SSRGD: Simple Stochastic Recursive Gradient Descent for Escaping Saddle Points

We analyze stochastic gradient algorithms for optimizing nonconvex problems. In particular, our goal is to find local minima (second-order stationary points) instead of just finding first-order stationary points which may be some bad unstable saddle points.