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Lightweight Look-Ahead-Based Value Heuristics for Constraint Optimization Problems

Authors Ziyang Yu , Hongbo Li

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LIPIcs.CP.2026.59.pdf
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Subject Classification

ACM Subject Classification
  • Theory of computation → Constraint and logic programming
Keywords
  • Constraint Optimization Problem
  • Value Heuristic
  • Look-Ahead

Metrics

Abstract

The Bound-Impact Value Selector (BIVS) employs a look-ahead strategy to enable black-box Constraint Optimization Problem (COP) solvers to find high-quality solutions earlier. However, its computational cost prohibits its use throughout the entire search process. To mitigate this cost, the Restricted Fixpoint (RF) approach considers only the constraints on the shortest paths between the selected variable and the objective, yielding better performance. In this paper, we propose a lightweight strategy from a different perspective, named Assess Before Look-Ahead (ABLA), to enhance the performance of look-ahead-based value heuristics for solving COPs. ABLA first assesses whether the look-ahead process can differentiate between the values of a variable, and only performs the look-ahead when this assessment passes. Experiments on benchmark instances from recent MiniZinc Challenges demonstrate that ABLA’s decisions to skip redundant look-ahead processes are highly reliable, with an average accuracy of over 94%. Consequently, ABLA significantly boosts the performance of both BIVS and RF, outperforming two other baselines: the minimum value heuristic and the RLARF value heuristic.

Cite As Get BibTex

Ziyang Yu and Hongbo Li. Lightweight Look-Ahead-Based Value Heuristics for Constraint Optimization Problems. In 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 379, pp. 59:1-59:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.CP.2026.59

Author Details

Ziyang Yu
  • School of Information Science and Technology, Northeast Normal University, Changchun, China
Hongbo Li
  • School of Computing and Artificial Intelligence, Shanghai University of Finance and Economics, Shanghai, China

Funding

The National Natural Science Foundation of China under Grant 62276060 and CCF-Huawei Populus Grove Fund

Acknowledgements

We thank the anonymous referees for their constructive comments.

Supplementary Materials

References

  1. Gilles Audemard, Christophe Lecoutre, and Charles Prud'Homme. Guiding backtrack search by tracking variables during constraint propagation. In International Conference on Principles and Practice of Constraint Programming. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023. URL: https://doi.org/10.4230/LIPIcs.CP.2023.9.
  2. Timo Berthold. Measuring the impact of primal heuristics. Operations Research Letters, 41(6):611-614, 2013. URL: https://doi.org/10.1016/J.ORL.2013.08.007.
  3. C. Bessiere. Constraint propagation. In Foundations of Artificial Intelligence, volume 2, pages 29-83. Elsevier, 2006. URL: https://doi.org/10.1016/S1574-6526(06)80007-6.
  4. F. Boussemart, F. Hemery, C. Lecoutre, and L. Sais. Boosting systematic search by weighting constraints. In Proc. ECAI'04, pages 146-150, 2004. Google Scholar
  5. Quentin Cappart, Thierry Moisan, Louis-Martin Rousseau, Isabeau Prémont-Schwarz, and Andre A. Cire. Combining reinforcement learning and constraint programming for combinatorial optimization. Proceedings of the AAAI Conference on Artificial Intelligence, 35(5):3677-3687, May 2021. Google Scholar
  6. Augustin Delecluse and Pierre Schaus. Black-box value heuristics for solving optimization problems with constraint programming (short paper). In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2024. URL: https://doi.org/10.4230/LIPIcs.CP.2024.36.
  7. Emir Demirovic, Geoffrey Chu, and Peter J. Stuckey. Solution-based phase saving and large neighbourhood search. In Proc. CP'18, pages 99-108. Springer, 2018. Google Scholar
  8. J. G. Fages and C. Prud'Homme. Making the first solution good. In Proc. ICTAI'17. IEEE, 2017. Google Scholar
  9. E. Freuder. In pursuit of the holy grail. ACM Comput. Surv., 28(4es):63-es, December 1996. URL: https://doi.org/10.1145/242224.242304.
  10. D. Habet and C. Terrioux. Conflict history based search for constraint satisfaction problem. In Proc. of the 34th ACM/SIGAPP Symposium on Applied Computing, pages 1117-1122. ACM, 2019. Google Scholar
  11. J. Hwang and D. G. Mitchell. 2-way vs d-way branching for csp. In Proc. of CP'05, pages 343-357. Springer, 2005. Google Scholar
  12. C. Lecoutre, L. Sais, S. Tabary, and V. Vidal. Recording and minimizing nogoods from restarts. Journal on Satisfiability, Boolean Modeling and Computation, 1(3-4):147-167, 2007. URL: https://doi.org/10.3233/sat190009.
  13. H. Li, M. Yin, and Z. Li. Failure Based Variable Ordering Heuristics for Solving CSPs. In Proc. CP'21, pages 9:1-9:10, 2021. Google Scholar
  14. M. Luby, A. Sinclair, and D. Zuckerman. Optimal speedup of las vegas algorithms. Information Processing Letters, 47(4):173-180, 1993. URL: https://doi.org/10.1016/0020-0190(93)90029-9.
  15. Tom Marty, Tristan François, Pierre Tessier, Louis Gautier, Louis-Martin Rousseau, and Quentin Cappart. Learning a Generic Value-Selection Heuristic Inside a Constraint Programming Solver. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023), volume 280 of Leibniz International Proceedings in Informatics (LIPIcs), pages 25:1-25:19, 2023. URL: https://doi.org/10.4230/LIPIcs.CP.2023.25.
  16. L. Michel and P. Van Hentenryck. Activity-based search for black-box constraint programming solvers. In Proc. CPAIOR'12, pages 228-243. Springer, 2012. Google Scholar
  17. A. Palmieri and G. Perez. Objective as a feature for robust search strategies. In Proc. CP'18, pages 328-344. Springer, 2018. Google Scholar
  18. G. Pesant, C. G. Quimper, and A. Zanarini. Counting-based search: Branching heuristics for constraint satisfaction problems. Journal of Artificial Intelligence Research, 43:173-210, 2012. URL: https://doi.org/10.1613/JAIR.3463.
  19. Gilles Pesant. Counting-based search for constraint optimization problems. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1), March 2016. Google Scholar
  20. C. Prud'homme, J-G. Fages, and X. Lorca. Choco Documentation. TASC - LS2N CNRS UMR 6241, COSLING S.A.S., 2017. URL: http://www.choco-solver.org.
  21. P. Refalo. Impact-based search strategies for constraint programming. In Proc. CP'04, pages 557-571. Springer, 2004. URL: https://doi.org/10.1007/978-3-540-30201-8_41.
  22. H. Wattez, C. Lecoutre, A. Paparrizou, and S. Tabary. Refining constraint weighting. In Proc. of ICTAI'19, pages 71-77. IEEE, 2019. Google Scholar
  23. Y. Zhang and H. Li. Right branches matter in failure-based variable ordering heuristics. In Proc. of AAAI'26, pages 14397-14404, 2026. Google Scholar
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