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Computing a Partition Function of a Generalized Pattern-Based Energy over a Semiring

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Abstract

Valued constraint satisfaction problems with ordered variables (VCSPO) are a special case of Valued CSPs in which variables are totally ordered and soft constraints are imposed on tuples of variables that do not violate the order. We study a restriction of VCSPO, in which soft constraints are imposed on a segment of adjacent variables and a constraint language \(\varvec{\Gamma }\) consists of \({\textbf {\{0,1\}}}\)-valued characteristic functions of predicates. This kind of potentials generalizes the so-called pattern-based potentials, which were applied in many tasks of structured prediction. For a constraint language \(\varvec{\Gamma }\) we introduce a closure operator, \( \overline{\varvec{\Gamma }^{\cap }}\supseteq \varvec{\Gamma }\), and give examples of constraint languages for which \(|\overline{\varvec{\Gamma }^{\cap }}|\) is small. If all predicates in \(\varvec{\Gamma }\) are cartesian products, we show that the minimization of a generalized pattern-based potential (or, the computation of its partition function) can be made in \({\varvec{\mathcal O}}(|\varvec{V}|\cdot |\varvec{D}|\varvec{^2} \cdot |\overline{\varvec{\Gamma }^{\cap }}|\varvec{^2})\) time, where \(\varvec{V}\) is a set of variables, \(\varvec{D}\) is a domain set. If, additionally, only non-positive weights of constraints are allowed, the complexity of the minimization task drops to \({\varvec{\mathcal O}}(|\varvec{V}|\cdot |\overline{\varvec{\Gamma }^{\cap }}| \cdot |\varvec{D}| \cdot \varvec{\max }_{\varrho \in \varvec{\Gamma }}\Vert \varrho \Vert \varvec{^2})\) where \(\Vert \varrho \Vert \) is the arity of \(\varrho \in \varvec{\Gamma }\). For a general language \(\varvec{\Gamma }\) and non-positive weights, the minimization task can be carried out in \({\varvec{\mathcal O}}(|\varvec{V}|\cdot |\overline{\varvec{\Gamma }^{\cap }}|\varvec{^2})\) time. We argue that in many natural cases \(\overline{\varvec{\Gamma }^{\cap }}\) is of moderate size, though in the worst case \(|\overline{\varvec{\Gamma }^{\cap }}|\) can blow up and depend exponentially on \(\varvec{\max }_{\varrho \in \varvec{\Gamma }}\Vert \varrho \Vert \).

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Notes

  1. In some languages, such as agglutinative languages, the number of roots is substantially smaller than the number of possible words (due to a composite structure of words).

References

  1. Khanna, S., Sudan, M., Trevisan, L.: Constraint satisfaction: the approximability of minimization problems. In: Proceedings of Computational Complexity. Twelfth Annual IEEE Conference, pp. 282–296 (1997). https://doi.org/10.1109/CCC.1997.612323

  2. Cooper, M.C.: Reduction operations in fuzzy or valued constraint satisfaction. Fuzzy Sets and Systems 134(3), 311–342 (2003). https://doi.org/10.1016/S0165-0114(02)00134-3

  3. Cooper, M., de Givry, S., Sanchez, M., Schiex, T., Zytnicki, M.: Virtual arc consistency for weighted CSP. In: Twenty-third AAAI Conference on Artificial Intelligence. Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, p. 6. AAAI - Association for the Advancement of Artificial Intelligence, Chicago, United States (2008). https://hal.inrae.fr/hal-02752851

  4. Hell, P., Nešetřil, J.: On the complexity of h-coloring. Journal of Combinatorial Theory, Series B 48(1), 92–110 (1990). https://doi.org/10.1016/0095-8956(90)90132-J

  5. Jeavons, P.: On the algebraic structure of combinatorial problems. Theoretical Computer Science 200(1), 185–204 (1998). https://doi.org/10.1016/S0304-3975(97)00230-2

  6. Bulatov, A., Jeavons, P., Krokhin, A.: Classifying the complexity of constraints using finite algebras. SIAM J. Comput. 34(3), 720–742 (2005). https://doi.org/10.1137/S0097539700376676

  7. Bulatov, A.A.: A dichotomy theorem for nonuniform csps. In: 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS), pp. 319–330 (2017). https://doi.org/10.1109/FOCS.2017.37

