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
71 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
61 2019 Adaptive catalyst for smooth convex optimization A Ivanova, D Pasechnyuk, D Grishchenko, E Shulgin, A Gasnikov, ...
International Conference on Optimization and Applications, 20-37, 2021
37 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 A Damped Newton Method Achieves Global and Local Quadratic Convergence Rate S Hanzely, D Kamzolov, D Pasechnyuk, A Gasnikov, P Richtárik, M Takác
Advances in Neural Information Processing Systems 35, 25320-25334, 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
19 2020 Solving strongly convex-concave composite saddle point problems with a small dimension of one of the variables E Gladin, I Kuruzov, F Stonyakin, D Pasechnyuk, M Alkousa, A Gasnikov
arXiv preprint arXiv:2010.02280, 2020
14 2020 A unified analysis of variational inequality methods: Variance reduction, sampling, quantization, and coordinate descent AN Beznosikov, AV Gasnikov, KE Zainullina, AY Maslovskii, ...
Computational Mathematics and Mathematical Physics 63 (2), 147-174, 2023
9 2023 Non-convex optimization in digital pre-distortion of the signal D Pasechnyuk, A Maslovskiy, A Gasnikov, A Anikin, A Rogozin, A Gornov, ...
arXiv preprint arXiv:2103.10552, 2021
9 * 2021 One Method for Minimization a Convex Lipschitz-Continuous Function of 2 Variables on a Fixed Square DA Pasechnyuk, FS Stonyakin
arXiv preprint arXiv:1812.10300, 2018
8 * 2018 Primal-dual gradient methods for searching network equilibria in combined models with nested choice structure and capacity constraints M Kubentayeva, D Yarmoshik, M Persiianov, A Kroshnin, E Kotliarova, ...
Computational Management Science 21 (1), 15, 2024
7 2024 Gradient-type adaptive methods for relatively Lipschitz convex optimization problems F Stonyakin, A Titov, M Alkousa, O Savchuk, D Pasechnyuk
arXiv preprint.—2021b.—https://arxiv. org/pdf/2107.05765, 2021
7 2021 Adaptive mirror descent for the network utility maximization problem A Ivanova, F Stonyakin, D Pasechnyuk, E Vorontsova, A Gasnikov
IFAC-PapersOnLine 53 (2), 7851-7856, 2020
6 2020 Numerical methods for the resource allocation problem in networks A Ivanova, D Pasechnyuk, P Dvurechensky, A Gasnikov, E Vorontsova
arXiv preprint arXiv:1909.13321, 2019
4 2019 Adaptive variant of the Frank–Wolfe algorithm for convex optimization problems GV Aivazian, FS Stonyakin, DA Pasechnyk, MS Alkousa, AM Raigorodsky, ...
Programming and Computer Software 49 (6), 493-504, 2023
3 2023 Algorithms for Euclidean-Regularised Optimal Transport DA Pasechnyuk, M Persiianov, P Dvurechensky, A Gasnikov
International Conference on Optimization and Applications, 84-98, 2023
3 2023 Upper bounds on maximum admissible noise in zeroth-order optimisation DA Pasechnyuk, A Lobanov, A Gasnikov
arXiv preprint arXiv:2306.16371, 2023
3 2023 Numerical methods for the resource allocation problem in a computer network EA Vorontsova, AV Gasnikov, PE Dvurechensky, AS Ivanova, ...
Computational Mathematics and Mathematical Physics 61, 297-328, 2021
3 2021 Convergence analysis of stochastic gradient descent with adaptive preconditioning for non-convex and convex functions DA Pasechnyuk, A Gasnikov, M Takáč
arXiv preprint arXiv:2308.14192, 2023
2 2023