Geodesic
Accelerated and Unaccelerated Stochastic Gradient Descent in Model Generality
Matematičeskie zametki, Tome 108 (2020) no. 4, pp. 515-528

Voir la notice de l'article provenant de la source Math-Net.Ru

A new method for deriving estimates of the rate of convergence of optimal methods for solving problems of smooth (strongly) convex stochastic optimization is described. The method is based on the results of stochastic optimization derived from results on the convergence of optimal methods under the conditions of inexact gradients with small noises of nonrandom nature. In contrast to earlier results, all estimates in the present paper are obtained in model generality.
Keywords: stochastic optimization, accelerated gradient descent, model generality, composite optimization. 
@article{MZM_2020_108_4_a3,
     author = {D. M. Dvinskikh and A. I. Turin and A. V. Gasnikov and S. S. Omelchenko},
     title = {Accelerated and {Unaccelerated} {Stochastic} {Gradient} {Descent} in {Model} {Generality}},
     journal = {Matemati\v{c}eskie zametki},
     pages = {515--528},
     publisher = {mathdoc},
     volume = {108},
     number = {4},
     year = {2020},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/MZM_2020_108_4_a3/}
}
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D. M. Dvinskikh; A. I. Turin; A. V. Gasnikov; S. S. Omelchenko. Accelerated and Unaccelerated Stochastic Gradient Descent in Model Generality. Matematičeskie zametki, Tome 108 (2020) no. 4, pp. 515-528. http://geodesic.mathdoc.fr/item/MZM_2020_108_4_a3/