Alexander Gasnikov
Alexander Gasnikov
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Введение в математическое моделирование транспортных потоков
А Гасников
Litres, 2022
Computational optimal transport: Complexity by accelerated gradient descent is better than by Sinkhorn’s algorithm
P Dvurechensky, A Gasnikov, A Kroshnin
International conference on machine learning, 1367-1376, 2018
A dual approach for optimal algorithms in distributed optimization over networks
CA Uribe, S Lee, A Gasnikov, A Nedić
2020 Information Theory and Applications Workshop (ITA), 1-37, 2020
Decentralize and randomize: Faster algorithm for Wasserstein barycenters
P Dvurechenskii, D Dvinskikh, A Gasnikov, C Uribe, A Nedich
Advances in Neural Information Processing Systems 31, 2018
Stochastic intermediate gradient method for convex problems with stochastic inexact oracle
P Dvurechensky, A Gasnikov
Journal of Optimization Theory and Applications 171, 121-145, 2016
Современные численные методы оптимизации. Метод универсального градиентного спуска
АВ Гасников
Федеральное государственное автономное образовательное учреждение высшего …, 2018
Стохастические градиентные методы с неточным оракулом
АВ Гасников, ПЕ Двуреченский, ЮЕ Нестеров
Труды Московского физико-технического института 8 (1 (29)), 41-91, 2016
On the complexity of approximating Wasserstein barycenters
A Kroshnin, N Tupitsa, D Dvinskikh, P Dvurechensky, A Gasnikov, C Uribe
International conference on machine learning, 3530-3540, 2019
Efficient numerical methods for entropy-linear programming problems
AV Gasnikov, EB Gasnikova, YE Nesterov, AV Chernov
Computational Mathematics and Mathematical Physics 56, 514-524, 2016
Near Optimal Methods for Minimizing Convex Functions with Lipschitz -th Derivatives
A Gasnikov, P Dvurechensky, E Gorbunov, E Vorontsova, ...
Conference on Learning Theory, 1392-1393, 2019
Stochastic optimization with heavy-tailed noise via accelerated gradient clipping
E Gorbunov, M Danilova, A Gasnikov
Advances in Neural Information Processing Systems 33, 15042-15053, 2020
Learning supervised pagerank with gradient-based and gradient-free optimization methods
L Bogolubsky, P Dvurechenskii, A Gasnikov, G Gusev, Y Nesterov, ...
Advances in neural information processing systems 29, 2016
Fast primal-dual gradient method for strongly convex minimization problems with linear constraints
A Chernov, P Dvurechensky, A Gasnikov
Discrete Optimization and Operations Research: 9th International Conference …, 2016
Optimal decentralized distributed algorithms for stochastic convex optimization
E Gorbunov, D Dvinskikh, A Gasnikov
arXiv preprint arXiv:1911.07363, 2019
Primal–dual accelerated gradient methods with small-dimensional relaxation oracle
Y Nesterov, A Gasnikov, S Guminov, P Dvurechensky
Optimization Methods and Software 36 (4), 773-810, 2021
Decentralized and parallel primal and dual accelerated methods for stochastic convex programming problems
D Dvinskikh, A Gasnikov
Journal of Inverse and Ill-posed Problems 29 (3), 385-405, 2021
Universal method for stochastic composite optimization problems
AV Gasnikov, YE Nesterov
Computational Mathematics and Mathematical Physics 58, 48-64, 2018
Distributed computation of Wasserstein barycenters over networks
CA Uribe, D Dvinskikh, P Dvurechensky, A Gasnikov, A Nedić
2018 IEEE Conference on Decision and Control (CDC), 6544-6549, 2018
On accelerated alternating minimization
S Guminov, P Dvurechensky, A Gasnikov
Berlin: Weierstraß-Institut für Angewandte Analysis und Stochastik 2695 (2695), 2020
Mirror descent and convex optimization problems with non-smooth inequality constraints
A Bayandina, P Dvurechensky, A Gasnikov, F Stonyakin, A Titov
Large-scale and distributed optimization, 181-213, 2018
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