Neural networks as an aid to iterative optimization methods
H.J. Li, Andrew H. Sung, W.W. Weiss, Shaochang Wo · 2002
This paper presents an approach of using neural networks to select starting points for iterative methods for optimization problems. Since input/output training data are often available or easily obtained from the problem description, a neural network can be trained to provide a rough model of the optimization problem. After the neural network is trained, it is used to select starting points for the iterative algorithm. We illustrate the potential of this approach with examples.