Primal and dual neural networks for shortest-path routing

This paper presents two recurrent neural networks for solving the shortest path problem. Simplifying the architecture of a recurrent neural network based on the primal problem formulation, the first recurrent neural network called the primal routing network has less complex connectivity than its predecessor. Based on the dual problem formulation, the second recurrent neural network called the dual routing network has even much simpler architecture. While being simple in architecture, the primal and dual routing networks are capable of shortest-path routing like their predecessor.