  8. Feder, T., Vardi, M.Y.: Monotone monadic snp and constraint satisfaction. In: Proceedings of the Twenty-Fifth Annual ACM Symposium on Theory of Computing. STOC ’93, pp. 612–622. Association for Computing Machinery, New York, NY, USA (1993). 10.1145/167088.167245. https://doi.org/10.1145/167088.167245

  9. Zhuk, D.: A proof of the csp dichotomy conjecture. J. ACM 67(5) (2020). https://doi.org/10.1145/3402029

  10. Dechter, R., Pearl, J.: Tree clustering for constraint networks (research note). Artif. Intell. 38(3), 353–366 (1989). https://doi.org/10.1016/0004-3702(89)90037-4

  11. Gottlob, G., Leone, N., Scarcello, F.: Hypertree decompositions and tractable queries. Journal of Computer and System Sciences 64(3), 579–627 (2002). https://doi.org/10.1006/jcss.2001.1809

  12. Grohe, M., Marx, D.: Constraint solving via fractional edge covers. ACM Trans. Algorithms 11(1) (2014). https://doi.org/10.1145/2636918

  13. Cooper, M.C., Živný, S.: A new hybrid tractable class of soft constraint problems. In: Cohen, D. (ed.) Principles and Practice of Constraint Programming – CP 2010, pp. 152–166. Springer, Berlin, Heidelberg (2010)

  14. Kolmogorov, V., Rolínek, M., Takhanov, R.: Effectiveness of structural restrictions for hybrid csps. In: Elbassioni, K., Makino, K. (eds.) Algorithms and Computation - 26th International Symposium, ISAAC 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 566–577. Springer, Germany (2015). 10.1007/978-3-662-48971-0_48. 26th International Symposium on Algorithms and Computation, ISAAC 2015 ; Conference date: 09-12-2015 Through 11-12-2015

  15. Takhanov, R.: Hybrid vcsps with crisp and valued conservative templates. In: Tokuyama, T., Okamoto, Y. (eds.) 28th International Symposium on Algorithms and Computation, ISAAC 2017, vol. 92. Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, Germany (2017). 10.4230/LIPIcs.ISAAC.2017.65. 28th International Symposium on Algorithms and Computation, ISAAC 2017 ; Conference date: 09-12-2017 Through 22-12-2017

  16. Takhanov, R.: Searching for an algebra on CSP solutions (2017)

  17. Ye, N., Lee, W.S., Chieu, H.L., Wu, D.: Conditional random fields with high-order features for sequence labeling. In: NIPS (2009)

  18. Takhanov, R., Kolmogorov, V.: Inference Algorithms for Pattern-based CRFs on Sequence Data, pp. 1182–1190 (2013). 30th International Conference on Machine Learning, ICML 2013 ; Conference date: 16-06-2013 Through 21-06-2013

  19. Qian, X., Jiang, X., Zhang, Q., Huang, X., Wu, L.: Sparse higher order conditional random fields for improved sequence labeling. In: ICML (2009)

  20. Takhanov, R., Assylbekov, Z.: Patterns versus characters in subword-aware neural language modeling. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.-S.M. (eds.) Neural Information Processing, pp. 157–166. Springer, Cham (2017)

  21. Takhanov, R., Kolmogorov, V.: Combining pattern-based crfs and weighted context-free grammars 26(1), 257–272 (2022). https://doi.org/10.3233/IDA-205623

  22. Impagliazzo, R., Paturi, R.: Complexity of k-sat. In: Proceedings of Fourteenth Annual IEEE Conference on Computational Complexity, pp. 237–240 (1999). https://doi.org/10.1109/CCC.1999.766282

  23. Bistarelli, S., Montanari, U., Rossi, F., Schiex, T., Verfaillie, G., Fargier, H.: Semiring-based csps and valued csps: Frameworks, properties, and comparison. Constraints 4(3), 199–240 (1999). https://doi.org/10.1023/A:1026441215081

  24. Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning. ICML ’01, pp. 282–289. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2001)

  25. Istrail, S.: Statistical mechanics, three-dimensionality and np-completeness: I. universality of intracatability for the partition function of the ising model across non-planar surfaces (extended abstract). In: Proceedings of the Thirty-Second Annual ACM Symposium on Theory of Computing. STOC ’00, pp. 87–96. Association for Computing Machinery, New York, NY, USA (2000). https://doi.org/10.1145/335305.335316

