Complexity of Finding Stationary Points of Nonsmooth Nonconvex Functions

• URL: http://arxiv.org/abs/2002.04130v3
• Date: Mon, 29 Jun 2020 14:53:13 GMT
• Title: Complexity of Finding Stationary Points of Nonsmooth Nonconvex Functions
• Authors: Jingzhao Zhang, Hongzhou Lin, Stefanie Jegelka, Ali Jadbabaie, Suvrit Sra
• Abstract summary: We provide the first non-asymptotic analysis for finding stationary points of nonsmooth, nonsmooth functions. In particular, we study Hadamard semi-differentiable functions, perhaps the largest class of nonsmooth functions.
• Score: 84.49087114959872
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We provide the first non-asymptotic analysis for finding stationary points of nonsmooth, nonconvex functions. In particular, we study the class of Hadamard semi-differentiable functions, perhaps the largest class of nonsmooth functions for which the chain rule of calculus holds. This class contains examples such as ReLU neural networks and others with non-differentiable activation functions. We first show that finding an $\epsilon$-stationary point with first-order methods is impossible in finite time. We then introduce the notion of $(\delta, \epsilon)$-stationarity, which allows for an $\epsilon$-approximate gradient to be the convex combination of generalized gradients evaluated at points within distance $\delta$ to the solution. We propose a series of randomized first-order methods and analyze their complexity of finding a $(\delta, \epsilon)$-stationary point. Furthermore, we provide a lower bound and show that our stochastic algorithm has min-max optimal dependence on $\delta$. Empirically, our methods perform well for training ReLU neural networks.

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