Natasha 2: Faster Non-Convex Optimization Than SGD

Part of Advances in Neural Information Processing Systems 31 (NeurIPS 2018)

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Authors

Zeyuan Allen-Zhu

Abstract

We design a stochastic algorithm to find $\varepsilon$-approximate local minima of any smooth nonconvex function in rate $O(\varepsilon^{-3.25})$, with only oracle access to stochastic gradients. The best result before this work was $O(\varepsilon^{-4})$ by stochastic gradient descent (SGD).