Abstract
An interior point recurrent neural network for convex inequality constrained optimization problems is proposed, based on the logarithmic barrier function. A time varying barrier parameter is used and the network’s dynamical equations are based on Newton’s method. Strictly feasible interior point trajectories are produced which converge to the exact solution of the constrained problem as t → ∞. Numerical results for examples of various sizes show that the method is both efficient and accurate.
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Acknowledgements
The authors wish to thank Prof. Themistocles M. Rassias for his kind editorial hospitality. The first author would like to thank the National Scholarship Foundation of Greece (IKY) for financial support during the course of this work.
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Krasopoulos, P.T., Maratos, N.G. (2014). An Interior Point Recurrent Neural Network for Convex Optimization Problems. In: Pardalos, P., Rassias, T. (eds) Mathematics Without Boundaries. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-1124-0_13
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DOI: https://doi.org/10.1007/978-1-4939-1124-0_13
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