A machine program for theorem-proving
Communications of the ACM
UnitWalk: A New SAT Solver that Uses Local Search Guided by Unit Clause Elimination
Annals of Mathematics and Artificial Intelligence
Complete local search for propositional satisfiability
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
ICTAI '09 Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence
A lightweight component caching scheme for satisfiability solvers
SAT'07 Proceedings of the 10th international conference on Theory and applications of satisfiability testing
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
On the power of clause-learning SAT solvers with restarts
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Adaptive restart strategies for conflict driven SAT solvers
SAT'08 Proceedings of the 11th international conference on Theory and applications of satisfiability testing
Between restarts and backjumps
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
Captain Jack: new variable selection heuristics in local search for SAT
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
Empirical study of the anatomy of modern sat solvers
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
EagleUP: solving random 3-SAT using SLS with unit propagation
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
Improved local search for circuit satisfiability
SAT'10 Proceedings of the 13th international conference on Theory and Applications of Satisfiability Testing
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Most state of the art SAT solvers for industrial problems are based on the Conflict Driven Clause Learning (CDCL) paradigm. Although this paradigm evolved from the systematic DPLL search algorithm, modern techniques of far backtracking and restarts make CDCL solvers non-systematic. CDCL solvers do not systematically examine all possible truth assignments as does DPLL. Local search solvers are also non-systematic and in this paper we show that CDCL can be reformulated as a local search algorithm: a local search algorithm that through clause learning is able to prove UNSAT. We show that the standard formulation of CDCL as a backtracking search algorithm and our new formulation of CDCL as a local search algorithm are equivalent, up to tie breaking. In the new formulation of CDCL as local search, the trail no longer plays a central role in the algorithm. Instead, the ordering of the literals on the trail is only a mechanism for efficiently controlling clause learning. This changes the paradigm and opens up avenues for further research and algorithm design. For example, in QBF the quantifier places restrictions on the ordering of variables on the trail. By making the trail less important, an extension of our local search algorithm to QBF may provide a way of reducing the impact of these variable ordering restrictions.