Warm start by Hopfield neural networks for interior point methods

Authors:
Marta I. Velazco Fontova;Aurelio R. L. Oliveira;Christiano Lyra
Affiliations:
Faculty of Electrical Engineering and Computer Science (FEEC), Department of Systems Engineering (DENSIS), State University of Campinas (UNICAMP), Av. Albert Einstein 400, CP 6101, 13083-970, Camp ...;Institute of Mathematics, Statistics and Scientific Computing (IMECC), State University of Campinas (UNICAMP), Praça Sérgio Buarque de Holanda 651, CP 6065, 13083-859, Campinas, SP, Braz ...;Faculty of Electrical Engineering and Computer Science (FEEC), Department of Systems Engineering (DENSIS), State University of Campinas (UNICAMP), Av. Albert Einstein 400, CP 6101, 13083-970, Camp ...
Venue:
Computers and Operations Research
Year:
2007

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Abstract

Hopfield neural networks and interior point methods are used in an integrated way to solve linear optimization problems. The Hopfield network gives warm start for the primal-dual interior point methods, which can be way ahead in the path to optimality. The approaches were applied to a set of real world linear programming problems. The integrated approaches provide promising results, indicating that there may be a place for neural networks in the ''real game'' of optimization.