An improved neural network for convex quadratic optimization with application to real-time beamforming

This paper develops an improved neural network to solve convex quadratic optimization problems with general linear constraints. Compared with the existing primal-dual neural network and dual neural network for solving such problems, the proposed neural network has a lower complexity for implementation. Unlike the Kennedy-Chua neural network, the proposed neural network can converge to an exact optimal solution. Analyzed results and illustrative examples show that the proposed neural network has a fast convergence to the optimal solution. Finally, the proposed neural network is effectively applied to real-time beamforming.

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