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Publications in Math-Net.Ru |
Citations |
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2025 |
| 1. |
S. S. Ablaev, F. S. Stonyakin, M. N. Fedotov, M. S. Alkousa, O. S. Savchuk, A. V. Gasnikov, “Study of gradient method with inexact gradient information on some classes of $(L_0, L_1)$-smooth non-convex problems”, Avtomat. i Telemekh., 2025, no. 9, 3–27  ; Autom. Remote Control, 86:9 (2025), 797–816 |
| 2. |
Nikita Kornilov, Mohammad Alkousa, Eduard Gorbunov, Fedor Stonyakin, Pavel Dvurechensky, Alexander Gasnikov, “Intermediate gradient methods with relative inexactness”, J. Optim. Theory Appl., 207 (2025), 62–42   |
| 3. |
O. S. Savchuk, M. S. Alkousa, A. I. Shushko, A. A. Vyguzov, F. S. Stonyakin, D. A. Pasechnyuk, A. V. Gasnikov, “Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems”, Uspekhi Mat. Nauk, 80:6(486) (2025), 137–172 ; Russian Math. Surveys, 80:6 (2025), 1067–1102  |
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| 4. |
O. S. Savchuk, F. S. Stonyakin, A. A. Vyguzov, M. S. Alkousa, A. V. Gasnikov, “Adaptive primal-dual methods with an inexact oracle for relatively smooth optimization problems and their applications to recovering low-rank matrices”, Zh. Vychisl. Mat. Mat. Fiz., 65:7 (2025), 1156–1177  ; Comput. Math. Math. Phys., 65:7 (2025), 1605–1627 |
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2024 |
| 5. |
O. S. Savchuk, M. S. Alkousa, F. S. Stonyakin, “On some mirror descent methods for strongly convex programming problems with Lipschitz functional constraints”, Computer Research and Modeling, 16:7 (2024), 1727–1746 |
| 6. |
S. M. Puchinin, E. R. Korolkov, F. S. Stonyakin, M. S. Alkousa, A. A. Vyguzov, “Subgradient methods with B.T. Polyak-type step for quasiconvex minimization problems with inequality constraints and analogs of the sharp minimum”, Computer Research and Modeling, 16:1 (2024), 105–122 |
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| 7. |
A. V. Gasnikov, M. S. Alkousa, A. V. Lobanov, Y. V. Dorn, F. S. Stonyakin, I. A. Kuruzov, S. R. Singh, “On Quasi-Convex Smooth Optimization Problems by a Comparison Oracle”, Rus. J. Nonlin. Dyn., 20:5 (2024), 813–825 |
| 8. |
M. S. Alkousa, F. S. Stonyakin, A. M. Abdo, M. M. Alcheikh, “Mirror Descent Methods with a Weighting Scheme for Outputs for Optimization Problems with Functional Constraints”, Rus. J. Nonlin. Dyn., 20:5 (2024), 727–745 |
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2023 |
| 9. |
F. S. Stonyakin, O. S. Savchuk, I. V. Baran, M. S. Alkousa, A. A. Titov, “Analogues of the relative strong convexity condition for relatively smooth problems and adaptive gradient-type methods”, Computer Research and Modeling, 15:2 (2023), 413–432 |
| 10. |
F. S. Stonyakin, S. S. Ablaev, I. V. Baran, M. S. Alkousa, “Subgradient methods for weakly convex and relatively weakly convex problems with a sharp minimum”, Computer Research and Modeling, 15:2 (2023), 393–412 |
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| 11. |
M. S. Alkousa, A. V. Gasnikov, E. L. Gladin, I. A. Kuruzov, D. A. Pasechnyuk, F. S. Stonyakin, “Solving strongly convex-concave composite saddle-point problems with low dimension of one group of variable”, Mat. Sb., 214:3 (2023), 3–53  ; Sb. Math., 214:3 (2023), 285–333 |
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| 12. |
S. S. Ablaev, F. S. Stonyakin, M. S. Alkousa, A. V. Gasnikov, “Adaptive Subgradient Methods for Mathematical Programming Problems with Quasiconvex Functions”, Trudy Inst. Mat. i Mekh. UrO RAN, 29:3 (2023), 7–25 ; Proc. Steklov Inst. Math., 323: suppl. 1 (2023), S1–S18 |
