We consider stochastic convex optimization problems with affine constraints and develop
several methods using either primal or dual approach to solve it. In the primal case, we use …

We introduce primal and dual stochastic gradient oracle methods for decentralized convex
optimization problems. Both for primal and dual oracles, the proposed methods are optimal …

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 optimization methods for convex minimization problems under inexact
information on the objective function. We introduce inexact model of the objective, which as …

In this paper, we propose a general algorithmic framework for the first-order methods in
optimization in a broad sense, including minimization problems, saddle-point problems and …

First-order methods for solving convex optimization problems have been at the forefront of
mathematical optimization in the last 20 years. The rapid development of this important class …

We consider smooth stochastic convex optimization problems in the context of algorithms
which are based on directional derivatives of the objective function. This context can be …

We present a stochastic descent algorithm for unconstrained optimization that is particularly
efficient when the objective function is slow to evaluate and gradients are not easily …

An envelope called an accelerated meta-algorithm is proposed. Based on the envelope,
accelerated methods for solving convex unconstrained minimization problems in various …

We consider the problem of unconstrained minimization of a smooth objective function in
$\mathbb {R}^ d $ in setting where only function evaluations are possible. We propose and …