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• W2000460928 abstract "In this paper, we present a recurrent neural network for solving mixed linear complementarity problems (MLCPs) with positive semi-definite matrices. The proposed neural network is derived based on an NCP function and has a low complexity respect to the other existing models. In theoretical and numerical aspects, global convergence of the proposed neural network is proved. As an application, we show that the proposed neural network can be used to solve linear and convex quadratic programming problems. The validity and transient behavior of the proposed neural network are demonstrated by using five numerical examples." @default.
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• W2000460928 date "2011-08-01" @default.
• W2000460928 modified "2023-10-14" @default.
• W2000460928 title "A NOVEL RECURRENT NEURAL NETWORK FOR SOLVING MLCPs AND ITS APPLICATION TO LINEAR AND QUADRATIC PROGRAMMING PROBLEMS" @default.
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• W2000460928 doi "https://doi.org/10.1142/s0217595911003223" @default.
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