Parameter estimation for Gibbs distributions
D.G. Harris, V. Kolmogorov, ACM Transactions on Algorithms 21 (2025).

Reflected three-operator splitting method for monotone inclusion problem
O.S. Iyiola, C.D. Enyi, Y. Shehu, Optimization Methods and Software 37 (2022) 1527–1565.

Convergence analysis of new inertial method for the split common null point problem
F.U. Ogbuisi, Y. Shehu, J.C. Yao, Optimization 71 (2022) 3767–3795.

Weak convergence for variational inequalities with inertial-type method
Y. Shehu, O.S. Iyiola, Applicable Analysis 101 (2022) 192–216.

An inertial subgradient extragradient algorithm extended to pseudomonotone equilibrium problems
Y. Shehu, O.S. Iyiola, D.V. Thong, N.T.C. Van, Mathematical Methods of Operations Research 93 (2021) 213–242.

New inertial projection methods for solving multivalued variational inequality problems beyond monotonicity
C. Izuchukwu, Y. Shehu, Networks and Spatial Economics 21 (2021) 291–323.

New strong convergence method for the sum of two maximal monotone operators
Y. Shehu, Q.-L. Dong, L.-L. Liu, J.-C. Yao, Optimization and Engineering 22 (2021) 2627–2653.

New inertial relaxed method for solving split feasibilities
Y. Shehu, A. Gibali, Optimization Letters 15 (2021) 2109–2126.

A new notion of commutativity for the algorithmic Lovász Local Lemma
D.G. Harris, F. Iliopoulos, V. Kolmogorov, in:, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021.

One-sided Frank-Wolfe algorithms for saddle problems
V. Kolmogorov, T. Pock, in:, 38th International Conference on Machine Learning, 2021.

Projection methods with alternating inertial steps for variational inequalities: Weak and linear convergence
Y. Shehu, O.S. Iyiola, Applied Numerical Mathematics 157 (2020) 315–337.

An efficient projection-type method for monotone variational inequalities in Hilbert spaces
Y. Shehu, X.-H. Li, Q.-L. Dong, Numerical Algorithms 84 (2020) 365–388.

Inertial projection-type methods for solving quasi-variational inequalities in real Hilbert spaces
Y. Shehu, A. Gibali, S. Sagratella, Journal of Optimization Theory and Applications 184 (2020) 877–894.

A local lemma for focused stochastical algorithms
D. Achlioptas, F. Iliopoulos, V. Kolmogorov, SIAM Journal on Computing 48 (2019) 1583–1602.

Convergence analysis of projection method for variational inequalities
Y. Shehu, O.S. Iyiola, X.-H. Li, Q.-L. Dong, Computational and Applied Mathematics 38 (2019).

Convergence results of forward-backward algorithms for sum of monotone operators in Banach spaces
Y. Shehu, Results in Mathematics 74 (2019).

Testing the complexity of a valued CSP language
V. Kolmogorov, in:, 46th International Colloquium on Automata, Languages and Programming, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2019, p. 77:1-77:12.

Map inference via block-coordinate Frank-Wolfe algorithm
P. Swoboda, V. Kolmogorov, in:, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 2019.

Efficient optimization for rank-based loss functions
P. Mohapatra, M. Rolinek, C.V. Jawahar, V. Kolmogorov, M.P. Kumar, in:, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018, pp. 3693–3701.

On the memory hardness of data independent password hashing functions
J.F. Alwen, P. Gazi, C. Kamath Hosdurg, K. Klein, G.F. Osang, K.Z. Pietrzak, L. Reyzin, M. Rolinek, M. Rybar, in:, Proceedings of the 2018 on Asia Conference on Computer and Communication Security, ACM, 2018, pp. 51–65.