Michael Metel

Simple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized Optimization

Our work focuses on stochastic gradient methods for optimizing a smooth non-convex loss function with a non-smooth non-convex regularizer. Research on this class of problem is quite limited, and until recently no non-asymptotic convergence results have been reported. We present two simple stochastic…