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arXiv · 2001.03443

Accelerated and nonaccelerated stochastic gradient descent with model conception

Darina Dvinskikh·Alexander Tyurin·Alexander Gasnikov·Sergey Omelchenko
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Abstract

In this paper, we describe a new way to get convergence rates for optimal methods in smooth (strongly) convex optimization tasks. Our approach is based on results for tasks where gradients have nonrandom small noises. Unlike previous results, we obtain convergence rates with model conception.

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Darina Dvinskikh, Alexander Tyurin, Alexander Gasnikov, Sergey Omelchenko. 2020-01-10. Accelerated and nonaccelerated stochastic gradient descent with model conception. https://arxiv.org/abs/2001.03443

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