New inertial projection methods for solving multivalued variational inequality problems beyond monotonicity
C. Izuchukwu, Y. Shehu, Networks and Spatial Economics 21 (2021) 291–323.
LibreCat: https://research-explorer.ista.ac.at/record/9234
DOI: 10.1007/s11067-021-09517-w
WoS: 000625002100001

Experimental evaluation of a local search approximation algorithm for the multiway cut problem
A. Bloch-Hansen, N. Samei, R. Solis-Oba, in:, Conference on Algorithms and Discrete Applied Mathematics, Springer Nature, 2021, pp. 346–358.
LibreCat: https://research-explorer.ista.ac.at/record/9227
DOI: 10.1007/978-3-030-67899-9_28

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.
LibreCat: https://research-explorer.ista.ac.at/record/8817
DOI: 10.1007/s00186-020-00730-w
WoS: 000590497300001

New convergence results for inertial Krasnoselskii–Mann iterations in Hilbert spaces with applications
O.S. Iyiola, Y. Shehu, Results in Mathematics 76 (2021).
LibreCat: https://research-explorer.ista.ac.at/record/9315
DOI: 10.1007/s00025-021-01381-x
WoS: 000632917700001

Convergence analysis of new inertial method for the split common null point problem
F.U. Ogbuisi, Y. Shehu, J.C. Yao, Optimization (2021).
LibreCat: https://research-explorer.ista.ac.at/record/9365
DOI: 10.1080/02331934.2021.1914035
WoS: 000640109300001

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.
LibreCat: https://research-explorer.ista.ac.at/record/8196
DOI: 10.1007/s11081-020-09544-5
WoS: 000559345400001

New inertial relaxed method for solving split feasibilities
Y. Shehu, A. Gibali, Optimization Letters 15 (2021) 2109–2126.
LibreCat: https://research-explorer.ista.ac.at/record/7925
DOI: 10.1007/s11590-020-01603-1
WoS: 000537342300001

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.
LibreCat: https://research-explorer.ista.ac.at/record/6593
DOI: 10.1007/s11075-019-00758-y
WoS: 000528979000015

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.
LibreCat: https://research-explorer.ista.ac.at/record/8077
DOI: 10.1016/j.apnum.2020.06.009
WoS: 000564648400018

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.
LibreCat: https://research-explorer.ista.ac.at/record/7161
DOI: 10.1007/s10957-019-01616-6
WoS: 000511805200009

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.
LibreCat: https://research-explorer.ista.ac.at/record/6725
DOI: 10.4230/LIPICS.ICALP.2019.77
arXiv: 1803.02289

Convergence results of forward-backward algorithms for sum of monotone operators in Banach spaces
Y. Shehu, Results in Mathematics 74 (2019).
LibreCat: https://research-explorer.ista.ac.at/record/6596
DOI: 10.1007/s00025-019-1061-4
WoS: 000473237500002
arXiv: 2101.09068

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).
LibreCat: https://research-explorer.ista.ac.at/record/7000
DOI: 10.1007/s40314-019-0955-9
WoS: 000488973100005
arXiv: 2101.09081

A local lemma for focused stochastical algorithms
D. Achlioptas, F. Iliopoulos, V. Kolmogorov, SIAM Journal on Computing 48 (2019) 1583–1602.
LibreCat: https://research-explorer.ista.ac.at/record/7412
DOI: 10.1137/16m109332x
WoS: 000493900200005
arXiv: 1809.01537

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.
LibreCat: https://research-explorer.ista.ac.at/record/7468
DOI: 10.1109/CVPR.2019.01140
WoS: 000542649304076
arXiv: 1806.05049

Function norms for neural networks
A. Rannen-Triki, M. Berman, V. Kolmogorov, M.B. Blaschko, in:, Proceedings of the 2019 International Conference on Computer Vision Workshop, IEEE, 2019.
LibreCat: https://research-explorer.ista.ac.at/record/7639
DOI: 10.1109/ICCVW.2019.00097
WoS: 000554591600090

Maximum persistency via iterative relaxed inference with graphical models
A. Shekhovtsov, P. Swoboda, B. Savchynskyy, IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (2018) 1668–1682.
LibreCat: https://research-explorer.ista.ac.at/record/703
DOI: 10.1109/TPAMI.2017.2730884
arXiv: 1508.07902

Absorption and directed Jónsson terms
A. Kazda, M. Kozik, R. McKenzie, M. Moore, in:, J. Czelakowski (Ed.), Don Pigozzi on Abstract Algebraic Logic, Universal Algebra, and Computer Science, Springer Nature, Cham, 2018, pp. 203–220.
LibreCat: https://research-explorer.ista.ac.at/record/10864
DOI: 10.1007/978-3-319-74772-9_7
arXiv: 1502.01072

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.
LibreCat: https://research-explorer.ista.ac.at/record/273
DOI: 10.1109/cvpr.2018.00389
WoS: 000457843603087
arXiv: 1604.08269

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.
LibreCat: https://research-explorer.ista.ac.at/record/193
DOI: 10.1145/3196494.3196534
WoS: 000516620100005