Alkousa Mohammad Soud

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. On some mirror descent methods for strongly convex programming problems with Lipschitz functional constraints
   
   Computer Research and Modeling, 16:7 (2024),  1727–1746
5. 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
6. On Quasi-Convex Smooth Optimization Problems by a Comparison Oracle
   
   Rus. J. Nonlin. Dyn., 20:5 (2024),  813–825
7. Mirror Descent Methods with a Weighting Scheme for Outputs for Optimization Problems with Functional Constraints
   
   Rus. J. Nonlin. Dyn., 20:5 (2024),  727–745
8. 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
9. Subgradient methods for weakly convex and relatively weakly convex problems with a sharp minimum
   
   Computer Research and Modeling, 15:2 (2023),  393–412
10. Solving strongly convex-concave composite saddle-point problems with low dimension of one group of variable
    
    Mat. Sb., 214:3 (2023),  3–53
11. Adaptive Subgradient Methods for Mathematical Programming Problems with Quasiconvex Functions
    
    Trudy Inst. Mat. i Mekh. UrO RAN, 29:3 (2023),  7–25
12. Subgradient methods for non-smooth optimization problems with some relaxation of sharp minimum
    
    Computer Research and Modeling, 14:2 (2022),  473–495
13. Adaptive first-order methods for relatively strongly convex optimization problems
    
    Computer Research and Modeling, 14:2 (2022),  445–472
14. An approach for the nonconvex uniformly concave structured saddle point problem
    
    Computer Research and Modeling, 14:2 (2022),  225–237
15. Numerical Methods for Some Classes of Variational Inequalities with Relatively Strongly Monotone Operators
    
    Mat. Zametki, 112:6 (2022),  879–894
16. 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
17. Accelerated methods for saddle-point problem
    
    Zh. Vychisl. Mat. Mat. Fiz., 60:11 (2020),  1843–1866
18. On some stochastic mirror descent methods for constrained online optimization problems
    
    Computer Research and Modeling, 11:2 (2019),  205–217
19. Adaptive mirror descent algorithms for convex and strongly convex optimization problems with functional constraints
    
    Diskretn. Anal. Issled. Oper., 26:3 (2019),  88–114
20. Adaptive mirror descent algorithms in convex programming problems with Lipschitz constraints
    
    Trudy Inst. Mat. i Mekh. UrO RAN, 24:2 (2018),  266–279