5 citations · 8 across the 7 of their papers we have counts for
7 papers
Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization
Yury Demidovich, Petr Ostroukhov, Grigory Malinovsky +4
Non-convex Machine Learning problems typically do not adhere to the standard smoothness assumption. Based on empirical findings, Zhang et al. (2020b) proposed a more realistic gene…
Exploring Jacobian Inexactness in Second-Order Methods for Variational Inequalities: Lower Bounds, Optimal Algorithms and Quasi-Newton Approximations
Artem Agafonov, Petr Ostroukhov, Roman Mozhaev +5
Variational inequalities represent a broad class of problems, including minimization and min-max problems, commonly found in machine learning. Existing second-order and high-order…
AdaBatchGrad: Combining Adaptive Batch Size and Adaptive Step Size
Petr Ostroukhov, Aigerim Zhumabayeva, Chulu Xiang +3
This paper presents a novel adaptation of the Stochastic Gradient Descent (SGD), termed AdaBatchGrad. This modification seamlessly integrates an adaptive step size with an adjustab…
Tensor methods inside mixed oracle for min-min problems
Petr Ostroukhov
In this article we consider min-min type of problems or minimization by two groups of variables. Min-min problems may occur in case if some groups of variables in convex optimizati…
Tensor methods for strongly convex strongly concave saddle point problems and strongly monotone variational inequalities
Petr Ostroukhov, Rinat Kamalov, Pavel Dvurechensky +1
In this paper we propose three -th order tensor methods for -strongly-convex-strongly-concave saddle point problems (SPP). The first method is based on the assumption of -…
Self-Concordant Analysis of Frank-Wolfe Algorithms
Pavel Dvurechensky, Petr Ostroukhov, Kamil Safin +2
Projection-free optimization via different variants of the Frank-Wolfe (FW), a.k.a. Conditional Gradient method has become one of the cornerstones in optimization for machine learn…
Near-optimal tensor methods for minimizing the gradient norm of convex functions and accelerated primal-dual tensor methods
Pavel Dvurechensky, Petr Ostroukhov, Alexander Gasnikov +2
Motivated, in particular, by the entropy-regularized optimal transport problem, we consider convex optimization problems with linear equality constraints, where the dual objective…