@inproceedings{95e3914636e64d7d9f0d6d797fdb87de,
title = "A Gradient-Descent Neurodynamic Approach for Distributed Linear Programming",
abstract = "In this paper, a gradient-descent neurodynamic approach is proposed for the distributed linear programming problem with affine equality constraints. It is rigorously proved that the state solution of the proposed gradient-descent approach with an arbitrary initial point reaches agreement and is convergent to an optimal solution of the considered optimization problem at the same time. In the end, some numerical experiments are conducted to verify the effectiveness of the proposed gradient-descent approach.",
keywords = "Convergence to an optimal solution, Distributed linear programming, Gradient-descent neurodynamic approach, Reach agreement",
author = "Xinrui Jiang and Sitian Qin and Ping Guo",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 16th International Symposium on Neural Networks, ISNN 2019 ; Conference date: 10-07-2019 Through 12-07-2019",
year = "2019",
doi = "10.1007/978-3-030-22808-8\_5",
language = "英语",
isbn = "9783030228071",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "45--53",
editor = "Huchuan Lu and Huajin Tang and Zhanshan Wang",
booktitle = "Advances in Neural Networks – ISNN 2019 - 16th International Symposium on Neural Networks, ISNN 2019, Proceedings",
address = "德国",
}