Abstract
Variational inequalities provide us with a tool to study a wide class of optimization arising in pure and applied sciences. In the paper,we present a neural network for solving linear variational inequalities and quadratic optimization by using a projection techniques. We also consider the global uniqueness of the solution of the neural network as well as the convergence of the modified projection neural network. Our results present a significant improvement of previously known projection methods for solving variational inequalities and related optimization problems. Two simulation examples are provided to show the effectiveness of the approach and applicability of the proposed criteria.
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Jiang, M., Zhao, Y., Shen, Y. (2009). A Modified Projection Neural Network for Linear Variational Inequalities and Quadratic Optimization Problems. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_1
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DOI: https://doi.org/10.1007/978-3-642-01513-7_1
Publisher Name: Springer, Berlin, Heidelberg
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