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
120 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
110 2019 Optimal decentralized distributed algorithms for stochastic convex optimization E Gorbunov, D Dvinskikh, A Gasnikov
arXiv preprint arXiv:1911.07363, 2019
71 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 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
58 2018 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
57 2019 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
43 2021 Decentralized distributed optimization for saddle point problems A Rogozin, A Beznosikov, D Dvinskikh, D Kovalev, P Dvurechensky, ...
arXiv preprint arXiv:2102.07758, 2021
39 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
37 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
34 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
27 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 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
20 2020 Randomized gradient-free methods in convex optimization A Gasnikov, D Dvinskikh, P Dvurechensky, E Gorbunov, A Beznosikov, ...
arXiv preprint arXiv:2211.13566, 2022
17 2022 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
17 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
16 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
12 2022