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Hopfield neural networks in large-scale linear optimization problems |
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| Authors: | Marta I Velazco Fontova |
| Institution: | a Faculty of Campo Limpo Paulista-FACCAMP, Rua Guatemala 167, Bairro Jd. América, 13231-230 Campo Limpo Paulista, SP, Brazil b Institute of Mathematics, Statistics and Scientific Computing (IMECC), University of Campinas (UNICAMP), Praça Sérgio Buarque de Holanda 651, CP 6065, 13083-859 Campinas, SP, Brazil c Department of Systems Engineering (DENSIS), School of Electrical and Computer Engineering (FEEC), University of Campinas (UNICAMP), Av. Albert Einstein 400, CP 6101, 13083-852 Campinas, SP, Brazil |
| Abstract: | Hopfield neural networks and affine scaling interior point methods are combined in a hybrid approach for solving linear optimization problems. The Hopfield networks perform the early stages of the optimization procedures, providing enhanced feasible starting points for both primal and dual affine scaling interior point methods, thus facilitating the steps towards optimality. The hybrid approach is applied to a set of real world linear programming problems. The results show the potential of the integrated approach, indicating that the combination of neural networks and affine scaling interior point methods can be a good alternative to obtain solutions for large-scale optimization problems. |
| Keywords: | Hopfield networks Optimization Interior point methods Affine scaling methods Linear programming Neural networks |
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