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 Dual approaches to the minimization of strongly convex functionals with a simple structure under affine constraints AS Anikin, AV Gasnikov, PE Dvurechensky, AI Tyurin, AV Chernov
Computational Mathematics and Mathematical Physics 57, 1262-1276, 2017
43 2017 Permutation compressors for provably faster distributed nonconvex optimization R Szlendak, A Tyurin, P Richtárik
In International Conference on Learning Representations. 2022. (ICLR 2022), 2021
28 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 DASHA: Distributed nonconvex optimization with communication compression, optimal oracle complexity, and no client synchronization A Tyurin, P Richtárik
In International Conference on Learning Representations. 2023. (ICLR 2023), 2022
20 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 EF21-P and Friends: Improved theoretical communication complexity for distributed optimization with bidirectional compression K Gruntkowska, A Tyurin, P Richtárik
International Conference on Machine Learning, 11761-11807, 2023
19 2023 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 Mirror version of similar triangles method for constrained optimization problems A Tyurin
arXiv preprint arXiv:1705.09809, 2017
15 2017 Fast gradient descent method for convex optimization problems with an oracle that generates a -model of a function in a requested point A Tyurin, A Gasnikov
arXiv preprint arXiv:1711.02747, 2017
13 2017 A heuristic adaptive fast gradient method in stochastic optimization problems AV Ogal’tsov, AI Tyurin
Computational Mathematics and Mathematical Physics 60, 1108-1115, 2020
8 2020 Momentum Provably Improves Error Feedback! I Fatkhullin, A Tyurin, P Richtárik
Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 2023
6 2023 Optimal Time Complexities of Parallel Stochastic Optimization Methods Under a Fixed Computation Model A Tyurin, P Richtárik
Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 2023
4 2023 2Direction: Theoretically Faster Distributed Training with Bidirectional Communication Compression A Tyurin, P Richtárik
Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 2023
3 2023 Sharper rates and flexible framework for nonconvex SGD with client and data sampling A Tyurin, L Sun, K Burlachenko, P Richtárik
Transactions on Machine Learning Research. 2023. (TMLR 2023), 2022
3 2022 A computation and communication efficient method for distributed nonconvex problems in the partial participation setting A Tyurin, P Richtárik
Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 2022
3 2022 Development of a method for solving structural optimization problems A Tyurin
arXiv preprint arXiv:2008.13098, 2020
1 2020 Accelerated and nonaccelerated stochastic gradient descent with model conception D Dvinskikh, A Tyurin, A Gasnikov, S Omelchenko
arXiv preprint arXiv:2001.03443, 2020
1 2020