SpiderBoost: A Class of Faster Variance-reduced Algorithms for Nonconvex Optimization

Zhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang, Vahid Tarokh

There has been extensive research on developing stochastic variance reduced methods to solve large-scale optimization problems. More recently, novel algorithms SARAH \citep{Lam2017a,Lam2017b} and SPIDER \citep{Fang2018} of such a type have been developed. In particular, SPIDER has been shown in \cite{Fang2018} to outperform existing algorithms of the same type and meet the lower bound in certain regimes for nonconvex optimization. Though appealing in theory, SPIDER requires $\epsilon$-level stepsize to guarantee the convergence, and consequently runs slow in practice. This paper proposes SpiderBoost as an improved scheme, which comes with two major advantages compared to SPIDER. First, it allows much larger stepsize without sacrificing the convergence rate, and hence runs substantially faster in practice. Second, it extends much more easily to proximal algorithms with guaranteed convergence for solving composite optimization problems, which appears challenging for SPIDER due to stringent requirement on per-iteration increment to guarantee its convergence. Both advantages can be attributed to the new convergence analysis we develop for SpiderBoost that allows much more flexibility for choosing algorithm parameters. As further generalization of SpiderBoost, we show that proximal SpiderBoost achieves a stochastic first-order oracle (SFO) complexity of $\mathcal{O}(\min \{n^{1/2}\epsilon^{-2},\epsilon^{-3}\})$ for composite optimization, which improves the existing best results by a factor of $\mathcal{O}(\min\{n^{1/6},\epsilon^{-1/3}\})$. Moreover, for nonconvex optimization under the gradient dominance condition and in an online setting, we also obtain improved oracle complexity results compared to the corresponding state-of-the-art results.

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