- Discrete and continuous optimization (large-scale problems, linear programming, and block-coordinate minimization/descent)
- Constraint programming (local consistencies and constraint propagation)
- Computer vision (pixel-level tasks via Markov models and convolutional neural networks)
Selected papers:
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T. Dlask, T. Werner, S. de Givry.
Super-Reparametrizations of Weighted CSPs: Properties and Optimization Perspective.
Constraints, 2023.
[DOI][preprint]
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T. Dlask, B. Savchynskyy.
Relative-Interior Solution for (Incomplete) Linear Assignment Problem with Applications to Quadratic Assignment Problem.
Arxiv preprint, 2023.
[preprint][local copy]
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T. Dlask.
Block-Coordinate Descent and Local Consistencies in Linear Programming.
Dissertation, 2022.
[local copy][DSpace]
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T. Dlask, T. Werner.
Classes of Linear Programs Solvable by Coordinate-Wise Minimization.
Annals of Mathematics and Artificial Intelligence (AMAI), 2022.
[DOI][local copy]
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T. Dlask, T. Werner, S. de Givry.
Bounds on Weighted CSPs Using Constraint Propagation and Super-Reparametrizations.
International Conference on Principles and Practice of Constraint Programming (CP), 2021.
[DOI][open access]
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T. Dlask, T. Werner.
On Relation Between Constraint Propagation and Block-Coordinate Descent in Linear Programs.
International Conference on Principles and Practice of Constraint Programming (CP), 2020.
[DOI][local copy]
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T. Dlask, T. Werner.
Bounding Linear Programs by Constraint Propagation: Application to Max-SAT.
International Conference on Principles and Practice of Constraint Programming (CP), 2020.
[DOI][local copy]
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T. Dlask, T. Werner.
A Class of Linear Programs Solvable by Coordinate-wise Minimization.
Learning and Intelligent Optimization Conference (LION14), 2020.
[DOI][preprint][local copy]
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T. Werner, D. Prusa, T. Dlask.
Relative Interior Rule in Block-Coordinate Descent.
Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
[open access]