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In computational complexity theory, a decision problem is NP-complete when itis both in NP and NP-hard. Although a solution to a NP-complete can be verifiedquickly, there is no known algorithm to solve it in polynomial time. Thereexists a method to reduce a SAT (Satifiability) problem to Subset Sum Problem(SSP) in the literature, however, it can only be applied to small or mediumsize problems. Our study is to find an efficient method to transform a SATproblem to a mixed integer linear programming problem in larger size. Observingthe feature of variable-clauses constraints in SAT, we apply linear inequalitymodel (LIM) to the problem and propose a method called LIMSAT. The new methodcan work efficiently for very large size problem with thousands of variablesand clauses in SAT tested using up-to-date benchmarks.

An Efficient Method to Transform SAT problems to Binary Integer Linear Programming Problem