66 citations · 450 across the 76 of their papers we have counts for
11 papers · 1 filter
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…
Generalized Self-concordant Hessian-barrier algorithms
Pavel Dvurechensky, Mathias Staudigl, César A. Uribe
Many problems in statistical learning, imaging, and computer vision involve the optimization of a non-convex objective function with singularities at the boundary of the feasible s…
Alternating Minimization Methods for Strongly Convex Optimization
Nazarii Tupitsa, Pavel Dvurechensky, Alexander Gasnikov +1
{We consider alternating minimization procedures for convex optimization problems with variable divided in many block, each block being amenable for minimization with respect to it…
Adaptive Gradient Descent for Convex and Non-Convex Stochastic Optimization
Darina Dvinskikh, Aleksandr Ogaltsov, Alexander Gasnikov +3
In this paper we propose several adaptive gradient methods for stochastic optimization. Unlike AdaGrad-type of methods, our algorithms are based on Armijo-type line search and they…
Numerical methods for the resource allocation problem in networks
Anastasiya Ivanova, Dmitry Pasechnyuk, Pavel Dvurechensky +2
In this paper, we consider the resource allocation problem in a network with a large number of connections which are used by a huge number of users. The resource allocation problem…
On a Combination of Alternating Minimization and Nesterov's Momentum
Sergey Guminov, Pavel Dvurechensky, Nazarii Tupitsa +1
Alternating minimization (AM) procedures are practically efficient in many applications for solving convex and non-convex optimization problems. On the other hand, Nesterov's accel…
Gradient Methods for Problems with Inexact Model of the Objective
Fedor Stonyakin, Darina Dvinskikh, Pavel Dvurechensky +8
We consider optimization methods for convex minimization problems under inexact information on the objective function. We introduce inexact model of the objective, which as a parti…
On Primal-Dual Approach for Distributed Stochastic Convex Optimization over Networks
Darina Dvinskikh, Eduard Gorbunov, Alexander Gasnikov +2
We introduce a primal-dual stochastic gradient oracle method for distributed convex optimization problems over networks. We show that the proposed method is optimal in terms of com…
Advances in Low-Memory Subgradient Optimization
Pavel Dvurechensky, Alexander Gasnikov, Evgeni Nurminsky +1
This chapter is devoted to the black-box subgradient algorithms with the minimal requirements for the storage of auxiliary results, which are necessary to execute these algorithms.…
Inexact Model: A Framework for Optimization and Variational Inequalities
Fedor Stonyakin, Alexander Gasnikov, Alexander Tyurin +6
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 variat…
On the Complexity of Approximating Wasserstein Barycenter
Alexey Kroshnin, Darina Dvinskikh, Pavel Dvurechensky +3
We study the complexity of approximating Wassertein barycenter of discrete measures, or histograms of size by contrasting two alternative approaches, both using entropic re…