Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4630108 | Applied Mathematics and Computation | 2012 | 9 Pages |
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.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Marta I. Velazco Fontova, Aurelio R.L. Oliveira, Christiano Lyra,