Improved Lower Bounds for First-order Stochastic Non-convex Optimization under Markov Sampling

We lower bound the complexity of finding -stationary points (with gradient norm at most ) 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 -4 queries to find an -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 -3 queries, establishing the optimality of recently proposed variance reduction techniques.