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.
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Acknowledgments
This research is supported by the National Natural Science Foundation of China (61773136, 11471088) and the NSF project of Shandong province in China with granted No. ZR2014FM023.
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Liu, N., Jia, W., Qin, S., Li, G. (2018). An Artificial Neural Network for Distributed Constrained Optimization. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11302. Springer, Cham. https://doi.org/10.1007/978-3-030-04179-3_38
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DOI: https://doi.org/10.1007/978-3-030-04179-3_38
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