High Probability Guarantees for Random Reshuffling

• URL: http://arxiv.org/abs/2311.11841v2
• Date: Fri, 8 Dec 2023 02:26:17 GMT
• Title: High Probability Guarantees for Random Reshuffling
• Authors: Hengxu Yu, Xiao Li
• Abstract summary: We consider the gradient method with random reshuffling ($mathsfRR$) for tackling nonmathp optimization problems. In this work, we first investigate the neural sample complexity for $mathsfRR$s sampling procedure. Then, we design a random reshuffling method ($mathsfp$mathsfRR$) that involves an additional randomized perturbation procedure stationary points.
• Score: 5.663909018247509
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider the stochastic gradient method with random reshuffling ($\mathsf{RR}$) for tackling smooth nonconvex optimization problems. $\mathsf{RR}$ finds broad applications in practice, notably in training neural networks. In this work, we first investigate the concentration property of $\mathsf{RR}$'s sampling procedure and establish a new high probability sample complexity guarantee for driving the gradient (without expectation) below $\varepsilon$, which effectively characterizes the efficiency of a single $\mathsf{RR}$ execution. Our derived complexity matches the best existing in-expectation one up to a logarithmic term while imposing no additional assumptions nor changing $\mathsf{RR}$'s updating rule. Furthermore, by leveraging our derived high probability descent property and bound on the stochastic error, we propose a simple and computable stopping criterion for $\mathsf{RR}$ (denoted as $\mathsf{RR}$-$\mathsf{sc}$). This criterion is guaranteed to be triggered after a finite number of iterations, and then $\mathsf{RR}$-$\mathsf{sc}$ returns an iterate with its gradient below $\varepsilon$ with high probability. Moreover, building on the proposed stopping criterion, we design a perturbed random reshuffling method ($\mathsf{p}$-$\mathsf{RR}$) that involves an additional randomized perturbation procedure near stationary points. We derive that $\mathsf{p}$-$\mathsf{RR}$ provably escapes strict saddle points and efficiently returns a second-order stationary point with high probability, without making any sub-Gaussian tail-type assumptions on the stochastic gradient errors. Finally, we conduct numerical experiments on neural network training to support our theoretical findings.

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