  26. Sarawagi, S., Cohen, W.W.: Semi-markov conditional random fields for information extraction. In: Saul, L., Weiss, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems, vol. 17, pp. 1185–1192. MIT Press, Cambridge, Massachusetts, USA (2005). https://proceedings.neurips.cc/paper/2004/file/eb06b9db06012a7a4179b8f3cb5384d3-Paper.pdf

  27. Rother, C., Kohli, P., Wei Feng, Jiaya Jia: Minimizing sparse higher order energy functions of discrete variables. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1382–1389 (2009). https://doi.org/10.1109/CVPR.2009.5206739

  28. Qian, X., Jiang, X., Zhang, Q., Huang, X., Wu, L.: Sparse higher order conditional random fields for improved sequence labeling. In: Proceedings of the 26th Annual International Conference on Machine Learning. ICML ’09, pp. 849–856. Association for Computing Machinery, New York, NY, USA (2009). 10.1145/1553374.1553483. https://doi.org/10.1145/1553374.1553483

  29. Komodakis, N., Paragios, N.: Beyond pairwise energies: Efficient optimization for higher-order mrfs. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2985–2992 (2009). https://doi.org/10.1109/CVPR.2009.5206846

  30. Cuong, N.V., Ye, N., Lee, W.S., Chieu, H.L.: Conditional random field with high-order dependencies for sequence labeling and segmentation. Journal of Machine Learning Research 15(28), 981–1009 (2014)

  31. Vieira, T., Cotterell, R., Eisner, J.: Speed-accuracy tradeoffs in tagging with variable-order CRFs and structured sparsity. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 1973–1978. Association for Computational Linguistics, Austin, Texas (2016). 10.18653/v1/D16-1206. https://www.aclweb.org/anthology/D16-1206

  32. Lavergne, T., Yvon, F.: Learning the structure of variable-order CRFs: a finite-state perspective. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 433–439. Association for Computational Linguistics, Copenhagen, Denmark (2017). 10.18653/v1/D17-1044. https://www.aclweb.org/anthology/D17-1044

  33. Martins, A., Smith, N., Figueiredo, M., Aguiar, P.: Structured sparsity in structured prediction. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 1500–1511. Association for Computational Linguistics, Edinburgh, Scotland, UK. (2011). https://www.aclweb.org/anthology/D11-1139

  34. Mukanov, Z., Takhanov, R.: Learning the pattern-based crf for prediction of a protein local structure. Informatica 46(6), 135–141 (2022). https://doi.org/10.31449/inf.v46i6.3787

  35. Felzenszwalb, P., Oberlin, J.G.: Multiscale fields of patterns. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 27. Curran Associates, Inc., New York, USA (2014). https://proceedings.neurips.cc/paper/2014/file/2a38a4a9316c49e5a833517c45d31070-Paper.pdf

  36. Terrioux, C., Jegou, P.: Bounded backtracking for the valued constraint satisfaction problems. In: Rossi, F. (ed.) Principles and Practice of Constraint Programming – CP 2003, pp. 709–723. Springer, Berlin, Heidelberg (2003)

  37. de Givry, S., Schiex, T., Verfaillie, G.: Exploiting tree decomposition and soft local consistency in weighted csp. AAAI’06, pp. 22–27. AAAI Press, Boston, Massachusetts (2006)

  38. Ndiaye, S.N., Jégou, P., Terrioux, C.: Extending to soft and preference constraints a framework for solving efficiently structured problems. In: 2008 20th IEEE International Conference on Tools with Artificial Intelligence, vol. 1, pp. 299–306 (2008). https://doi.org/10.1109/ICTAI.2008.109

  39. Gottlob, G., Greco, G., Scarcello, F.: Tractable optimization problems through hypergraph-based structural restrictions. In: Albers, S., Marchetti-Spaccamela, A., Matias, Y., Nikoletseas, S., Thomas, W. (eds.) Automata, Languages and Programming, pp. 16–30. Springer, Berlin, Heidelberg (2009)

  40. Färnqvist, T.: Exploiting structure in CSP-related problems. PhD thesis (2013)

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Acknowledgements

This research has been funded by Nazarbayev University under Faculty-development competitive research grants program for 2023-2025 Grant #20122022FD4131, PI Zh. Assylbekov. We would like to thank Anuar Sharafudinov for his help with writing a Java code that computes the closure of a simple language.

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Takhanov, R. Computing a Partition Function of a Generalized Pattern-Based Energy over a Semiring. Theory Comput Syst 67, 760–784 (2023). https://doi.org/10.1007/s00224-023-10128-w

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