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Lagrange Neural Network for Nonsmooth Nonconvex Optimization Problems with Equality and Inequality Constrains |
YU Xin① XU Zhijian① CHEN Zhaorong① XU Chenhua② |
①(School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China)
②(School of Electrical Engineering, Guangxi University, Nanning 530004, China) |
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Abstract Nonconvex nonsmooth optimization problems are related to many fields of science and engineering applications, which are research hotspots. For the lack of neural network based on early penalty function for nonsmooth optimization problems, a recurrent neural network model is proposed using Lagrange multiplier penalty function to solve the nonconvex nonsmooth optimization problems with equality and inequality constrains. Since the penalty factor in this network model is variable, without calculating initial penalty factor value, the network can still guarantee convergence to the optimal solution, which is more convenient for network computing. Compared with the traditional Lagrange method, the network model adds an equality constraint penalty term, which can improve the convergence ability of the network. Through the detailed analysis, it is proved that the trajectory of the network model can reach the feasible region in finite time and finally converge to the critical point set. In the end, numerical experiments are given to verify the effectiveness of the theoretic results.
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Received: 12 October 2016
Published: 18 May 2017
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Fund: The National Natural Science Foundation of China (61462006, 51407037), The Natural Science Foundation of Guangxi Province (2014GXNSFAA118391) |
Corresponding Authors:
XU Zhijian
E-mail: zhongxiawuyu@126.com
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[1] |
MIAO Peng, SHEN Yanjun, LI Yujiao, et al. Finite-time recurrent neural networks for solving nonlinear optimization problems and their application[J]. Neurocomputing, 2016, 177(7): 120-129. doi: 10.1016/j.neucom.2015.11.014.
|
[2] |
MESTARI M, BENZIRAR M, SABER N, et al. Solving nonlinear equality constrained multiobjective optimization problems using neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(10): 19-35. doi: 10.1109/TNNLS.2015.2388511.
|
[3] |
HOSSEINI A. A non-penalty recurrent neural network for solving a class of constrained optimization problems[J]. Neural Networks, 2016, 73(1): 10-25. doi: 10.1016/j.neunet. 2015.09.013.
|
[4] |
FORTI M, NISTRI P, and QUINCAMPOIX M. Generalized neural network for nonsmooth nonlinear programming problems[J]. IEEE Transactions on Circuits and Systems I Regular Papers, 2004, 51(9): 1741-1754. doi: 10.1109/TCSI. 2004.834493.
|
[5] |
XUE Xiaoping and BIAN Wei. Subgradient-based neural networks for nonsmooth convex optimization problems[J]. IEEE Transactions on Circuits and Systems I Regular Papers, 2008, 55(8): 2378-2391. doi: 10.1109/TCSI.2008.920131.
|
[6] |
BIAN Wei and XUE Xiaoping. Subgradient-based neural networks for nonsmooth nonconvex optimization problems[J]. IEEE Transactions on Neural Networks, 2009, 20(6): 1024-1038. doi: 10.1109/TNN.2009.2016340.
|
[7] |
LIU Qingshan and WANG Jun. A one-layer projection neural network for nonsmooth optimization subject to linear equalities and bound constraints[J]. IEEE Transactions on Neural Networks and Learning Systems, 2013, 24(5): 812-824. doi: 10.1109/TNNLS.2013.2244908.
|
[8] |
BIAN Wei and CHEN Xiaojun. Smoothing neural network for constrained non-Lipschitz optimization with applications [J]. IEEE Transactions on Neural Networks and Learning Systems, 2012, 23(3): 399-411. doi: 10.1109/TNNLS.2011. 2181867.
|
[9] |
QIN Sitian, BIAN Wei, and XUE Xiaoping. A new one-layer recurrent neural network for nonsmooth pseudoconvex optimization[J]. Neurocomputing, 2013, 120(22): 655-662. doi: 10.1016/j.neucom.2013.01.025.
|
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