Randomized gradient-free methods in convex optimization A Gasnikov, D Dvinskikh, P Dvurechensky, E Gorbunov, A Beznosikov, ...
Encyclopedia of Optimization, 1-15, 2023
29 2023 Gradient-Free Federated Learning Methods with A Lobanov, B Alashqar, D Dvinskikh, A Gasnikov
arXiv preprint arXiv:2211.10783, 2022
22 * 2022 Mini-batch adaptive random search method for the parametric identification of dynamic systems AV Panteleev, AV Lobanov
Automation and Remote Control 81 (11), 2026-2045, 2020
16 * 2020 Accelerated zeroth-order method for non-smooth stochastic convex optimization problem with infinite variance N Kornilov, O Shamir, A Lobanov, D Dvinskikh, A Gasnikov, I Shibaev, ...
Advances in Neural Information Processing Systems 36, 2024
14 * 2024 Highly smoothness zero-order methods for solving optimization problems under pl condition A Lobanov, A Gasnikov, F Stonyakin
arXiv preprint arXiv:2305.15828, 2023
14 * 2023 Градиентные методы оптимизации в машинном обучении идентификации параметров динамических систем АВ Пантелеев, АВ Лобанов
Моделирование и анализ данных 9 (4), 88-99, 2019
13 * 2019 Zero-Order Stochastic Conditional Gradient Sliding Method for Non-smooth Convex Optimization A Lobanov, A Anikin¹, A Gasnikov, A Gornov¹, S Chukanov
Mathematical Optimization Theory and Operations Research: Recent Trends …, 2023
11 2023 Accelerated zero-order sgd method for solving the black box optimization problem under “overparametrization” condition A Lobanov, A Gasnikov
International Conference on Optimization and Applications, 72-83, 2023
10 2023 Stochastic adversarial noise in the “black box” optimization problem A Lobanov
International Conference on Optimization and Applications, 60-71, 2023
10 2023 The “Black-Box” Optimization Problem: Zero-Order Accelerated Stochastic Method via Kernel Approximation A Lobanov, N Bashirov, A Gasnikov
Journal of Optimization Theory and Applications, 1-36, 2024
8 2024 Acceleration exists! optimization problems when oracle can only compare objective function values A Lobanov, A Gasnikov, A Krasnov
The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024
8 * 2024 Non-smooth setting of stochastic decentralized convex optimization problem over time-varying graphs A Lobanov, A Veprikov, G Konin, A Beznosikov, A Gasnikov, D Kovalev
Computational Management Science 20 (1), 48, 2023
8 2023 The mini-batch adaptive method of random search (MAMRS) for parameters optimization in the tracking control problem AV Panteleev, AV Lobanov
IOP Conference Series: Materials Science and Engineering 927 (1), 012025, 2020
8 2020 Application of Mini-Batch Metaheuristic algorithms in problems of optimization of deterministic systems with incomplete information about the state vector AV Panteleev, AV Lobanov
Algorithms 14 (11), 332, 2021
6 2021 Application of the mini-batch adaptive method of random search (MAMRS) in problems of optimal in mean control of the trajectory pencils AV Panteleev, AV Lobanov
Journal of Physics: Conference Series 1925 (1), 012006, 2021
6 2021 On Some Works of Boris Teodorovich Polyak on the Convergence of Gradient Methods and Their Development SS Ablaev, AN Beznosikov, AV Gasnikov, DM Dvinskikh, AV Lobanov, ...
Computational Mathematics and Mathematical Physics 64 (4), 635-675, 2024
3 * 2024 Upper bounds on maximum admissible noise in zeroth-order optimisation DA Pasechnyuk, A Lobanov, A Gasnikov
arXiv preprint arXiv:2306.16371, 2023
3 2023 Gradient-free algorithm for saddle point problems under overparametrization E Statkevich, S Bondar, D Dvinskikh, A Gasnikov, A Lobanov
Chaos, Solitons & Fractals 185, 115048, 2024
2 2024 Gradient-Free Algorithms for Solving Stochastic Saddle Optimization Problems with the Polyak–Łojasiewicz Condition SI Sadykov, AV Lobanov, AM Raigorodskii
Programming and Computer Software 49 (6), 535-547, 2023
2 2023 Linear Convergence Rate in Convex Setup is Possible! Gradient Descent Method Variants under A Lobanov, A Gasnikov, E Gorbunov, M Takác
arXiv preprint arXiv:2412.17050, 2024
1 2024 
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