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Hybrid Stochastic Gradient Descent Algorithms forStochastic Nonconvex Optimization

Quoc Tran-Dinh(quoctd***at***email.unc.edu)
H. Nhan Pham(nhanph***at***live.unc.edu)
T. Dzung Phan(phandu***at***us.ibm.com)
M. Lam Nguyen(LamNguyen.MLTD***at***ibm.com)

Abstract: We introduce a hybrid stochastic estimator to design stochastic gradient algorithms for solving stochastic optimization problems. Such a hybrid estimator is a convex combination of two existing biased and unbiased estimators and leads to some useful property on its variance. We limit our consideration to a hybrid SARAH-SGD for nonconvex expectation problems. However, our idea can be extended to handle a broader class of estimators in both convex and nonconvex settings. We propose a new single-loop stochastic gradient descent algorithm that can achieve $O(\max\{\sigma^3\varepsilon^{-1},\sigma\varepsilon^{-3}\})$-complexity bound to obtain an $\varepsilon$-stationary point under smoothness and $\sigma^2$-bounded variance assumptions. This complexity is better than $O(\sigma^2\varepsilon^{-4})$ often obtained in state-of-the-art SGDs when $\sigma < O(\varepsilon^{-3})$. We also consider different extensions of our method, including constant and adaptive step-size with single-loop, double-loop, and mini-batch variants. We compare our algorithms with existing methods on several datasets using two nonconvex models.

Keywords: Stochastic Optimization; Nonconvex Optimization; Hybrid Gradient Descent

Category 1: Nonlinear Optimization

Category 2: Stochastic Programming

Citation: This is a preprint.

Download: [PDF]

Entry Submitted: 05/28/2019
Entry Accepted: 05/28/2019
Entry Last Modified: 05/28/2019

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