Kolmogorov Group

Discrete Optimization

When we step out into the street, we automatically judge the distance and speed of cars. For computers, estimating the depth of objects in an image requires complex computations. A popular approach for tackling this problem is to use discrete optimization algorithms – the research focus of the Kolmogorov group.

The work of Vladimir Kolmogorov’s group can be divided into three topics. The first is the development of efficient algorithms for inference in graphical models and combinatorial optimization problems. Some of their techniques are widely used in computer vision and other areas, for example the “Boykov-Kolmogorov” maximum flow algorithm and the “TRW-S” algorithm for MAP inference in pairwise graphical models. Kolmogorov‘s “Blossom V” algorithm is currently the fastest technique in practice for computing a minimum cost perfect matching in a graph. Their second focus is theoretical investigations of the complexity of discrete optimization, in particular using the framework of valued constraint satisfaction problems and their variants. Finally, the Kolmogorov group has worked on applications of discrete optimization in computer vision, such as image segmentation and stereo reconstruction.

On this site:


Current Projects

Inference in graphical models | Combinatorial optimization problems | Theory of discrete optimization


Takhanov R, Kolmogorov V. 2022. Combining pattern-based CRFs and weighted context-free grammars. Intelligent Data Analysis. 26(1), 257–272. View

Harris DG, Iliopoulos F, Kolmogorov V. 2021. A new notion of commutativity for the algorithmic Lovász Local Lemma. Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques. APPROX/RANDOM: Approximation Algorithms for Combinatorial Optimization Problems/ Randomization and Computation, LIPIcs, vol. 207, 31. View

Kolmogorov V, Pock T. 2021. One-sided Frank-Wolfe algorithms for saddle problems. 38th International Conference on Machine Learning. ICML: International Conference on Machine Learning. View

Dvorak M, Nicholson S. Massively winning configurations in the convex grabbing game on the plane. Proceedings of the 33rd Canadian Conference on Computational Geometry. CCCG: Canadian Conference on Computational Geometry. View

Izuchukwu C, Shehu Y. 2021. New inertial projection methods for solving multivalued variational inequality problems beyond monotonicity. Networks and Spatial Economics. 21(2), 291–323. View

View All Publications

ReX-Link: Vladimir Kolmogorov


since 2014 Professor, Institute of Science and Technology Austria (ISTA)
2011 – 2014 Assistant Professor, Institute of Science and Technology Austria (ISTA)
2005 – 2011 Lecturer, University College London, UK
2003 – 2005 Assistant Researcher, Microsoft Research, Cambridge, UK
2003 PhD, Cornell University, Ithaca, U

Selected Distinctions

2018 Best paper award honorable mention at IEEE/CVF Conference on Computer Vision and Pattern Recognition
2013 ERC Consolidator Grant
2012 Koenderink Prize at the European Conference on Computer Vision for fundamental contributions to computer vision
2007 Honorable mention, outstanding student paper award (to M. Pawan Kumar) at Neural Information Processing Systems Conference
2006 – 2011 Royal Academy of Engineering/EPSRC Research Fellowship
2005 Best paper honorable mention award at IEEE Conference on Computer Vision and Pattern Recognition
2002 Best paper award at the European Conference on Computer Vision

Additional Information

Open Kolmogorov’s homepage

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