Effectiveness of Structural Restrictions for Hybrid CSPs

  • Vladimir Kolmogorov
  • Michal Rolínek
  • Rustem TakhanovEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9472)


Constraint Satisfaction Problem (CSP) is a fundamental algorithmic problem that appears in many areas of Computer Science. It can be equivalently stated as computing a homomorphism \({\mathbf {R}\rightarrow \varvec{\Gamma }}\) between two relational structures, e.g. between two directed graphs. Analyzing its complexity has been a prominent research direction, especially for the fixed template CSPs where the right side \(\varvec{\Gamma }\) is fixed and the left side \(\mathbf {R}\) is unconstrained.

Far fewer results are known for the hybrid setting that restricts both sides simultaneously. It assumes that \(\mathbf {R}\) belongs to a certain class of relational structures (called a structural restriction in this paper). We study which structural restrictions are effective, i.e. there exists a fixed template \(\varvec{\Gamma }\) (from a certain class of languages) for which the problem is tractable when \(\mathbf {R}\) is restricted, and NP-hard otherwise. We provide a characterization for structural restrictions that are closed under inverse homomorphisms. The criterion is based on the chromatic number of a relational structure defined in this paper; it generalizes the standard chromatic number of a graph.

As our main tool, we use the algebraic machinery developed for fixed template CSPs. To apply it to our case, we introduce a new construction called a “lifted language”. We also give a characterization for structural restrictions corresponding to minor-closed families of graphs, extend results to certain Valued CSPs (namely conservative valued languages), and state implications for (valued) CSPs with ordered variables and for the maximum weight independent set problem on some restricted families of graphs.


Valued Constraint Satisfaction Problems (VCSP) Minor-closed Family Inverse Homomorphism Prominent Research Directions Fundamental Algorithmic Problems 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank Andrei Krokhin for helpful comments on the manuscript. This work was supported by the European Research Council under the European Unions Seventh Framework Programme (FP7/2007–2013)/ERC grant agreement no 616160.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Vladimir Kolmogorov
    • 1
  • Michal Rolínek
    • 1
  • Rustem Takhanov
    • 2
    Email author
  1. 1.IST AustriaKlosterneuburgAustria
  2. 2.Nazarbayev UniversityAstanaKazakhstan

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