Alkousa Mohammad Soud

Publications in Math-Net.Ru

1. 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
2. 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
3. Subgradient methods for weakly convex and relatively weakly convex problems with a sharp minimum
   
   Computer Research and Modeling, 15:2 (2023),  393–412
4. Solving strongly convex-concave composite saddle-point problems with low dimension of one group of variable
   
   Mat. Sb., 214:3 (2023),  3–53
5. Adaptive Subgradient Methods for Mathematical Programming Problems with Quasiconvex Functions
   
   Trudy Inst. Mat. i Mekh. UrO RAN, 29:3 (2023),  7–25
6. Subgradient methods for non-smooth optimization problems with some relaxation of sharp minimum
   
   Computer Research and Modeling, 14:2 (2022),  473–495
7. Adaptive first-order methods for relatively strongly convex optimization problems
   
   Computer Research and Modeling, 14:2 (2022),  445–472
8. An approach for the nonconvex uniformly concave structured saddle point problem
   
   Computer Research and Modeling, 14:2 (2022),  225–237
9. Numerical Methods for Some Classes of Variational Inequalities with Relatively Strongly Monotone Operators
   
   Mat. Zametki, 112:6 (2022),  879–894
10. 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
11. Accelerated methods for saddle-point problem
    
    Zh. Vychisl. Mat. Mat. Fiz., 60:11 (2020),  1843–1866
12. On some stochastic mirror descent methods for constrained online optimization problems
    
    Computer Research and Modeling, 11:2 (2019),  205–217
13. Adaptive mirror descent algorithms for convex and strongly convex optimization problems with functional constraints
    
    Diskretn. Anal. Issled. Oper., 26:3 (2019),  88–114
14. Adaptive mirror descent algorithms in convex programming problems with Lipschitz constraints
    
    Trudy Inst. Mat. i Mekh. UrO RAN, 24:2 (2018),  266–279