(Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2021) Agafonov, Artem; Kamzolov, Dmitry; Dvurechensky, Pavel; Gasnikov, Alexander
We propose a general non-accelerated tensor method under inexact information on higher- order derivatives, analyze its convergence rate, and provide sufficient conditions for this method to have similar complexity as the exact tensor method. As a corollary, we propose the first stochastic tensor method for convex optimization and obtain sufficient mini-batch sizes for each derivative.
