A machine program for theorem-proving
Communications of the ACM
BerkMin: A Fast and Robust Sat-Solver
Proceedings of the conference on Design, automation and test in Europe
Satisfiability-Based Algorithms for Boolean Optimization
Annals of Mathematics and Artificial Intelligence
Some Computational Aspects of distance-sat
Journal of Automated Reasoning
Propagation via lazy clause generation
Constraints
Solving (Weighted) Partial MaxSAT through Satisfiability Testing
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Branch and Bound for Boolean Optimization and the Generation of Optimality Certificates
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
Finding diverse and similar solutions in constraint programming
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
MINIMAXSAT: an efficient weighted max-SAT solver
Journal of Artificial Intelligence Research
On Modern Clause-Learning Satisfiability Solvers
Journal of Automated Reasoning
A Framework for Certified Boolean Branch-and-Bound Optimization
Journal of Automated Reasoning
Conflict directed lazy decomposition
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
Inter-instance nogood learning in constraint programming
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
Reformulation based MaxSAT robustness
Constraints
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Motivated by our own industrial users, we attack the following challenge that is crucial in many practical planning, scheduling or timetabling applications. Assume that a solver has found a solution for a given hard problem and, due to unforeseen circumstances (e.g., rescheduling), or after an analysis by a committee, a few more constraints have to be added and the solver has to be re-run. Then it is almost always important that the new solution is "close" to the original one. The activity-based variable selection heuristics used by SAT solvers make search chaotic, i.e., extremely sensitive to the initial conditions. Therefore, re-running with just one additional clause added at the end of the input usually gives a completely different solution. We show that naive approaches for finding close solutions do not work at all, and that solving the Boolean optimization problem is far too inefficient: to find a reasonably close solution, state-of-the-art tools typically require much more time than was needed to solve the original problem. Here we propose the first (to our knowledge) approach that obtains close solutions quickly. In fact, it typically finds the optimal (i.e., closest) solution in only 25% of the time the solver took in solving the original problem. Our approach requires no deep theoretical or conceptual innovations. Still, it is non-trivial to come up with and will certainly be valuable for researchers and practitioners facing the same problem.