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arXiv · 1607.07329

Accelerating Stochastic Composition Optimization

Mengdi Wang·Ji Liu·Ethan X. Fang
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Abstract

Consider the stochastic composition optimization problem where the objective is a composition of two expected-value functions. We propose a new stochastic first-order method, namely the accelerated stochastic compositional proximal gradient (ASC-PG) method, which updates based on queries to the sampling oracle using two different timescales. The ASC-PG is the first proximal gradient method for the stochastic composition problem that can deal with nonsmooth regularization penalty. We show that the ASC-PG exhibits faster convergence than the best known algorithms, and that it achieves the optimal sample-error complexity in several important special cases. We further demonstrate the application of ASC-PG to reinforcement learning and conduct numerical experiments.

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Mengdi Wang, Ji Liu, Ethan X. Fang. 2016-07-25. Accelerating Stochastic Composition Optimization. https://arxiv.org/abs/1607.07329

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