# Getting to local/global minima with (stochastic) gradient descent on non-convex objectives

Is there any class of non-convex objective functions for which (stochastic) gradient descent can provably get to a local or a global minima? (..maybe in the approximate sense like a point such that the spectral norm of the Hessian is bounded by some epsilon..)