@inproceedings{ffb9b153d1c246a2ac2015f845cbd785,
title = "An artificial neural network for distributed constrained optimization",
abstract = "This paper studies the distributed convex optimization problems, where the objective function can be expressed as the sum of nonsmooth local convex objective functions. By the virtue of KKT conditions, an artificial neural network is presented to solve the distributed convex optimization problems with inequality and equality constraints. And it is shown that the state solution of the artificial neural network converges to the optimal solution to the original optimization problem. Compared with the existing continuous time algorithms, the provided algorithm has the advantages of lower model complexity and easy implementation. Finally, a numerical example displays the practicality of the algorithm.",
keywords = "Artificial neural network, Consensus, Distributed optimization, Global convergence, Lyapunov function",
author = "Na Liu and Wenwen Jia and Sitian Qin and Guocheng Li",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 25th International Conference on Neural Information Processing, ICONIP 2018 ; Conference date: 13-12-2018 Through 16-12-2018",
year = "2018",
doi = "10.1007/978-3-030-04179-3\_38",
language = "英语",
isbn = "9783030041786",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "430--441",
editor = "Leung, \{Andrew Chi Sing\} and Seiichi Ozawa and Long Cheng",
booktitle = "Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings",
address = "德国",
}