Discrete Optimization in Computer Vision: Theory and Practice

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.

On the complexity of scrypt and proofs of space in the parallel random oracle model
J.F. Alwen, B. Chen, C. Kamath Hosdurg, V. Kolmogorov, K.Z. Pietrzak, S. Tessaro, in:, Springer, 2016, pp. 358–387.

Total variation on a tree
V. Kolmogorov, T. Pock, M. Rolinek, SIAM Journal on Imaging Sciences 9 (2016) 605–636.

Effectiveness of structural restrictions for hybrid CSPs
V. Kolmogorov, M. Rolinek, R. Takhanov, in:, 26th International Symposium, Springer Nature, 2015, pp. 566–577.

A new look at reweighted message passing
V. Kolmogorov, IEEE Transactions on Pattern Analysis and Machine Intelligence 37 (2015) 919–930.

A multi-plane block-coordinate Frank-Wolfe algorithm for training structural SVMs with a costly max-oracle
N. Shah, V. Kolmogorov, C. Lampert, in:, IEEE, 2015, pp. 2737–2745.

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.

Proofs of space
S. Dziembowski, S. Faust, V. Kolmogorov, K.Z. Pietrzak, 9216 (2015) 585–605.

Inference algorithms for pattern-based CRFs on sequence data
V. Kolmogorov, R. Takhanov, Algorithmica 76 (2016) 17–46.

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.

A faster approximation algorithm for the Gibbs partition function
V. Kolmogorov, in:, Proceedings of the 31st Conference On Learning Theory, PMLR, 2017, pp. 228–249.

Commutativity in the algorithmic Lovasz local lemma
V. Kolmogorov, in:, Proceedings - Annual IEEE Symposium on Foundations of Computer Science, IEEE, 2016.

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).

Even delta-matroids and the complexity of planar Boolean CSPs
A. Kazda, V. Kolmogorov, M. Rolinek, in:, SIAM, 2017, pp. 307–326.

The complexity of general-valued CSPs
V. Kolmogorov, A. Krokhin, M. Rolinek, in:, IEEE, 2015, pp. 1246–1258.

The complexity of general-valued CSPs
V. Kolmogorov, A. Krokhin, M. Rolinek, SIAM Journal on Computing 46 (2017) 1087–1110.

A novel convex relaxation for non binary discrete tomography
J. Kuske, P. Swoboda, S. Petra, in:, F. Lauze, Y. Dong, A. Bjorholm Dahl (Eds.), Springer, 2017, pp. 235–246.

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

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.

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.

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.

New inertial relaxed method for solving split feasibilities
Y. Shehu, A. Gibali, Optimization Letters 15 (2020) 2109–2126.

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.

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 (2020) 2627–2653.

A study of lagrangean decompositions and dual ascent solvers for graph matching
P. Swoboda, C. Rother, C. Abu Alhaija, D. Kainmueller, B. Savchynskyy, in:, IEEE, 2017, pp. 7062–7071.

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

Complexity of constraint satisfaction
M. Rolinek, Complexity of Constraint Satisfaction, IST Austria, 2017.

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.

A message passing algorithm for the minimum cost multicut problem
P. Swoboda, B. Andres, in:, IEEE, 2017, pp. 4990–4999.

A dual ascent framework for Lagrangean decomposition of combinatorial problems
P. Swoboda, J. Kuske, B. Savchynskyy, in:, IEEE, 2017, pp. 4950–4960.

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

Weak convergence for variational inequalities with inertial-type method
Y. Shehu, O.S. Iyiola, Applicable Analysis (2020) 1–25.