Stochastic Smoothing for Nonsmooth Minimizations: Accelerating SGD by Exploiting Structure
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Abstract
In this work we consider the stochastic minimization of nonsmooth convex loss functions, a central problem in machine learning. We propose a novel algorithm called Accelerated Nonsmooth Stochastic Gradient Descent (ANSGD), which exploits the structure of common nonsmooth loss functions to achieve optimal convergence rates for a class of problems including SVMs. It is the first stochastic algorithm that can achieve the optimal O(1/t) rate for minimizing nonsmooth loss functions (with strong convexity). The fast rates are con?rmed by empirical comparisons, in which ANSGD signi?cantly outperforms previous subgradient descent algorithms including SGD.
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