2026-08-03T13:48:43Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:15347 2024-03-06T10:55:12Z ddc:004 open_access

Solution Sampling with Random Table Constraints Vavrille, Mathieu Truchet, Charlotte Prud'homme, Charles solutions sampling table constraint 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Mathieu Vavrille and Charlotte Truchet and Charles Prud'homme 2021 Is Part Of LIPIcs, Volume 210, 27th International Conference on Principles and Practice of Constraint Programming (CP 2021) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.CP.2021.56 urn:nbn:de:0030-drops-153477 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2021.56 eng https://creativecommons.org/licenses/by/4.0/legalcode