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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 18·5 min read
Translating finite-domain integer constraint models to CP/SMT/ILP/PB/SAT solvers with CPMpy
TL;DR
A modular framework was developed to translate high-level constraint satisfaction problems into various lower-level solving paradigms like CP, SMT, and ILP. This allows users to easily compare different solvers without needing to remodel their problems for each one.
✦ Why It Matters
Explore the CPMpy library to streamline your constraint modeling and solver comparisons today.
Key Takeaways
- Utilize CPMpy for efficient constraint model translations.
- Experiment with different solvers for optimal problem-solving.
- Focus on linearization techniques for ILP and PB performance.
- Leverage global constraints to simplify complex models.
How It Works
The framework translates high-level constraints into various lower-level formats through a modular waterfall approach. Each transformation is designed to handle specific challenges, such as negation and linearization, ensuring that the models remain compatible across different solving paradigms.