Testing the Complexity of a Valued CSP Language

Author Vladimir Kolmogorov



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Vladimir Kolmogorov
  • Institute of Science and Technology Austria, Klosterneuburg, Austria

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Vladimir Kolmogorov. Testing the Complexity of a Valued CSP Language. In 46th International Colloquium on Automata, Languages, and Programming (ICALP 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 132, pp. 77:1-77:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/LIPIcs.ICALP.2019.77

Abstract

A Valued Constraint Satisfaction Problem (VCSP) provides a common framework that can express a wide range of discrete optimization problems. A VCSP instance is given by a finite set of variables, a finite domain of labels, and an objective function to be minimized. This function is represented as a sum of terms where each term depends on a subset of the variables. To obtain different classes of optimization problems, one can restrict all terms to come from a fixed set Gamma of cost functions, called a language. Recent breakthrough results have established a complete complexity classification of such classes with respect to language Gamma: if all cost functions in Gamma satisfy a certain algebraic condition then all Gamma-instances can be solved in polynomial time, otherwise the problem is NP-hard. Unfortunately, testing this condition for a given language Gamma is known to be NP-hard. We thus study exponential algorithms for this meta-problem. We show that the tractability condition of a finite-valued language Gamma can be tested in O(sqrt[3]{3}^{|D|}* poly(size(Gamma))) time, where D is the domain of Gamma and poly(*) is some fixed polynomial. We also obtain a matching lower bound under the Strong Exponential Time Hypothesis (SETH). More precisely, we prove that for any constant delta<1 there is no O(sqrt[3]{3}^{delta|D|}) algorithm, assuming that SETH holds.

Subject Classification

ACM Subject Classification
  • Theory of computation → Parameterized complexity and exact algorithms
Keywords
  • Valued Constraint Satisfaction Problems
  • Exponential time algorithms
  • Exponential Time Hypothesis

