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In computer science and formal methods, a SAT solver is a computer program which aims to solve the Boolean satisfiability problem.On input a formula over Boolean variables, such as "(x or y) and (x or not y)", a SAT solver outputs whether the formula is satisfiable, meaning that there are possible values of x and y which make the formula true, or unsatisfiable, meaning that there are no such ...
A variant of the 3-satisfiability problem is the one-in-three 3-SAT (also known variously as 1-in-3-SAT and exactly-1 3-SAT). Given a conjunctive normal form with three literals per clause, the problem is to determine whether there exists a truth assignment to the variables so that each clause has exactly one TRUE literal (and thus exactly two ...
Some of these algorithms are deterministic, while others may be stochastic. As there exist polynomial-time algorithms to convert any Boolean expression to conjunctive normal form such as Tseitin's algorithm, posing SAT problems in CNF does not change their computational difficulty. SAT problems are canonically expressed in CNF because CNF has ...
In computer science, conflict-driven clause learning (CDCL) is an algorithm for solving the Boolean satisfiability problem (SAT). Given a Boolean formula, the SAT problem asks for an assignment of variables so that the entire formula evaluates to true. The internal workings of CDCL SAT solvers were inspired by DPLL solvers.
Davis, Logemann, Loveland (1961) had developed this algorithm. Some properties of this original algorithm are: It is based on search. It is the basis for almost all modern SAT solvers. It does not use learning or non-chronological backtracking (introduced in 1996). An example with visualization of a DPLL algorithm having chronological backtracking:
The soft satisfiability problem (soft-SAT), given a set of SAT problems, asks for the maximum number of those problems which can be satisfied by any assignment. [16] The minimum satisfiability problem. The MAX-SAT problem can be extended to the case where the variables of the constraint satisfaction problem belong to the set
In computer science and mathematical logic, satisfiability modulo theories (SMT) is the problem of determining whether a mathematical formula is satisfiable.It generalizes the Boolean satisfiability problem (SAT) to more complex formulas involving real numbers, integers, and/or various data structures such as lists, arrays, bit vectors, and strings.
The algorithm extends the original SAT-solving DPLL algorithm with the ability to reason about an arbitrary theory T. [ 1 ] [ 2 ] [ 3 ] At a high level, the algorithm works by transforming an SMT problem into a SAT formula where atoms are replaced with Boolean variables.