Hopfield neural networks in large-scale linear optimization problems

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 sc...

Full description

Saved in:
Bibliographic Details
Published in:Applied mathematics and computation 2012-02, Vol.218 (12), p.6851-6859
Main Authors: Velazco Fontova, Marta I., Oliveira, Aurelio R.L., Lyra, Christiano
Format: Article
Language:English
Subjects:
Affine scaling methods
Computation
Hopfield networks
Interior point methods
Linear programming
Mathematical models
Networks
Neural networks
Optimization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2011.12.059