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Neural network approaches based on new NCP-functions for solving tensor complementarity problem

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Abstract

Two new NCP-functions are constructed firstly in this paper. The main purpose is to accelerate the process of solution-finding for tensor complementarity problem, which is implemented by neural network methods based on the promising NCP-functions. Moreover, the stability properties of the proposed neural networks are achieved via some theoretics and properties of generalization for linear and nonlinear complementarity problems. Plentiful numerical simulations demonstrate that the presented neural networks possess significantly better stability and comparable convergence rates than neural networks based on some existing NCP-functions.

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Acknowledgements

This research is supported by National Science Foundation of China (11901098) and National Key Research and Development Program of China (2018YFC1504200), Fujian Natural Science Foundation (Grant No. 2019J01879).

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Authors and Affiliations

  1. School of Technology, Fuzhou University of International Studies and Trade, Fuzhou, 350202, People’s Republic of China

    Ya-Jun Xie

  2. College of Mathematics and Informatics, Fujian Normal University, Fuzhou, 350117, People’s Republic of China

    Yi-Fen Ke

  3. FJKLMAA and Center for Applied Mathematics of Fujian Province (FJNU), Fuzhou, 350007, People’s Republic of China

    Yi-Fen Ke

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  1. Ya-Jun Xie
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Correspondence to Yi-Fen Ke.

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Xie, YJ., Ke, YF. Neural network approaches based on new NCP-functions for solving tensor complementarity problem. J. Appl. Math. Comput. 67, 833–853 (2021). https://doi.org/10.1007/s12190-021-01509-w

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  • DOI: https://doi.org/10.1007/s12190-021-01509-w

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