Stochastic Proximal Methods for Non-Smooth Non-Convex Constrained Sparse Optimization.

This paper focuses on stochastic proximal gradient methods for optimizing a smooth nonconvex loss function with a non-smooth non-convex regularizer and convex constraints. To the best of our knowledge we present the rst non-asymptotic convergence bounds for this class of problem. We present two simple stochastic proximal gradient algorithms, for general stochastic and nite-sum optimization problems. In a numerical experiment we compare our algorithms with the current state-of-the-art deterministic algorithm and nd our algorithms to exhibit superior convergence. [ABSTRACT FROM AUTHOR]