15 citations · 58 across the 38 of their papers we have counts for
7 papers · 1 filter
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…
Mirror Descent for Constrained Optimization Problems with Large Subgradient Values
Fedor Stonyakin, Alexey Stepanov, Alexander Gasnikov +1
Based on the ideas of arXiv:1710.06612, we consider the problem of minimization of the Holder-continuous non-smooth functional with non-positive convex (generally, non-smooth)…
New Version of Mirror Prox for Variational Inequalities with Adaptation to Inexactness
Fedor Stonyakin, Evgeniya Vorontsova, Mohammad Alkousa
Some adaptive analogue of the Mirror Prox method for variational inequalities is proposed. In this work we consider the adaptation not only to the value of the Lipschitz constant,…
Accelerated methods for composite non-bilinear saddle point problem
Mohammad Alkousa, Darina Dvinskikh, Fedor Stonyakin +2
Based on G. Lan's accelerated gradient sliding and general relation between the smoothness and strong convexity parameters of function under Legendre transformation we show that un…
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…
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…