Research interests:

• 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:

• T. Dlask, T. Werner.
  Activity Propagation in Systems of Linear Inequalities and Its Relation to Block-Coordinate Descent in Linear Programs.
  Constraints, 2023.
  [DOI][local copy]
  
  
  
• T. Dlask, T. Werner, S. de Givry.
  Super-Reparametrizations of Weighted CSPs: Properties and Optimization Perspective.
  Constraints, 2023.
  [DOI][preprint]
  
  
  
• T. Dlask, B. Savchynskyy.
  Relative-Interior Solution for (Incomplete) Linear Assignment Problem with Applications to Quadratic Assignment Problem.
  Arxiv preprint, 2023.
  [preprint][local copy]
  
  
  
• T. Dlask.
  Block-Coordinate Descent and Local Consistencies in Linear Programming.
  Dissertation, 2022.
  [local copy][DSpace][published abstract]
  
  
  
• T. Dlask, T. Werner.
  Classes of Linear Programs Solvable by Coordinate-Wise Minimization.
  Annals of Mathematics and Artificial Intelligence (AMAI), 2022.
  [DOI][local copy]
  
  
  
• 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]
  
  
  
• 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]
  
  
  
• 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]
  
  
  
• 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]
  
  
  
• T. Werner, D. Prusa, T. Dlask.
  Relative Interior Rule in Block-Coordinate Descent.
  Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
  [open access]