Theory of linear and integer programming
Theory of linear and integer programming
New ideas for solving covering problems
DAC '95 Proceedings of the 32nd annual ACM/IEEE Design Automation Conference
DAC '96 Proceedings of the 33rd annual Design Automation Conference
Solving covering problems using LPR-based lower bounds
DAC '97 Proceedings of the 34th annual Design Automation Conference
GRASP: A Search Algorithm for Propositional Satisfiability
IEEE Transactions on Computers
An exact solution to the minimum size test pattern problem
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Radio Link Frequency Assignment
Constraints
Efficient conflict driven learning in a boolean satisfiability solver
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Discrete Applied Mathematics
Using weighted MAX-SAT engines to solve MPE
Eighteenth national conference on Artificial intelligence
Comparing Arguments Using Preference Orderings for Argument-Based Reasoning
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Satisfiability-Based Algorithms for Boolean Optimization
Annals of Mathematics and Artificial Intelligence
DATE '03 Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
Optimization algorithms for the minimum-cost satisfiability problem
Optimization algorithms for the minimum-cost satisfiability problem
Solving the minimum-cost satisfiability problem using SAT based branch-and-bound search
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
A logical approach to efficient Max-SAT solving
Artificial Intelligence
Efficient Generation of Unsatisfiability Proofs and Cores in SAT
LPAR '08 Proceedings of the 15th International Conference on Logic for Programming, Artificial Intelligence, and Reasoning
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
An algorithm for optimal winner determination in combinatorial auctions
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
MaxSolver: An efficient exact algorithm for (weighted) maximum satisfiability
Artificial Intelligence
A simple and flexible way of computing small unsatisfiable cores in SAT modulo theories
SAT'07 Proceedings of the 10th international conference on Theory and applications of satisfiability testing
Rocket-fast proof checking for SMT solvers
TACAS'08/ETAPS'08 Proceedings of the Theory and practice of software, 14th international conference on Tools and algorithms for the construction and analysis of systems
On SAT modulo theories and optimization problems
SAT'06 Proceedings of the 9th international conference on Theory and Applications of Satisfiability Testing
Search pruning techniques in SAT-based branch-and-bound algorithms for the binate covering problem
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Reducing chaos in SAT-like search: finding solutions close to a given one
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
SAT and SMT are still resolution: questions and challenges
IJCAR'12 Proceedings of the 6th international joint conference on Automated Reasoning
IJCAR'12 Proceedings of the 6th international joint conference on Automated Reasoning
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We consider optimization problems of the form (S, cost), where S is a clause set over Boolean variables x 1驴...驴x n , with an arbitrary cost function $\mathit{cost}\colon \mathbb{B}^n \rightarrow \mathbb{R}$ , and the aim is to find a model A of S such that cost(A) is minimized. Here we study the generation of proofs of optimality in the context of branch-and-bound procedures for such problems. For this purpose we introduce $\mathtt{DPLL_{BB}}$ , an abstract DPLL-based branch-and-bound algorithm that can model optimization concepts such as cost-based propagation and cost-based backjumping. Most, if not all, SAT-related optimization problems are in the scope of $\mathtt{DPLL_{BB}}$ . Since many of the existing approaches for solving these problems can be seen as instances, $\mathtt{DPLL_{BB}}$ allows one to formally reason about them in a simple way and exploit the enhancements of $\mathtt{DPLL_{BB}}$ given here, in particular its uniform method for generating independently verifiable optimality proofs.