2 citations · 7 across the 20 of their papers we have counts for
23 papers · 1 filter
Decentralised convex optimisation with probability-proportional-to-size quantization
Dmitrii Pasechniuk, Pavel Dvurechensky, César A. Uribe +1
Communication is one of the bottlenecks of distributed optimisation and learning. To overcome this bottleneck, we propose a novel quantization method that transforms a vector into…
OPTAMI: Global Superlinear Convergence of High-order Methods
Dmitry Kamzolov, Dmitry Pasechnyuk, Artem Agafonov +2
Second-order methods for convex optimization outperform first-order methods in terms of theoretical iteration convergence, achieving rates up to for highly-smooth funct…
Algorithms for Euclidean-regularised Optimal Transport
Dmitry A. Pasechnyuk, Michael Persiianov, Pavel Dvurechensky +1
This paper addresses the Optimal Transport problem, which is regularized by the square of Euclidean -norm. It offers theoretical guarantees regarding the iteration complexi…
Primal-Dual Gradient Methods for Searching Network Equilibria in Combined Models with Nested Choice Structure and Capacity Constraints
Meruza Kubentayeva, Demyan Yarmoshik, Mikhail Persiianov +8
We consider a network equilibrium model (i.e. a combined model), which was proposed as an alternative to the classic four-step approach for travel forecasting in transportation net…
A randomised non-descent method for global optimisation
Dmitry A. Pasechnyuk, Alexander Gornov
This paper proposes novel algorithm for non-convex multimodal constrained optimisation problems. It is based on sequential solving restrictions of problem to sections of feasible s…
A Damped Newton Method Achieves Global and Local Quadratic Convergence Rate
Slavomír Hanzely, Dmitry Kamzolov, Dmitry Pasechnyuk +3
In this paper, we present the first stepsize schedule for Newton method resulting in fast global and local convergence guarantees. In particular, a) we prove an $O\left( \frac 1 {k…
Effects of momentum scaling for SGD
Dmitry A. Pasechnyuk, Alexander Gasnikov, Martin Takáč
The paper studies the properties of stochastic gradient methods with preconditioning. We focus on momentum updated preconditioners with momentum coefficient . Seeking to explain…
Stochastic optimization in digital pre-distortion of the signal
A. V. Alpatov, E. A. Peters, D. A. Pasechnyuk +1
In this paper, we test the performance of some modern stochastic optimization methods and practices in application to digital pre-distortion problem, that is a valuable part of pro…
Network utility maximization by updating individual transmission rates
Dmitry Pasechnyuk
This paper discusses the problem of maximizing the total data transmission utility of the computer network. The total utility is defined as the sum of the individual (corresponding…
Stochastic optimization for dynamic pricing
Dmitry Pasechnyuk, Pavel Dvurechensky, Sergey Omelchenko +1
We consider the problem of supply and demand balancing that is stated as a minimization problem for the total expected revenue function describing the behavior of both consumers an…
Non-convex optimization in digital pre-distortion of the signal
Dmitry Pasechnyuk, Alexander Maslovskiy, Alexander Gasnikov +9
In this paper, we give some observation of applying modern optimization methods for functionals describing digital predistortion (DPD) of signals with orthogonal frequency division…
On the Computational Efficiency of Catalyst Accelerated Coordinate Descent
Dmitry Pasechnyuk, Vladislav Matyukhin
This article is devoted to one particular case of using universal accelerated proximal envelopes to obtain computationally efficient accelerated versions of methods used to solve v…
Accelerated Proximal Envelopes: Application to the Coordinate Descent Method
Dmitry Pasechnyuk, Anton Anikin, Vladislav Matyukhin
This article is devoted to one particular case of using universal accelerated proximal envelopes to obtain computationally efficient accelerated versions of methods used to solve v…
Solving strongly convex-concave composite saddle point problems with a small dimension of one of the variables
Egor Gladin, Ilya Kuruzov, Fedor Stonyakin +3
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 of the…
Oracle Complexity Separation in Convex Optimization
Anastasiya Ivanova, Evgeniya Vorontsova, Dmitry Pasechnyuk +4
Many convex optimization problems have structured objective function written as a sum of functions with different types of oracles (full gradient, coordinate derivative, stochastic…
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…
Adaptive Mirror Descent for the Network Utility Maximization Problem
Anastasiya Ivanova, Fedor Stonyakin, Dmitry Pasechnyuk +2
Network utility maximization is the most important problem in network traffic management. Given the growth of modern communication networks, we consider the utility maximization pr…
Adaptive Catalyst for Smooth Convex Optimization
Anastasiya Ivanova, Dmitry Pasechnyuk, Dmitry Grishchenko +3
In this paper, we present a generic framework that allows accelerating almost arbitrary non-accelerated deterministic and randomized algorithms for smooth convex optimization probl…
Scheduling strategies for resource allocation in a cellular base station
Dmitry Pasechnyuk
The problem of scheduling the (time) resource allocation of a base station (cell tower) that interacts with clients (users of wireless mobile devices with Internet access) and serv…
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…
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…
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…
One Method for Minimization a Convex Lipschitz-Continuous Function of 2 Variables on a Fixed Square
Dmitry A. Pasechnyuk, Fedor S. Stonyakin
In the article we have obtained some estimates of the rate of convergence for the recently proposed by Yu.E. Nesterov method of minimization of a convex Lipschitz-continuous functi…