In this paper, a new theory is developed for firstorder stochastic convex optimization, showing that the global convergence rate is sufficiently quantified by a local growth rate of the objective function in a neighborhood of the optimal solutions. In particular, if the objective function F (w) in the -sublevel set grows as fast as ‖w − w∗‖ 2 , where w∗ represents the closest optimal solution t...
