Neural networks give a warm start to linear optimization problems

Hopfield neural networks and interior point methods are used in an integrated way to solve linear optimization problems. The neural network unveils a warm starting point for the primal-dual interior point method. This approach was applied to a set of real world linear programming problems. Results from a pure primal-dual algorithm provide a yardstick. The integrated approach provides promising results, indicating that there might be a place for neural networks in the "real game" of optimization