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Fast and parallel decomposition of constraint satisfaction problems
Georg Gottlob1, Cem Okulmus2, Reinhard Pichler2
1University of Oxford, Oxford, UK.
Constraints : an International Journal
|August 15, 2022
Summary
We developed faster algorithms and parallelization techniques to compute Generalized Hypertree Decompositions (GHDs) for complex Constraint Satisfaction Problems (CSPs). This enables efficient computation of optimal GHDs for a wider range of problems.
Area of Science:
- Artificial Intelligence
- Computational Complexity
Background:
- Constraint Satisfaction Problems (CSPs) are computationally challenging.
- Decomposition methods are crucial for solving complex CSPs.
- Computing optimal decompositions like Generalized Hypertree Decompositions (GHDs) has been algorithmically limited.
Purpose of the Study:
- To present algorithmic improvements and parallelization techniques for faster GHD computation.
- To enhance the ability to compute optimal GHDs for a broader range of CSP instances.
- To provide a foundation for CSPs and related problems using structural properties.
Main Methods:
- Algorithmic enhancements for GHD computation.
- Development of parallelization techniques.
- Focus on computing minimal-width GHDs.
Main Results:
- Significantly faster computation of Generalized Hypertree Decompositions (GHDs).
- Enables computation of optimal GHDs for a wider array of CSP instances.
- Improved performance on modern computing architectures.
Conclusions:
- The presented methods advance the practical computation of GHDs.
- Facilitates the evaluation of CSPs and related problems (e.g., Conjunctive Query answering) via structural analysis.
- Opens new possibilities for applying GHDs in AI and database systems.
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