Journal of the ACM (JACM)
Many hard examples for resolution
Journal of the ACM (JACM)
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Regular resolution versus unrestricted resolution
SIAM Journal on Computing
Boosting combinatorial search through randomization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Act, and the rest will follow: exploiting determinism in planning as satisfiability
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
GRASP: A Search Algorithm for Propositional Satisfiability
IEEE Transactions on Computers
Symbolic model checking using SAT procedures instead of BDDs
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
A Machine-Oriented Logic Based on the Resolution Principle
Journal of the ACM (JACM)
A machine program for theorem-proving
Communications of the ACM
Circuit-based Boolean Reasoning
Proceedings of the 38th annual Design Automation Conference
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
An exponential separation between regular and general resolution
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
The Efficiency of Resolution and Davis--Putnam Procedures
SIAM Journal on Computing
Efficient conflict driven learning in a boolean satisfiability solver
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Dependent and Independent Variables in Propositional Satisfiability
JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
Tuning SAT Checkers for Bounded Model Checking
CAV '00 Proceedings of the 12th International Conference on Computer Aided Verification
BerkMin: A Fast and Robust Sat-Solver
Proceedings of the conference on Design, automation and test in Europe
"Planar" Tautologies Hard for Resolution
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
Solving Satisfiability in Combinational Circuits
IEEE Design & Test
Mutilated chessboard problem is exponentially hard for resolution
Theoretical Computer Science
Exponential bounds for DPLL below the satisfiability threshold
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
A sharp threshold in proof complexity yields lower bounds for satisfiability search
Journal of Computer and System Sciences - STOC 2001
Unrestricted vs restricted cut in a tableau method for Boolean circuits
Annals of Mathematics and Artificial Intelligence
The resolution complexity of random graphk-colorability
Discrete Applied Mathematics - Special issue: Typical case complexity and phase transitions
Exponential Lower Bounds for the Running Time of DPLL Algorithms on Satisfiable Formulas
Journal of Automated Reasoning
The Resolution Complexity of Independent Sets and Vertex Covers in Random Graphs
Computational Complexity
On the power of top-down branching heuristics
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Towards understanding and harnessing the potential of clause learning
Journal of Artificial Intelligence Research
Heuristics based on unit propagation for satisfiability problems
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
The effect of restarts on the efficiency of clause learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Backdoors to typical case complexity
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Limitations of restricted branching in clause learning
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Extended asp tableaux and rule redundancy in normal logic programs1
Theory and Practice of Logic Programming
Solution Enumeration for Projected Boolean Search Problems
CPAIOR '09 Proceedings of the 6th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Heuristics for planning with SAT
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Propelling SAT and SAT-based BMC using careset
Proceedings of the 2010 Conference on Formal Methods in Computer-Aided Design
DPLL+ROBDD Derivation applied to inversion of some cryptographic functions
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
On the relative efficiency of DPLL and OBDDs with axiom and join
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
Planning as satisfiability: Heuristics
Artificial Intelligence
Tableau Calculi for Logic Programs under Answer Set Semantics
ACM Transactions on Computational Logic (TOCL)
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The techniques for making decisions, that is, branching, play a central role in complete methods for solving structured instances of constraint satisfaction problems (CSPs). In this work we consider branching heuristics in the context of propositional satisfiability (SAT), where CSPs are expressed as propositional formulas. In practice, there are cases when SAT solvers based on the Davis-Putnam-Logemann-Loveland procedure (DPLL) benefit from limiting the set of variables the solver is allowed to branch on to so called input variables which provide a strong unit propagation backdoor set to any SAT instance. Theoretically, however, restricting branching to input variables implies a super-polynomial increase in the length of the optimal proofs for DPLL (without clause learning), and thus input-restricted DPLL cannot polynomially simulate DPLL. In this paper we settle the case of DPLL with clause learning. Surprisingly, even with unlimited restarts, input-restricted clause learning DPLL cannot simulate DPLL (even without clause learning). The opposite also holds, and hence DPLL and input-restricted clause learning DPLL are polynomially incomparable. Additionally, we analyze the effect of input-restricted branching on clause learning solvers in practice with various structured real-world benchmarks.