| Введение в математическое моделирование транспортных потоков А Гасников Litres, 2022 | 597* | 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 | 330 | 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 | 164 | 2020 |
| Современные численные методы оптимизации. Метод универсального градиентного спуска АВ Гасников Федеральное государственное автономное образовательное учреждение высшего …, 2018 | 127 | 2018 |
| 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 | 125 | 2018 |
| 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 | 123 | 2020 |
| 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 | 117 | 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 | 116 | 2019 |
| Стохастические градиентные методы с неточным оракулом АВ Гасников, ПЕ Двуреченский, ЮЕ Нестеров Труды Московского физико-технического института 8 (1 (29)), 41-91, 2016 | 106* | 2016 |
| 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 | 86* | 2016 |
| Universal method for stochastic composite optimization problems AV Gasnikov, YE Nesterov Computational Mathematics and Mathematical Physics 58, 48-64, 2018 | 81 | 2018 |
| 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 | 80 | 2019 |
| Recent theoretical advances in non-convex optimization M Danilova, P Dvurechensky, A Gasnikov, E Gorbunov, S Guminov, ... High-Dimensional Optimization and Probability: With a View Towards Data …, 2022 | 79 | 2022 |
| 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 | 79 | 2016 |
| 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 | 76 | 2021 |
| Optimal decentralized distributed algorithms for stochastic convex optimization E Gorbunov, D Dvinskikh, A Gasnikov arXiv preprint arXiv:1911.07363, 2019 | 74 | 2019 |
| 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 | 69 | 2021 |
| 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 | 67 | 2017 |
| Optimal algorithms for distributed optimization CA Uribe, S Lee, A Gasnikov, A Nedić arXiv preprint arXiv:1712.00232, 2017 | 66 | 2017 |
| 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 | 66 | 2016 |
Pavel DvurechenskyWeierstrass Institute for Applied Analysis and StochasticsVerified email at wias-berlin.de
