Optimal speedup of Las Vegas algorithms
Information Processing Letters
Boosting combinatorial search through randomization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Efficient conflict driven learning in a boolean satisfiability solver
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
BerkMin: A Fast and Robust Sat-Solver
Proceedings of the conference on Design, automation and test in Europe
AMUSE: a minimally-unsatisfiable subformula extractor
Proceedings of the 41st annual Design Automation Conference
Refinement strategies for verification methods based on datapath abstraction
ASP-DAC '06 Proceedings of the 2006 Asia and South Pacific Design Automation Conference
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
The effect of restarts on the efficiency of clause learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Predicting learnt clauses quality in modern SAT solvers
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Effective Bit-Width and Under-Approximation
Computer Aided Systems Theory - EUROCAST 2009
Adaptive restart strategies for conflict driven SAT solvers
SAT'08 Proceedings of the 11th international conference on Theory and applications of satisfiability testing
SAT'08 Proceedings of the 11th international conference on Theory and applications of satisfiability testing
Artificial Intelligence
A single-instance incremental SAT formulation of proof- and counterexample-based abstraction
Proceedings of the 2010 Conference on Formal Methods in Computer-Aided Design
Boosting minimal unsatisfiable core extraction
Proceedings of the 2010 Conference on Formal Methods in Computer-Aided Design
Faster extraction of high-level minimal unsatisfiable cores
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
On freezing and reactivating learnt clauses
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
Managing SAT inconsistencies with HUMUS
Proceedings of the Sixth International Workshop on Variability Modeling of Software-Intensive Systems
Accelerating MUS extraction with recursive model rotation
Proceedings of the International Conference on Formal Methods in Computer-Aided Design
On solving the partial MAX-SAT problem
SAT'06 Proceedings of the 9th international conference on Theory and Applications of Satisfiability Testing
Towards efficient MUS extraction
AI Communications - 18th RCRA International Workshop on “Experimental evaluation of algorithms for solving problems with combinatorial explosion”
Efficient SAT solving under assumptions
SAT'12 Proceedings of the 15th international conference on Theory and Applications of Satisfiability Testing
Preprocessing in incremental SAT
SAT'12 Proceedings of the 15th international conference on Theory and Applications of Satisfiability Testing
IJCAR'12 Proceedings of the 6th international joint conference on Automated Reasoning
Refining restarts strategies for SAT and UNSAT
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
Formula preprocessing in MUS extraction
TACAS'13 Proceedings of the 19th international conference on Tools and Algorithms for the Construction and Analysis of Systems
| Hi-index | 0.00 |
In earlier work on a limited form of extended resolution for CDCL based SAT solving, new literals were introduced to factor out parts of learned clauses. The main goal was to shorten clauses, reduce proof size and memory usage and thus speed up propagation and conflict analysis. Even though some reduction was achieved, the effectiveness of this technique was rather modest for generic SAT solving. In this paper we show that factoring out literals is particularly useful for incremental SAT solving, based on assumptions. This is the most common approach for incremental SAT solving and was pioneered by the authors of MINISAT. Our first contribution is to focus on factoring out only assumptions, and actually all eagerly. This enables the use of compact dedicated data structures, and naturally suggests a new form of clause minimization, our second contribution. As last main contribution, we propose to use these data structures to maintain a partial proof trace for learned clauses with assumptions, which gives us a cheap way to flush useless learned clauses. In order to evaluate the effectiveness of our techniques we implemented them within the version of MINISAT used in the publically available state-of-the-art MUS extractor MUSer. An extensive experimental evaluation shows that factoring out assumptions in combination with our novel clause minimization procedure and eager clause removal is particularly effective in reducing average clause size, improves running time and in general the state-of-the-art in MUS extraction.