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.
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Acknowledgments
This research is supported by the National Natural Science Foundation of China (61773136, 11471088) and the NSFC and CAS project in China with granted No. U1531242.
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Jiang, X., Qin, S., Guo, P. (2019). A Gradient-Descent Neurodynamic Approach for Distributed Linear Programming. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11555. Springer, Cham. https://doi.org/10.1007/978-3-030-22808-8_5
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DOI: https://doi.org/10.1007/978-3-030-22808-8_5
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