An Interior Point Recurrent Neural Network for Convex Optimization Problems

Panagiotis T. Krasopoulos () and Nicholas G. Maratos ()
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Panagiotis T. Krasopoulos: National Technical University of Athens (NTUA), School of Electrical and Computer Engineering
Nicholas G. Maratos: National Technical University of Athens (NTUA), School of Electrical and Computer Engineering

A chapter in Mathematics Without Boundaries, 2014, pp 409-427 from Springer

Abstract: 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.

Keywords: Recurrent neural networks; Interior point methods; Convex optimization; Inequality constrains; Barrier functions; Convergence (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4939-1124-0_13

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DOI: 10.1007/978-1-4939-1124-0_13

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