We study structured convex optimization problems, with additive objective $ r:= p+ q $,
where $ r $ is ($\mu $-strongly) convex, $ q $ is $ L_q $-smooth and convex, and $ p $ is …

In the last few years, the theory of decentralized distributed convex optimization has made
significant progress. The lower bounds on communications rounds and oracle calls have …

We consider a distributed stochastic optimization problem that is solved by a decentralized
network of agents with only local communication between neighboring agents. The goal of …

We introduce an inexact oracle model for variational inequalities with monotone operators,
propose a numerical method that solves such variational inequalities, and analyze its …

We propose general non-accelerated [The results for non-accelerated methods first
appeared in December 2020 in the preprint (A. Agafonov, D. Kamzolov, P. Dvurechensky …

This paper considers the problem of decentralized, personalized federated learning. For
centralized personalized federated learning, a penalty that measures the deviation from the …

Many convex optimization problems have structured objective functions written as a sum of
functions with different oracle types (eg, full gradient, coordinate derivative, stochastic …

Exploiting higher-order derivatives in convex optimization is known at least since 1970's. In
each iteration higher-order (also called tensor) methods minimize a regularized Taylor …

In this paper we propose three $ p $-th order tensor methods for $\mu $-strongly-convex-
strongly-concave saddle point problems (SPP). The first method is based on the assumption …

The article is devoted to the development of algorithmic methods ensuring efficient
complexity bounds for strongly convex-concave saddle point problems in the case when one …