QuteSAT: a robust circuit-based SAT solver for complex circuit structure
Proceedings of the conference on Design, automation and test in Europe
Loop formulas for logic programs with arbitrary constraint atoms
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Improved lower bounds for tree-like resolution over linear inequalities
SAT'07 Proceedings of the 10th international conference on Theory and applications of satisfiability testing
A novel SAT-based approach to the task graph cost-optimal scheduling problem
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Core-generating discretization for rough set feature selection
Transactions on rough sets XIII
An integrative framework for intelligent software project risk planning
Decision Support Systems
Automated reencoding of boolean formulas
HVC'12 Proceedings of the 8th international conference on Hardware and Software: verification and testing
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Linear pseudo-Boolean (LPB) constraints denote inequalities between arithmetic sums of weighted Boolean functions and provide a significant extension of the modeling power of purely propositional constraints. They can be used to compactly describe many discrete electronic design automation problems with constraints on linearly combined, weighted Boolean variables, yet also offer efficient search strategies for proving or disproving whether a satisfying solution exists. Furthermore, corresponding decision procedures can easily be extended for minimizing or maximizing an LPB objective function, thus providing a core optimization method for many problems in logic and physical synthesis. In this paper, we review how recent advances in satisfiability search can be extended for pseudo-Boolean constraints and describe a new LPB solver that is based on generalized constraint propagation and conflict-based learning. We present a comparison with other, state-of-the-art LPB solvers which demonstrates the overall efficiency of our method.