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References

  1. L. Barto, A. Krokhin, and R. Willard. Polymorphisms, and how to use them. In A. Krokhin and S. Živný, editors, The Constraint Satisfaction Problem: Complexity and Approximability. Dagstuhl Follow-Ups series, Volume 7, 2017. Google Scholar
  2. Libor Barto. The dichotomy for conservative constraint satisfaction problems revisited. In Proceedings of the 26th IEEE Symposium on Logic in Computer Science (LICS'11), pages 301-310. IEEE Computer Society, 2011. URL: http://dx.doi.org/10.1109/LICS.2011.25.
  3. Libor Barto, Marcin Kozik, and Todd Niven. The CSP dichotomy holds for digraphs with no sources and no sinks (a positive answer to a conjecture of Bang-Jensen and Hell). SIAM Journal on Computing, 38(5):1782-1802, 2009. URL: http://dx.doi.org/10.1137/070708093.
  4. A. Björklund, T. Husfeldt, and M. Koivisto. Set partitioning via inclusion–exclusion. SIAM J. Computing, 39(2):546-563, 2009. Google Scholar
  5. Andrei Bulatov. A dichotomy theorem for constraint satisfaction problems on a 3-element set. Journal of the ACM, 53(1):66-120, 2006. URL: http://dx.doi.org/10.1145/1120582.1120584.
  6. Andrei Bulatov. A dichotomy theorem for nonuniform CSPs. In Proceedings of the 58th Annual IEEE Symposium on Foundations of Computer Science (FOCS'17). IEEE Computer Society, 2017. URL: http://dx.doi.org/10.1109/FOCS.2017.37.
  7. Andrei Bulatov, Andrei Krokhin, and Peter Jeavons. Classifying the Complexity of Constraints using Finite Algebras. SIAM Journal on Computing, 34(3):720-742, 2005. URL: http://dx.doi.org/10.1137/S0097539700376676.
  8. Andrei A. Bulatov. Complexity of conservative constraint satisfaction problems. ACM Transactions on Computational Logic, 12(4), 2011. Article 24. URL: http://dx.doi.org/10.1145/1970398.1970400.
  9. Chris Calabro, Russell Impagliazzo, and Ramamohan Paturi. The complexity of satisfiability of small depth circuits. In Proceedings of the 4th International Workshop on Parameterized and Exact Computation (IWPEC), volume 5917 of LNCS, pages 75-85, 2009. Google Scholar
  10. Hubie Chen and Benoit Larose. Asking the Metaquestions in Constraint Tractability. ACM Transactions on Computation Theory (TOCT), 9(3), October 2017. URL: http://dx.doi.org/10.1145/3134757.
  11. Tomás Feder and Moshe Y. Vardi. The Computational Structure of Monotone Monadic SNP and Constraint Satisfaction: A Study through Datalog and Group Theory. SIAM Journal on Computing, 28(1):57-104, 1998. URL: http://dx.doi.org/10.1137/S0097539794266766.
  12. Fedor V. Fomin, Alexander Golovnev, Alexander S. Kulikov, and Ivan Mihajlin. Lower Bounds for the Graph Homomorphism Problem. In Proceedings of the 36th International Colloquium on Automata, Languages and Programming (ICALP), volume 9134 of LNCS. Springer, 2015. Google Scholar
  13. Martin Grötschel, László Lovász, and Alexander Schrijver. Geometric algorithms and combinatorial optimization. Springer, 1988. Google Scholar
  14. P. Hell and J. Nešetřil. The core of a graph. Discrete Mathematics, 109(1-3):117-126, 1992. Google Scholar
  15. Russell Impagliazzo and Ramamohan Paturi. On the complexity of k-SAT. Journal of Computer and System Sciences, 62(2):367-375, 2001. Google Scholar
  16. Russell Impagliazzo, Ramamohan Paturi, and Francis Zane. Which problems have strongly exponential complexity? Journal of Computer and System Sciences, 63(4):512-530, 2001. Google Scholar
  17. P. Jonsson, V. Lagerkvist, G. Nordh, , and B. Zanuttini. Strong partial clones and the time complexity of SAT problems. Journal of Computer and System Sciences, 84:52-78, 2017. Google Scholar
  18. Peter Jonsson, Victor Lagerkvist, and Biman Roy. Time Complexity of Constraint Satisfaction via Universal Algebra. In Proceedings of the 42nd International Symposium on Mathematical Foundations of Computer Science (MFCS), 2017. Google Scholar
  19. Keith Kearnes, Petar Marković, and Ralph McKenzie. Optimal strong Mal'cev conditions for omitting type 1 in locally finite varieties. Algebra Universalis, 72(1):91-100, 2014. Google Scholar
  20. Vladimir Kolmogorov. Testing the complexity of a valued CSP language. arXiv, 2019. URL: http://arxiv.org/abs/1803.02289v3.
  21. Vladimir Kolmogorov, Andrei Krokhin, and Michal Rolínek. The Complexity of General-Valued CSPs. SIAM Journal on Computing (SICOMP), 46(3):1087-1110, 2017. Google Scholar
  22. Vladimir Kolmogorov, Johan Thapper, and Stanislav Živný. The power of linear programming for general-valued CSPs. SIAM Journal on Computing, 44(1):1–-36, 2015. Google Scholar
  23. Marcin Kozik and Joanna Ochremiak. Algebraic Properties of Valued Constraint Satisfaction Problem. arXiv, 2015. Extended abstract published in ICALP'15. URL: http://arxiv.org/abs/1403.0476.
  24. Victor Lagerkvist and Magnus Wahlström. Which NP-Hard SAT and CSP Problems Admit Exponentially Improved Algorithms? arXiv, 2018. URL: http://arxiv.org/abs/1801.09488.
  25. E.L. Lawler. A note on the complexity of the chromatic number problem. Inf. Process. Lett., 5(3):66-67, 1976. Google Scholar
  26. Igor Razgon. Complexity Analysis of Heuristic CSP Search Algorithms. In International Workshop on Constraint Solving and Constraint Logic Programming (CSCLP), volume 3978 of LNCS, pages 88-99, 2005. Google Scholar
  27. Thomas J. Schaefer. The Complexity of Satisfiability Problems. In Proceedings of the 10th Annual ACM Symposium on Theory of Computing (STOC'78), pages 216-226. ACM, 1978. URL: http://dx.doi.org/10.1145/800133.804350.
  28. Mark H. Siggers. A strong Mal'cev condition for locally finite varieties omitting the unary type. Algebra Universalis, 64(1-2):15-20, 2010. Google Scholar
  29. Johan Thapper and Stanislav Živný. The complexity of finite-valued CSPs. Journal of the ACM (JACM), 63(4), 2016. Google Scholar
  30. Patrick Traxler. The time complexity of constraint satisfaction. In Proceedings of the 3rd International Workshop on Parameterized and Exact Computation (IWPEC), pages 190-201, 2008. Google Scholar
  31. Dmitriy Zhuk. A Proof of CSP Dichotomy Conjecture. In Proceedings of the 58th Annual IEEE Symposium on Foundations of Computer Science (FOCS'17). IEEE Computer Society, 2017. URL: http://dx.doi.org/10.1109/FOCS.2017.38.
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