35 citations to https://www.mathnet.ru/rus/at14682
  1. Aleksandr Lobanov, Lecture Notes in Computer Science, 14395, Optimization and Applications, 2023, 60  crossref
  2. 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)  crossref
  3. Raghu Bollapragada, Stefan M. Wild, “Adaptive sampling quasi-Newton methods for zeroth-order stochastic optimization”, Math. Prog. Comp., 15:2 (2023), 327  crossref
  4. Balasubramanian K., Ghadimi S., “Zeroth-Order Nonconvex Stochastic Optimization: Handling Constraints, High Dimensionality, and Saddle Points”, Found. Comput. Math., 22:1 (2022), 35–76  crossref  isi
  5. 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  mathnet  crossref
  6. Abhishek Roy, Lingqing Shen, Krishnakumar Balasubramanian, Saeed Ghadimi, “Stochastic zeroth-order discretizations of Langevin diffusions for Bayesian inference”, Bernoulli, 28:3 (2022)  crossref
  7. Vasilii Novitskii, Alexander Gasnikov, “Improved exploitation of higher order smoothness in derivative-free optimization”, Optim Lett, 16:7 (2022), 2059  crossref
  8. Darina Dvinskikh, Vladislav Tominin, Iaroslav Tominin, Alexander Gasnikov, Lecture Notes in Computer Science, 13367, Mathematical Optimization Theory and Operations Research, 2022, 18  crossref
  9. Eduard Gorbunov, Pavel Dvurechensky, Alexander Gasnikov, “An Accelerated Method for Derivative-Free Smooth Stochastic Convex Optimization”, SIAM J. Optim., 32:2 (2022), 1210  crossref
  10. 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  crossref
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