BDD-Guided Clause Generation

@inproceedings{Kell2015BDDGuidedCG,
  title={BDD-Guided Clause Generation},
  author={B. Kell and Ashish Sabharwal and W. V. Hoeve},
  booktitle={CPAIOR},
  year={2015}
}
Nogood learning is a critical component of Boolean satisfiability (SAT) solvers, and increasingly popular in the context of integer programming and constraint programming. We present a generic method to learn valid clauses from exact or approximate binary decision diagrams (BDDs) and resolution in the context of SAT solving. We show that any clause learned from SAT conflict analysis can also be generated using our method, while, in addition, we can generate stronger clauses that cannot be… Expand
Decision Diagrams for Combinatorial Optimization and Satisfaction
The Size of BDDs and Other Data Structures in Temporal Logics Model Checking
Domain reduction techniques for global NLP and MINLP optimization

References

SHOWING 1-10 OF 27 REFERENCES
Lazy Clause Generation: Combining the Power of SAT and CP (and MIP?) Solving
On the power of clause-learning SAT solvers as resolution engines
Nogood processing in csps
Conflict analysis in mixed integer programming
Explaining Flow-Based Propagation
On Threshold BDDs and the Optimal Variable Ordering Problem
Propagation via lazy clause generation
Optimization Bounds from Binary Decision Diagrams
...
1
2
3
...