Pavel Dvurechensky, Petr Ostroukhov, Alexander Gasnikov, César A. Uribe, Anastasiya Ivanova, “Near-optimal tensor methods for minimizing the gradient norm of convex functions and accelerated primal–dual tensor methods”, Optimization Methods and Software, 2024, 1
Vladimir Krutikov, Elena Tovbis, Anatoly Bykov, Predrag Stanimirovic, Ekaterina Chernova, Lev Kazakovtsev, “Properties of the Quadratic Transformation of Dual Variables”, Algorithms, 16:3 (2023), 148
José A. Vásquez-Coronel, Marco Mora, Karina Vilches, “A Review of multilayer extreme learning machine neural networks”, Artif Intell Rev, 56:11 (2023), 13691
Muhammad Adil Khan, Asadullah Sohail, Hidayat Ullah, Tareq Saeed, “Estimations of the Jensen Gap and Their Applications Based on 6-Convexity”, Mathematics, 11:8 (2023), 1957
Tiapkin D., Gasnikov A., Dvurechensky P., “Stochastic Saddle-Point Optimization For the Wasserstein Barycenter Problem”, Optim. Lett., 16:7 (2022), 2145–2175
Eduard Gorbunov, Alexander Rogozin, Aleksandr Beznosikov, Darina Dvinskikh, Alexander Gasnikov, Springer Optimization and Its Applications, 191, High-Dimensional Optimization and Probability, 2022, 253
N. Tupitsa, P. Dvurechensky, A. Gasnikov, S. Guminov, “Alternating minimization methods for strongly convex optimization”, J. Inverse Ill-Posed Probl., 29:5 (2021), 721–739
S. Guminov, P. Dvurechensky, N. Tupitsa, A. Gasnikov, “On a combination of alternating minimization and Nesterov's momentum”, International Conference on Machine Learning, Proceedings of Machine Learning Research, 139, eds. M. Meila, T. Zhang, Jmlr-Journal Machine Learning Research, 2021
P. Dvurechensky, Sh. Shtern, M. Staudigl, “First-order methods for convex optimization”, EURO J. Comput. Optim., 9 (2021), 100015
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