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CPMpy library translates constraint models across solvers

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Researchers have developed CPMpy, an open-source library designed to translate high-level constraint satisfaction and optimization models into various lower-level formalisms. This framework allows users to express problems once and then test them across different solving technologies like CP, SMT, ILP, PB, and SAT without manual remodeling. The CPMpy system implements a modular transformation waterfall, addressing challenges such as handling negations and minimizing auxiliary variables, with a particular focus on linearizing non-linear operators for ILP, PB, and SAT solvers. Evaluations demonstrate that constraint models undergo significant changes during transformation, highlighting the importance of optimized linearization for certain solver types. AI

IMPACT Enables broader experimentation and comparison of different constraint-solving technologies for complex problems.

RANK_REASON The item is a research paper detailing a new software library for constraint solving. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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CPMpy library translates constraint models across solvers

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The item is a research paper detailing a new software library for constraint solving. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Tias Guns, Ignace Bleukx, Hendrik Bierlee, Jo Devriendt, Emilio Gamba, Orestis Lomis, Wout Piessens, Thomas Sergeys, Dimos Tsouros, Wout Vanroose, H\'el\`ene Verhaeghe ·

    Translating finite-domain integer constraint models to CP/SMT/ILP/PB/SAT solvers with CPMpy

    arXiv:2608.15143v1 Announce Type: new Abstract: Constraint solving is a declarative approach for solving combinatorial satisfaction and optimization problems. The user specifies their problem through constraints and decision variables, and a generic solver is used to find a solut…

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