Abstract
A neural network algorithm for second-order conic programming is proposed. By the Smooth technique, a smooth and convex energy function is constructed. We have proved that for any initial point, every trajectory of the neural network converges to an optimal solution of the second-order conic programming. The simulation results show the proposed neural network is feasible and efficient.
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© 2005 Springer-Verlag Berlin Heidelberg
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Mu, X., Liu, S., Zhang, Y. (2005). A Neural Network Algorithm for Second-Order Conic Programming. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_115
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DOI: https://doi.org/10.1007/11427391_115
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25912-1
Online ISBN: 978-3-540-32065-4
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