DATE '03 Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
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
New inference rules for Max-SAT
Journal of Artificial Intelligence Research
MiniMaxSAT: a new weighted Max-SAT solver
SAT'07 Proceedings of the 10th international conference on Theory and applications of satisfiability testing
Variable dependency in local search: prevention Is better than cure
SAT'07 Proceedings of the 10th international conference on Theory and applications of satisfiability testing
On solving the partial MAX-SAT problem
SAT'06 Proceedings of the 9th international conference on Theory and Applications of Satisfiability Testing
Symmetry Breaking for Maximum Satisfiability
LPAR '08 Proceedings of the 15th International Conference on Logic for Programming, Artificial Intelligence, and Reasoning
Spatial and temporal design debug using partial MaxSAT
Proceedings of the 19th ACM Great Lakes symposium on VLSI
Solving (Weighted) Partial MaxSAT through Satisfiability Testing
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Relaxed DPLL Search for MaxSAT
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Generalizing Core-Guided Max-SAT
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
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
Integrating systematic and local search paradigms: a new strategy for MaxSAT
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
Automated design debugging with maximum satisfiability
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Boolean lexicographic optimization: algorithms & applications
Annals of Mathematics and Artificial Intelligence
Improving unsatisfiability-based algorithms for boolean optimization
SAT'10 Proceedings of the 13th international conference on Theory and Applications of Satisfiability Testing
Parallel search for maximum satisfiability
AI Communications - 18th RCRA International Workshop on “Experimental evaluation of algorithms for solving problems with combinatorial explosion”
Restoring CSP Satisfiability with MaxSAT
Fundamenta Informaticae - RCRA 2009 Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion
Improving SAT-Based weighted MaxSAT solvers
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
Reasoning over biological networks using maximum satisfiability
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
Artificial Intelligence
HVC'12 Proceedings of the 8th international conference on Hardware and Software: verification and testing
Planning as satisfiability with IPC simple preferences and action costs
AI Communications
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The MaxSAT problem and some of its well-known variants find an increasing number of practical applications in a wide range of areas. Examples include different optimization problems in system design and verification. However, most MaxSAT problem instances from these practical applications are too hard for existing branch and bound algorithms. One recent alternative to branch and bound MaxSAT algorithms is based on unsatisfiable subformula identification. A number of different unsatisfiability-based MaxSAT algorithms have been developed, which are effective at solving different classes of problem instances. All MaxSAT algorithms based on unsatisfiable subformula identification require using additional Boolean variables, either to allow relaxing some of the clauses or to encode cardinality constraints used by these algorithms. As a result, these algorithms may require using a significant number of additional Boolean variables, that can exceed the original number of variables for some problem instances. This paper proposes techniques for effectively reducing the number of auxiliary variables that must be used in unsatisfiability-based MaxSAT algorithms. Experimental results indicate that the techniques for reducing the number of auxiliary variables are effective, and contribute to more efficient MaxSAT algorithms.