Discussions of neural network solvers for inverse optimization problems

Abstract

We discuss a neural network solver for the inverse optimization problem. The problem is that input/teaching data include defects, and predict the defect values, and estimate functional relation between the input/output data. The network structure of the solver is series-connected three-layer neural networks. Information propagates among the networks alternatively, and the defects are complemented by the correlations among data. On ideal structure-activity data, we could make the prediction within 0.17-3.6% error.