Jinde Cao},
journal= {arXiv preprint arXiv:2103.11659},
year = {2021}
}