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
Современные численные методы оптимизации. Метод универсального градиентного спуска
АВ Гасников
Федеральное государственное автономное образовательное учреждение высшего …, 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
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
Стохастические градиентные методы с неточным оракулом
АВ Гасников, ПЕ Двуреченский, ЮЕ Нестеров
Труды Московского физико-технического института 8 (1 (29)), 41-91, 2016
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
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
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
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
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
Universal method for stochastic composite optimization problems
AV Gasnikov, YE Nesterov
Computational Mathematics and Mathematical Physics 58, 48-64, 2018
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
Stochastic online optimization. Single-point and multi-point non-linear multi-armed bandits. Convex and strongly-convex case
AV Gasnikov, EA Krymova, AA Lagunovskaya, IN Usmanova, ...
Automation and remote control 78, 224-234, 2017
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
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