Yuanhanqing Huang, Jianghai Hu, “Zeroth-Order Learning in Continuous Games via Residual Pseudogradient Estimates”, IEEE Trans. Automat. Contr., 70:4 (2025), 2258
Aleksandr Beznosikov, Ivan Stepanov, Artyom Voronov, Alexander Gasnikov, “One-point feedback for composite optimization with applications to distributed and federated learning”, Optimization Methods and Software, 2025, 1
Yan Zhang, Michael M. Zavlanos, “Cooperative Multiagent Reinforcement Learning With Partial Observations”, IEEE Trans. Automat. Contr., 69:2 (2024), 968
A. V. Gasnikov, A. V. Lobanov, F. S. Stonyakin, “Highly Smooth Zeroth-Order Methods for Solving Optimization Problems under the PL Condition”, Comput. Math. and Math. Phys., 64:4 (2024), 739
Wouter Jongeneel, Man-Chung Yue, Daniel Kuhn, “Small Errors in Random Zeroth-Order Optimization Are Imaginary”, SIAM J. Optim., 34:3 (2024), 2638
Alexander Gasnikov, Darina Dvinskikh, Pavel Dvurechensky, Eduard Gorbunov, Aleksandr Beznosikov, Alexander Lobanov, Encyclopedia of Optimization, 2024, 1
Yan Zhang, Yi Zhou, Kaiyi Ji, Yi Shen, Michael M. Zavlanos, “Boosting One-Point Derivative-Free Online Optimization via Residual Feedback”, IEEE Trans. Automat. Contr., 69:9 (2024), 6309
Andrey Veprikov, Alexander Bogdanov, Vladislav Minashkin, Aleksandr Beznosikov, “New aspects of black box conditional gradient: Variance reduction and one point feedback”, Chaos, Solitons & Fractals, 189 (2024), 115654
G. K. Bychkov, D. M. Dvinskikh, A. V. Antsiferova, A. V. Gasnikov, A. V. Lobanov, “Accelerated Zero-Order SGD under High-Order Smoothness and Overparameterized Regime”, Rus. J. Nonlin. Dyn., 20:5 (2024), 759–788
Pavel Dvurechensky, Alexander Gasnikov, Alexander Tyurin, Vladimir Zholobov, Springer Proceedings in Mathematics & Statistics, 425, Foundations of Modern Statistics, 2023, 511
Б. А. Альашкар, А. В. Гасников, Д. М. Двинских, А. В. Лобанов, “Безградиентные методы федеративного обучения с $l_1$ и $l_2$-рандомизацией для задач негладкой выпуклой стохастической оптимизации”, Ж. вычисл. матем. и матем. физ., 63:9 (2023), 1458–1512; B. A. Alashkar, A. V. Gasnikov, D. M. Dvinskikh, A. V. Lobanov, “Gradient-free federated learning methods with $l_1$ and $l_2$-randomization for non-smooth convex stochastic optimization problems”, Comput. Math. Math. Phys., 63:9 (2023), 1600–1653
Oleg Savchuk, Fedor Stonyakin, Mohammad Alkousa, Rida Zabirova, Alexander Titov, Alexander Gasnikov, Communications in Computer and Information Science, 1881, Mathematical Optimization Theory and Operations Research: Recent Trends, 2023, 29
Aleksandr Lobanov, Andrew Veprikov, Georgiy Konin, Aleksandr Beznosikov, Alexander Gasnikov, Dmitry Kovalev, “Non-smooth setting of stochastic decentralized convex optimization problem over time-varying Graphs”, Comput Manag Sci, 20:1 (2023)
Aleksandr Lobanov, Lecture Notes in Computer Science, 14395, Optimization and Applications, 2023, 60
Nikita Kornilov, Alexander Gasnikov, Pavel Dvurechensky, Darina Dvinskikh, “Gradient-free methods for non-smooth convex stochastic optimization with heavy-tailed noise on convex compact”, Comput Manag Sci, 20:1 (2023)
Raghu Bollapragada, Stefan M. Wild, “Adaptive sampling quasi-Newton methods for zeroth-order stochastic optimization”, Math. Prog. Comp., 15:2 (2023), 327
Balasubramanian K., Ghadimi S., “Zeroth-Order Nonconvex Stochastic Optimization: Handling Constraints, High Dimensionality, and Saddle Points”, Found. Comput. Math., 22:1 (2022), 35–76
A. I. Bazarova, A. N. Beznosikov, A. V. Gasnikov, “Linearly convergent gradient-free methods for minimization of parabolic approximation”, Компьютерные исследования и моделирование, 14:2 (2022), 239–255
Abhishek Roy, Lingqing Shen, Krishnakumar Balasubramanian, Saeed Ghadimi, “Stochastic zeroth-order discretizations of Langevin diffusions for Bayesian inference”, Bernoulli, 28:3 (2022)
Vasilii Novitskii, Alexander Gasnikov, “Improved exploitation of higher order smoothness in derivative-free optimization”, Optim Lett, 16:7 (2022), 2059
