Mini-batch stochastic Nesterov's smoothing method for constrained convex stochastic composite optimization Article Swipe

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Ruyu Wang , Chao Zhang , Lichun Wang , Yuan‐Hai Shao · YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2109.05167

This paper considers a class of constrained convex stochastic composite optimization problems whose objective function is given by the summation of a differentiable convex component, together with a nonsmooth but convex component. The nonsmooth component has an explicit max structure that may not easy to compute its proximal mapping. In order to solve these problems, we propose a mini-batch stochastic Nesterov's smoothing (MSNS) method. Convergence and the optimal iteration complexity of the method are established. Numerical results are provided to illustrate the efficiency of the proposed MSNS method for a support vector machine (SVM) model.

Type

preprint

Language

en

Landing Page

http://arxiv.org/abs/2109.05167

PDF

https://arxiv.org/pdf/2109.05167

OA Status

green

References

22

Related Works

10

OpenAlex ID

https://openalex.org/W3199980613

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