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 | 122 | 2018 |
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 | 115 | 2019 |
Optimal decentralized distributed algorithms for stochastic convex optimization E Gorbunov, D Dvinskikh, A Gasnikov arXiv preprint arXiv:1911.07363, 2019 | 72 | 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 |
Gradient methods for problems with inexact model of the objective FS Stonyakin, D Dvinskikh, P Dvurechensky, A Kroshnin, O Kuznetsova, ... Mathematical Optimization Theory and Operations Research: 18th International …, 2019 | 59 | 2019 |
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 | 57 | 2018 |
Inexact model: A framework for optimization and variational inequalities F Stonyakin, A Tyurin, A Gasnikov, P Dvurechensky, A Agafonov, ... Optimization Methods and Software 36 (6), 1155-1201, 2021 | 50 | 2021 |
Decentralized distributed optimization for saddle point problems A Rogozin, A Beznosikov, D Dvinskikh, D Kovalev, P Dvurechensky, ... arXiv preprint arXiv:2102.07758, 2021 | 42 | 2021 |
Recent theoretical advances in decentralized distributed convex optimization E Gorbunov, A Rogozin, A Beznosikov, D Dvinskikh, A Gasnikov High-Dimensional Optimization and Probability: With a View Towards Data …, 2022 | 39 | 2022 |
Accelerated methods for saddle-point problem MS Alkousa, AV Gasnikov, DM Dvinskikh, DA Kovalev, FS Stonyakin Computational Mathematics and Mathematical Physics 60, 1787-1809, 2020 | 38 | 2020 |
On primal and dual approaches for distributed stochastic convex optimization over networks D Dvinskikh, E Gorbunov, A Gasnikov, P Dvurechensky, CA Uribe 2019 IEEE 58th Conference on Decision and Control (CDC), 7435-7440, 2019 | 31 | 2019 |
Accelerated methods for composite non-bilinear saddle point problem M Alkousa, D Dvinskikh, F Stonyakin, A Gasnikov, D Kovalev arXiv preprint arXiv:1906.03620, 2019 | 29 | 2019 |
Improved complexity bounds in wasserstein barycenter problem D Dvinskikh, D Tiapkin International conference on artificial intelligence and statistics, 1738-1746, 2021 | 28 | 2021 |
Accelerated meta-algorithm for convex optimization problems AV Gasnikov, DM Dvinskikh, PE Dvurechensky, DI Kamzolov, ... Computational Mathematics and Mathematical Physics 61, 17-28, 2021 | 26 | 2021 |
Randomized gradient-free methods in convex optimization A Gasnikov, D Dvinskikh, P Dvurechensky, E Gorbunov, A Beznosikov, ... Encyclopedia of Optimization, 1-15, 2023 | 21 | 2023 |
Oracle complexity separation in convex optimization A Ivanova, P Dvurechensky, E Vorontsova, D Pasechnyuk, A Gasnikov, ... Journal of Optimization Theory and Applications 193 (1), 462-490, 2022 | 21 | 2022 |
Inexact relative smoothness and strong convexity for optimization and variational inequalities by inexact model F Stonyakin, A Tyurin, A Gasnikov, P Dvurechensky, A Agafonov, ... arXiv preprint arXiv:2001.09013, 2020 | 19 | 2020 |
Adaptive gradient descent for convex and non-convex stochastic optimization D Dvinskikh, A Ogaltsov, A Gasnikov, P Dvurechensky, A Tyurin, ... arXiv preprint arXiv:1911.08380, 2019 | 19 | 2019 |
Inexact model: A framework for optimization and variational inequalities F Stonyakin, A Gasnikov, A Tyurin, D Pasechnyuk, A Agafonov, ... arXiv preprint arXiv:1902.00990, 2019 | 15 | 2019 |
Gradient-Free Federated Learning Methods with and -Randomization for Non-Smooth Convex Stochastic Optimization Problems A Lobanov, B Alashqar, D Dvinskikh, A Gasnikov arXiv preprint arXiv:2211.10783, 2022 | 13 | 2022 |