Darina Dvinskikh, Vladislav Tominin, Iaroslav Tominin, Alexander Gasnikov, Lecture Notes in Computer Science, 13367, Mathematical Optimization Theory and Operations Research, 2022, 18
Eduard Gorbunov, Pavel Dvurechensky, Alexander Gasnikov, “An Accelerated Method for Derivative-Free Smooth Stochastic Convex Optimization”, SIAM J. Optim., 32:2 (2022), 1210
Yan Zhang, Yi Zhou, Kaiyi Ji, Michael M. Zavlanos, “A new one-point residual-feedback oracle for black-box learning and control”, Automatica, 136 (2022), 110006
P. Dvurechensky, E. Gorbunov, A. Gasnikov, “An accelerated directional derivative method for smooth stochastic convex optimization”, Eur. J. Oper. Res., 290:2 (2021), 601–621
R. Bollapragada, M. Menickelly, W. Nazarewicz, J. O'Neal, P.-G. Reinhard, S. M. Wild, “Optimization and supervised machine learning methods for fitting numerical physics models without derivatives”, J. Phys. G-Nucl. Part. Phys., 48:2 (2021), 024001
Aleksandr Beznosikov, Vasilii Novitskii, Alexander Gasnikov, Lecture Notes in Computer Science, 12755, Mathematical Optimization Theory and Operations Research, 2021, 144
X. Luo, Ya. Zhang, M. M. Zavlanos, “Socially-aware robot planning via bandit human feedback”, 2020 Acm/IEEE 11Th International Conference on Cyber-Physical Systems (Iccps 2020), Acm-IEEE International Conference on Cyber-Physical Systems, IEEE Computer Soc, 2020, 216–225
X. Yi, X. Li, T. Yang, L. Xie, T. Chai, K. H. Johansson, “A distributed primal-dual algorithm for bandit online convex optimization with time-varying coupled inequality constraints”, 2020 American Control Conference (Acc), Proceedings of the American Control Conference, IEEE, 2020, 327–332
Alexander A. Titov, Fedor S. Stonyakin, Mohammad S. Alkousa, Seydamet S. Ablaev, Alexander V. Gasnikov, Communications in Computer and Information Science, 1275, Mathematical Optimization Theory and Operations Research, 2020, 133
Xinlei Yi, Xiuxian Li, Tao Yang, Lihua Xie, Tianyou Chai, Karl H. Johansson, 2020 American Control Conference (ACC), 2020, 327
Aleksandr Beznosikov, Abdurakhmon Sadiev, Alexander Gasnikov, Communications in Computer and Information Science, 1275, Mathematical Optimization Theory and Operations Research, 2020, 105
Е. А. Воронцова, А. В. Гасников, Э. А. Горбунов, “Ускоренный спуск по случайному направлению с неевклидовой прокс-структурой”, Автомат. и телемех., 2019, № 4, 126–143
M. S. Alkousa, “On some stochastic mirror descent methods for constrained online optimization problems”, Компьютерные исследования и моделирование, 11:2 (2019), 205–217
J. Larson, M. Menickelly, S. M. Wild, “Derivative-free optimization methods”, Acta Numer., 28 (2019), 287–404
Alexander A. Titov, Fedor S. Stonyakin, Alexander V. Gasnikov, Mohammad S. Alkousa, Communications in Computer and Information Science, 974, Optimization and Applications, 2019, 64
E. A. Vorontsova, A. V. Gasnikov, E. A. Gorbunov, “Accelerated Directional Search with Non-Euclidean Prox-Structure”, Autom Remote Control, 80:4 (2019), 693
А. С. Баяндина, А. В. Гасников, А. А. Лагуновская, “Безградиентные двухточечные методы решения задач стохастической негладкой выпуклой оптимизации при наличии малых шумов не случайной природы”, Автомат. и телемех., 2018, № 8, 38–49; A. S. Bayandina, A. V. Gasnikov, A. A. Lagunovskaya, “Gradient-free two-point methods for solving stochastic nonsmooth convex optimization problems with small non-random noises”, Autom. Remote Control, 79:8 (2018), 1399–1408
А. С. Баяндина, А. В. Гасников, Е. В. Гасникова, С. В. Мациевский, “Прямо-двойственный метод зеркального спуска для условных задач стохастической оптимизации”, Ж. вычисл. матем. и матем. физ., 58:11 (2018), 1794–1803; A. S. Bayandina, A. V. Gasnikov, E. V. Gasnikova, S. V. Matsievskii, “Primal-dual mirror descent method for constraint stochastic optimization problems”, Comput. Math. Math. Phys., 58:11 (2018), 1728–1736
