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Stonyakin Fedor Sergeevich

Publications in Math-Net.Ru

  1. Intermediate gradient methods with relative inexactness

    J. Optim. Theory Appl., 207 (2025),  62–42
  2. Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems

    Uspekhi Mat. Nauk, 80:6(486) (2025),  137–172
  3. 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
  4. An adaptive variant of the Frank–Wolfe method for relative smooth convex optimization problems

    Zh. Vychisl. Mat. Mat. Fiz., 65:3 (2025),  364–375
  5. Subgradient methods for weakly convex problems with a sharp minimum in the case of inexact information about the function or subgradient

    Computer Research and Modeling, 16:7 (2024),  1765–1778
  6. On some mirror descent methods for strongly convex programming problems with Lipschitz functional constraints

    Computer Research and Modeling, 16:7 (2024),  1727–1746
  7. 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
  8. On Quasi-Convex Smooth Optimization Problems by a Comparison Oracle

    Rus. J. Nonlin. Dyn., 20:5 (2024),  813–825
  9. Mirror Descent Methods with a Weighting Scheme for Outputs for Optimization Problems with Functional Constraints

    Rus. J. Nonlin. Dyn., 20:5 (2024),  727–745
  10. Highly smooth zeroth-order methods for solving optimization problems under the PL condition

    Zh. Vychisl. Mat. Mat. Fiz., 64:4 (2024),  739–770
  11. On some works of Boris Teodorovich Polyak on the convergence of gradient methods and their development

    Zh. Vychisl. Mat. Mat. Fiz., 64:4 (2024),  587–626
  12. 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
  13. Subgradient methods for weakly convex and relatively weakly convex problems with a sharp minimum

    Computer Research and Modeling, 15:2 (2023),  393–412
  14. Solving strongly convex-concave composite saddle-point problems with low dimension of one group of variable

    Mat. Sb., 214:3 (2023),  3–53
  15. Adaptive Subgradient Methods for Mathematical Programming Problems with Quasiconvex Functions

    Trudy Inst. Mat. i Mekh. UrO RAN, 29:3 (2023),  7–25
  16. Subgradient methods for non-smooth optimization problems with some relaxation of sharp minimum

    Computer Research and Modeling, 14:2 (2022),  473–495
  17. Adaptive first-order methods for relatively strongly convex optimization problems

    Computer Research and Modeling, 14:2 (2022),  445–472
  18. Numerical Methods for Some Classes of Variational Inequalities with Relatively Strongly Monotone Operators

    Mat. Zametki, 112:6 (2022),  879–894
  19. Adaptive gradient-type methods for optimization problems with relative error and sharp minimum

    Trudy Inst. Mat. i Mekh. UrO RAN, 27:4 (2021),  175–188
  20. Mirror descent for constrained optimization problems with large subgradient values of functional constraints

    Computer Research and Modeling, 12:2 (2020),  301–317
  21. On some algorithms for constrained optimization problems with relative accuracy with respect to the objective functional

    Trudy Inst. Mat. i Mekh. UrO RAN, 26:3 (2020),  198–210
  22. Accelerated methods for saddle-point problem

    Zh. Vychisl. Mat. Mat. Fiz., 60:11 (2020),  1843–1866
  23. One method for minimization a convex Lipschitz-continuous function of two variables on a fixed square

    Computer Research and Modeling, 11:3 (2019),  379–395
  24. Adaptive mirror descent algorithms for convex and strongly convex optimization problems with functional constraints

    Diskretn. Anal. Issled. Oper., 26:3 (2019),  88–114
  25. Hahn–Banach type theorems on functional separation for convex ordered normed cones

    Eurasian Math. J., 10:1 (2019),  59–79
  26. An adaptive analog of Nesterov's method for variational inequalities with a strongly monotone operator

    Sib. Zh. Vychisl. Mat., 22:2 (2019),  201–211
  27. Adaptation to inexactness for some gradient-type optimization methods

    Trudy Inst. Mat. i Mekh. UrO RAN, 25:4 (2019),  210–225
  28. On the Adaptive Proximal Method for a Class of Variational Inequalities and Related Problems

    Trudy Inst. Mat. i Mekh. UrO RAN, 25:2 (2019),  185–197
  29. An adaptive proximal method for variational inequalities

    Zh. Vychisl. Mat. Mat. Fiz., 59:5 (2019),  889–894
  30. A Sublinear Analog of the Banach–Mazur Theorem in Separable Convex Cones with Norm

    Mat. Zametki, 104:1 (2018),  118–130
  31. On Sublinear Analogs of Weak Topologies in Normed Cones

    Mat. Zametki, 103:5 (2018),  794–800
  32. Adaptive mirror descent algorithms in convex programming problems with Lipschitz constraints

    Trudy Inst. Mat. i Mekh. UrO RAN, 24:2 (2018),  266–279
  33. On some problem of set-valued analysis in asymmetric normed spaces

    Taurida Journal of Computer Science Theory and Mathematics, 2017, no. 1,  82–94
  34. An analogue of the Hahn–Banach theorem for functionals on abstract convex cones

    Eurasian Math. J., 7:3 (2016),  89–99
  35. Analogs of the Schauder Theorem that Use Anticompacta

    Mat. Zametki, 99:6 (2016),  950–953
  36. Sequential analogues of the Lyapunov and Krein–Milman theorems in Fréchet spaces

    CMFD, 57 (2015),  162–183
  37. Applications of anticompact sets to analogs of Denjoy–Young–Saks and Lebesgue theorems

    Eurasian Math. J., 6:1 (2015),  115–122
  38. Anti-compacts and their applications to analogs of Lyapunov and Lebesgue theorems in Frechét spaces

    CMFD, 53 (2014),  155–176
  39. The limiting form of the Radon–Nikodym property is true for all Fréchet spaces

    CMFD, 37 (2010),  55–69
  40. Compact subdifferentials: the formula of finite increments and related topics

    CMFD, 34 (2009),  121–138

© Steklov Math. Inst. of RAS, 2026