Abstract
In this paper we present an introduction to satisfiability algorithms. Since most complete SAT solvers (e.g. Satz, SATO, GRASP, and Chaff) are based on the Davis-Putnam procedure, we first describe in detail that procedure. Then, we present the improvements that can be incorporated into the Davis-Putnam Procedure in order to develop a competitive SAT solver: optimized data structures, variable selection heuristics, non-chronological backtracking, conflict-driven learning, and restarts. Finally, we describe GSAT and WalkSAT, which are the most widely used local search algorithms for solving SAT.
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