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.

Convergence analysis of new inertial method for the split common null point problem
F.U. Ogbuisi, Y. Shehu, J.C. Yao, Optimization (2021).

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

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.

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.

Reflected three-operator splitting method for monotone inclusion problem
O.S. Iyiola, C.D. Enyi, Y. Shehu, Optimization Methods and Software (2021).

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.

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.

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).

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

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.

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.

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.

Commutativity in the algorithmic Lovász local lemma
V. Kolmogorov, SIAM Journal on Computing 47 (2018) 2029–2056.

Even delta-matroids and the complexity of planar boolean CSPs
A. Kazda, V. Kolmogorov, M. Rolinek, ACM Transactions on Algorithms 15 (2018).