Abstract

In this paper, we present a recurrent neural network for solving convex quadratic programming problems, in the theoretical aspect, we prove that the proposed neural network can converge globally to the solution set of the problem when the matrix involved in the problem is positive semi-definite and can converge exponentially to a unique solution when the matrix is positive definite. Illustrative examples further show the good performance of the proposed neural network.

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