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Poster

in

Workshop: OPT 2021: Optimization for Machine Learning

## ANITA: An Optimal Loopless Accelerated Variance-Reduced Gradient Method

### Zhize Li

Abstract:
In this paper, we propose a novel accelerated gradient method called ANITA for solving the fundamental finite-sum optimization problems. Concretely, we consider both general convex and strongly convex settings:
i) For general convex finite-sum problems, ANITA improves previous state-of-the-art result given by Varag (Lan et al., 2019). In particular, for large-scale problems or the target error is not very small, i.e., $n \geq \frac{1}{\epsilon^2}$, ANITA obtains the \emph{first} optimal result $O(n)$, matching the lower bound $\Omega(n)$ provided by Woodworth and Srebro (2016), while previous results are $O(n \log \frac{1}{\epsilon})$ of Varag (Lan et al., 2019) and $O(\frac{n}{\sqrt{\epsilon}})$ of Katyusha (Allen-Zhu, 2017).
ii) For strongly convex finite-sum problems, we also show that ANITA can achieve the optimal convergence rate $O\big((n+\sqrt{\frac{nL}{\mu}})\log\frac{1}{\epsilon}\big)$ matching the lower bound $\Omega\big((n+\sqrt{\frac{nL}{\mu}})\log\frac{1}{\epsilon}\big)$ provided by Lan and Zhou (2015).
Besides, ANITA enjoys a simpler loopless algorithmic structure unlike previous accelerated algorithms such as Varag (Lan et al., 2019) and Katyusha (Allen-Zhu, 2017) where they use an inconvenient double-loop structure. Moreover, by exploiting the loopless structure of ANITA, we provide a new \emph{dynamic multi-stage convergence analysis}, which is the key technical part for improving previous results to the optimal rates.
Finally, the numerical experiments show that ANITA converges faster than the previous state-of-the-art Varag (Lan et al., 2019), validating our theoretical results and confirming the practical superiority of ANITA.
We believe that our new theoretical rates and convergence analysis for this fundamental finite-sum problem will directly lead to key improvements for many other related problems, such as distributed/federated/decentralized optimization problems. For instance, Li and Richtarik (2021) obtain the first compressed and accelerated result, substantially improving previous state-of-the-art results, by applying ANITA to the distributed optimization problems with compressed communication.

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