Accelerated and nonaccelerated stochastic gradient descent with model conception
Darina Dvinskikh, Tyurin, Alexander, Alexander Vladimirovich Gasnikov, Sergey Sergeevich Omelchenko · arXiv (Cornell University) · 2020
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.