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2022 |
| 13. |
S. S. Ablaev, D. V. Makarenko, F. S. Stonyakin, M. S. Alkousa, I. V. Baran, “Subgradient methods for non-smooth optimization problems with some relaxation of sharp minimum”, Computer Research and Modeling, 14:2 (2022), 473–495 |
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| 14. |
O. S. Savchuk, A. A. Titov, F. S. Stonyakin, M. S. Alkousa, “Adaptive first-order methods for relatively strongly convex optimization problems”, Computer Research and Modeling, 14:2 (2022), 445–472 |
| 15. |
M. S. Alkousa, A. V. Gasnikov, P. E. Dvurechenskii, A. A. Sadiev, L. Ya. Razouk, “An approach for the nonconvex uniformly concave structured saddle point problem”, Computer Research and Modeling, 14:2 (2022), 225–237 |
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| 16. |
F. S. Stonyakin, A. A. Titov, D. V. Makarenko, M. S. Alkousa, “Numerical Methods for Some Classes of Variational Inequalities with Relatively Strongly Monotone Operators”, Mat. Zametki, 112:6 (2022), 879–894 ; Math. Notes, 112:6 (2022), 965–977 |
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2021 |
| 17. |
E. L. Gladin, M. Alkousa, A. V. Gasnikov, “Solving convex min-min problems with smoothness and strong convexity in one group of variables and low dimension in the other”, Avtomat. i Telemekh., 2021, no. 10, 60–75 ; Autom. Remote Control, 82:10 (2021), 1679–1691 |
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2020 |
| 18. |
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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2019 |
| 19. |
M. S. Alkousa, “On some stochastic mirror descent methods for constrained online optimization problems”, Computer Research and Modeling, 11:2 (2019), 205–217 |
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| 20. |
F. S. Stonyakin, M. Alkousa, A. N. Stepanov, A. A. Titov, “Adaptive mirror descent algorithms for convex and strongly convex optimization problems with functional constraints”, Diskretn. Anal. Issled. Oper., 26:3 (2019), 88–114 ; J. Appl. Industr. Math., 13:3 (2019), 557–574 |
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2018 |
| 21. |
F. S. Stonyakin, M. S. Alkousa, A. N. Stepanov, M. A. Barinov, “Adaptive mirror descent algorithms in convex programming problems with Lipschitz constraints”, Trudy Inst. Mat. i Mekh. UrO RAN, 24:2 (2018), 266–279 |
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| Presentations in Math-Net.Ru |
| 1. |
Gradient-Type Method for Optimization Problems with Polyak-Lojasiewicz Condition: Relative Inexactness in Gradient and Adaptive Parameters Setting
S. M. Puchinin, F. S. Stonyakin, M. S. Alkousa
The ninth international conference "Quasilinear Equations, Inverse Problems and their Applications" (QIPA 2023)
December 4, 2023 18:05
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| 2. |
Online optimization problems with functional constraints under relative Lipschitz continuity and relative strong convexity conditions
O. S. Savchuk, A. A. Titov, A. V. Gasnikov, F. S. Stonyakin, M. S. Alkousa, R. Zabirova
The ninth international conference "Quasilinear Equations, Inverse Problems and their Applications" (QIPA 2023)
December 4, 2023 17:40
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| 3. |
Adaptive subgradient methods for mathematical programming problems with quasi-convex functions
S. S. Ablaev, F. S. Stonyakin, M. S. Alkousa, A. V. Gasnikov
The ninth international conference "Quasilinear Equations, Inverse Problems and their Applications" (QIPA 2023)
December 4, 2023 15:00
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