This survey presents a general framework (an algorithm space) that integrates existing SAT algorithms into a unified perspective and describes sequential and parallel SAT algorithms including variable splitting, resolution, local search, global optimization, mathematical programming, and practical SAT algorithms.
A local search method with a search space smoothing technique that is capable of smoothing the rugged terrain surface of the search space and has significantly improved the performance of existing heuristic search algorithms.
This work has applied this general search framework to study a benchmark constraint-based search problem, the n-queens problem, and implemented an efficient local search algorithm, running in linear time, that does not backtrack.
The author shows how to use the local search techniques to solve the satisfiability problem and indicates that the localsearch algorithms are much more efficient than the existing SAT algorithms for certain classes of conjunctive normal form (CNF) formulas.
QS2 and QS3 are probabilistic local search algorithms, based on a gradient-based heuristic, capable of finding a solution for extremely large n-queens problems.
The Universal SAT problem model, UniSAT, is introduced, which transforms the discrete SAT problem on Boolean space {0, 1}/Sup m/ into an unconstrained global optimization problem on real space E/sup m/ and establishes a direct correspondence between the solution of the SAT problem and the global minimum point of the UniSat objective function.
A fine-grained, massively parallel hardware computer architecture has been designed for the DRA5 algorithm, and it is shown that many orders of magnitude of efficiency improvement can be reached on such a hardware architecture.