Solution Sampling with Random Table Constraints

Authors Mathieu Vavrille, Charlotte Truchet, Charles Prud'homme

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Mathieu Vavrille
  • Laboratoire des Sciences du Numérique de Nantes, 44322 Nantes, France
Charlotte Truchet
  • Laboratoire des Sciences du Numérique de Nantes, 44322 Nantes, France
Charles Prud'homme
  • TASC, IMT-Atlantique, LS2N-CNRS, F-44307 Nantes, France

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Mathieu Vavrille, Charlotte Truchet, and Charles Prud'homme. Solution Sampling with Random Table Constraints. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 56:1-56:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)


Constraint programming provides generic techniques to efficiently solve combinatorial problems. In this paper, we tackle the natural question of using constraint solvers to sample combinatorial problems in a generic way. We propose an algorithm, inspired from Meel’s ApproxMC algorithm on SAT, to add hashing constraints to a CP model in order to split the search space into small cells of solutions. By sampling the solutions in the restricted search space, we can randomly generate solutions without revamping the model of the problem. We ensure the randomness by introducing a new family of hashing constraints: randomly generated tables. We implemented this solving method using the constraint solver Choco-solver. The quality of the randomness and the running time of our approach are experimentally compared to a random branching strategy. We show that our approach improves the randomness while being in the same order of magnitude in terms of running time.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Randomized search
  • solutions
  • sampling
  • table constraint


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