• Corpus ID: 219792827

Stochastic Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization

@article{Song2020StochasticVR,
  title={Stochastic Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization},
  author={Chaobing Song and Yong Jiang and Yi Ma},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.10281}
}
In this paper, we introduce a simplified and unified method for finite-sum convex optimization, named \emph{Stochastic Variance Reduction via Accelerated Dual Averaging (SVR-ADA)}. In the nonstrongly convex and smooth setting, SVR-ADA can attain an $O\big(\frac{1}{n}\big)$-accurate solution in $O(n\log\log n)$ number of stochastic gradient evaluations, where $n$ is the number of samples; meanwhile, SVR-ADA matches the lower bound of this setting up to a $\log\log n$ factor. In the strongly… 

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