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Publications in Math-Net.Ru |
Citations |
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2023 |
| 1. |
S. N. Skorik, V. V. Pirau, S. A. Sedov, D. M. Dvinskikh, “Comparsion of stochastic approximation and sample average approximation for saddle point problem with bilinear coupling term”, Computer Research and Modeling, 15:2 (2023), 381–391  |
| 2. |
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”, Zh. Vychisl. Mat. Mat. Fiz., 63:9 (2023), 1458–1512  ; Comput. Math. Math. Phys., 63:9 (2023), 1600–1653 |
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2022 |
| 3. |
D. M. Dvinskikh, V. V. Pirau, A. V. Gasnikov, “On the relations of stochastic convex optimization problems with empirical risk minimization problems on p-norm balls”, Computer Research and Modeling, 14:2 (2022), 309–319 |
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2021 |
| 4. |
A. V. Gasnikov, D. M. Dvinskikh, P. E. Dvurechenskii, D. Kamzolov, V. V. Matyukhin, D. A. Pasechnyuk, N. K. Tupitsa, A. V. Chernov, “Accelerated meta-algorithm for convex optimization problems”, Zh. Vychisl. Mat. Mat. Fiz., 61:1 (2021), 20–31 ; Comput. Math. Math. Phys., 61:1 (2021), 17–28   |
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2020 |
| 5. |
D. M. Dvinskikh, S. S. Omelchenko, A. V. Gasnikov, A. I. Turin, “Accelerated gradient sliding for minimizing a sum of functions”, Dokl. RAN. Math. Inf. Proc. Upr., 492 (2020), 85–88  ; Dokl. Math., 101:3 (2020), 244–246 |
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| 6. |
D. M. Dvinskikh, A. I. Turin, A. V. Gasnikov, S. S. Omelchenko, “Accelerated and Unaccelerated Stochastic Gradient Descent in Model Generality”, Mat. Zametki, 108:4 (2020), 515–528  ; Math. Notes, 108:4 (2020), 511–522 |
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| 7. |
M. S. Alkousa, A. V. Gasnikov, D. M. Dvinskikh, D. A. Kovalev, F. S. Stonyakin, “Accelerated methods for saddle-point problem”, Zh. Vychisl. Mat. Mat. Fiz., 60:11 (2020), 1843–1866 ; Comput. Math. Math. Phys., 60:11 (2020), 1787–1809 |
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