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Operation of the Collaborative Composite Manufacturing CCM System

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Published on: October 1, 2019

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

This summary is machine-generated.

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.

Keywords:

Constraint satisfaction Hypergraphs Parallel computing Structural decomposition methods

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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.