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Journal of Applied Mathematics and Computing

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16.02.2021 | Original Research

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

verfasst von: Ya-Jun Xie, Yi-Fen Ke

Erschienen in: Journal of Applied Mathematics and Computing | Ausgabe 1-2/2021

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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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Titel: Neural network approaches based on new NCP-functions for solving tensor complementarity problem
verfasst von: Ya-Jun Xie
Yi-Fen Ke
Publikationsdatum: 16.02.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Journal of Applied Mathematics and Computing / Ausgabe 1-2/2021
Print ISSN: 1598-5865
Elektronische ISSN: 1865-2085
DOI: https://doi.org/10.1007/s12190-021-01509-w

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