Sub-gradient based projection neural networks for non-differentiable optimization problems

This paper further investigates the sub-gradient projection neural networks model for solving non- differentiable convex optimization problems proposed in reference [1]. It is proved in this paper that when the initial points are belong to the constraint set or the initial points are not belong to the constraint set and the objective function is strictly convex, the network trajectories converge to an optimal solution of the primal optimal problem.