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math.OC2020★ 1 cited
Inexact Tensor Methods and Their Application to Stochastic Convex Optimization
Artem Agafonov, Dmitry Kamzolov, Pavel Dvurechensky +2
We propose general non-accelerated and accelerated tensor methods under inexact information on the derivatives of the objective, analyze their convergence rate. Further, we provide…
math.OC2020
Lower bounds for conditional gradient type methods for minimizing smooth strongly convex functions
Artem Agafonov
In this paper, we consider conditional gradient methods. These are methods that use a linear minimization oracle, which, for a given vector , computes the solut…
math.OC2020★ 2 cited
Inexact Relative Smoothness and Strong Convexity for Optimization and Variational Inequalities by Inexact Model
Fedor Stonyakin, Alexander Tyurin, Alexander Gasnikov +7
In this paper, we propose a general algorithmic framework for first-order methods in optimization in a broad sense, including minimization problems, saddle-point problems, and vari…