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.