Lower Bounds for Non-Convex Stochastic Optimization

We lower bound the complexity of finding \(\epsilon\)-stationary points (with gradient norm at most \(\epsilon\)) using stochastic first-order methods. In a well-studied model where algorithms access smooth, potentially non-convex functions through queries to an unbiased stochastic gradient oracle with bounded variance, we prove that (in the worst case) any algorithm requires at least \(\epsilon^{-4}\) queries to find an \(\epsilon\) stationary point. The lower bound is tight, and establishes that stochastic gradient descent is minimax optimal in this model. In a more restrictive model where the noisy gradient estimates satisfy a mean-squared smoothness property, we prove a lower bound of \(\epsilon^{-3}\) queries, establishing the optimality of recently proposed variance reduction techniques.