Введение в математическое моделирование транспортных потоков А Гасников
Litres, 2022
546 * 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
268 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
150 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
109 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
106 2016 Современные численные методы оптимизации. Метод универсального градиентного спуска АВ Гасников
Федеральное государственное автономное образовательное учреждение высшего …, 2018
103 2018 Стохастические градиентные методы с неточным оракулом АВ Гасников, ПЕ Двуреченский, ЮЕ Нестеров
Труды Московского физико-технического института 8 (1 (29)), 41-91, 2016
103 * 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
101 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
83 * 2016 Near Optimal Methods for Minimizing Convex Functions with Lipschitz A Gasnikov, P Dvurechensky, E Gorbunov, E Vorontsova, ...
Conference on Learning Theory, 1392-1393, 2019
74 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
71 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
70 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
67 2016 Optimal decentralized distributed algorithms for stochastic convex optimization E Gorbunov, D Dvinskikh, A Gasnikov
arXiv preprint arXiv:1911.07363, 2019
66 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
65 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
65 2021 Universal method for stochastic composite optimization problems AV Gasnikov, YE Nesterov
Computational Mathematics and Mathematical Physics 58, 48-64, 2018
62 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
59 2018 On accelerated alternating minimization S Guminov, P Dvurechensky, A Gasnikov
Berlin: Weierstraß-Institut für Angewandte Analysis und Stochastik 2695 (2695), 2020
58 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
57 2018 
Pavel DvurechenskyWeierstrass Institute for Applied Analysis and StochasticsПодтвержден адрес электронной почты в домене wias-berlin.de
