A theory of diagnosis from first principles
Artificial Intelligence
DATE '03 Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
Algorithms for Computing Minimal Unsatisfiable Subsets of Constraints
Journal of Automated Reasoning
A logical approach to efficient Max-SAT solving
Artificial Intelligence
Improved Design Debugging Using Maximum Satisfiability
FMCAD '07 Proceedings of the Formal Methods in Computer Aided Design
Algorithms for maximum satisfiability using unsatisfiable cores
Proceedings of the conference on Design, automation and test in Europe
Reveal: A Formal Verification Tool for Verilog Designs
LPAR '08 Proceedings of the 15th International Conference on Logic for Programming, Artificial Intelligence, and Reasoning
Solving (Weighted) Partial MaxSAT through Satisfiability Testing
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Algorithms for Weighted Boolean Optimization
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
New inference rules for Max-SAT
Journal of Artificial Intelligence Research
MINIMAXSAT: an efficient weighted max-SAT solver
Journal of Artificial Intelligence Research
Cause clue clauses: error localization using maximum satisfiability
Proceedings of the 32nd ACM SIGPLAN conference on Programming language design and implementation
Boolean lexicographic optimization: algorithms & applications
Annals of Mathematics and Artificial Intelligence
On solving the partial MAX-SAT problem
SAT'06 Proceedings of the 9th international conference on Theory and Applications of Satisfiability Testing
A modular approach to MaxSAT modulo theories
SAT'13 Proceedings of the 16th international conference on Theory and Applications of Satisfiability Testing
| Hi-index | 0.00 |
Maximum Satisfiability (MaxSAT) and its weighted variants are well-known optimization formulations of Boolean Satisfiability (SAT). Motivated by practical applications, recent years have seen the development of core-guided algorithms for MaxSAT. Among these, core-guided binary search with disjoint cores (BCD) represents a recent robust solution. This paper identifies a number of inefficiencies in the original BCD algorithm, related with the computation of lower and upper bounds during the execution of the algorithm, and develops solutions for them. In addition, the paper proposes two additional novel techniques, which can be implemented on top of core-guided MaxSAT algorithms that maintain both lower and upper bounds. Experimental results, obtained on representative problem instances, indicate that the proposed optimizations yield significant performance gains, and allow solving more problem instances.