Примери коришћења Satisfiability на Енглеском и њихови преводи на Српски
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Satisfiability of first-order Horn clauses is undecidable.
These operations are aimed at making the constraint store simpler to be checked for satisfiability and solved.
Its proof that satisfiability is NP-complete contains technical details about Turing machines as they relate to the definition of NP.
A labeling literal over a set of variables enforces a satisfiability check of the constraints over these variables.
In practice, satisfiability of the constraint store may be checked using an incomplete algorithm, which does not always detect inconsistency.
As a result,using all variables mentioned in the constraint store results in checking satisfiability of the store.
SAT and 2-SAT are special cases of k-satisfiability(k-SAT)or simply satisfiability(SAT), when each clause contains exactly k= 3 and k= 2 literals respectively.
In December 2005, Jeff Stuckman andGuo-Qiang Zhang showed in an arXiv article that the Mastermind satisfiability problem is NP-complete.
In practice, satisfiability is checked using methods that simplify the constraint store, that is, rewrite it into an equivalent but simpler-to-solve form.
The first use of the labeling literal is to actual check satisfiability or partial satisfiability of the constraint store.
Satisfiability of the formula is detected either when all variables are assigned without generating the empty clause, or, in modern implementations, if all clauses are satisfied.
Logic programming clauses in conjunction with constraint handling rules can be used to specify a method for establishing the satisfiability of the constraint store.
This is the Cook-Levin theorem;its proof that satisfiability is NP-complete contains technical details about Turing machines as they relate to the definition of NP.
Inference allows for the addition of new constraints, which may lead to proving inconsistency of the constraint store, andmay generally reduce the amount of search needed to establish its satisfiability.
The labeling literals are used on variables over finite domains to check satisfiability or partial satisfiability of the constraint store and to find a satisfying assignment.
The Mastermind satisfiability problem is a decision problem that asks,"Given a set of guesses and the number of colored and white pegs scored for each guess, is there at least one secret pattern that generates those exact scores?".
Since modern answer-set solvers make use of boolean SAT algorithms to very rapidly ascertain satisfiability, this implies that action languages can also enjoy the progress being made in the domain of boolean SAT solving.
Constraint propagation may solve the problem by reducing all domains to a single value, it may prove that the problem has no solution by reducing a domain to the empty set, butmay also terminate without proving satisfiability or unsatisfiability.
As Williams(2010) shows,if there exists an algorithm A that solves Boolean circuit satisfiability in time 2n/ƒ(n) for some superpolynomially growing function ƒ, then NEXPTIME is not a subset of P/poly.
The limiting value s∞ of the sequence of numbers sk is at most equal to sCNF,where sCNF is the infimum of the numbers δ such that satisfiability of conjunctive normal form formulas without clause length limits can be solved in time O(2δn).
Another application that often involves DPLL is automated theorem proving or satisfiability modulo theories(SMT), which is a SAT problem in which propositional variables are replaced with formulas of another mathematical theory.
Procedurally, subgoals whose predicates are defined by the program are solved by goal-reduction, as in ordinary logic programming, butconstraints are checked for satisfiability by a domain-specific constraint-solver, which implements the semantics of the constraint predicates.
This approach is used for a number of NP-hard problems Integer programming Nonlinear programming Travelling salesman problem( TSP)Quadratic assignment problem( QAP) Maximum satisfiability problem( MAX-SAT) Nearest neighbor search Flow shop scheduling Cutting stock problem False noise analysis( FNA) Computational phylogenetics Set inversion Parameter estimation 0/1 knapsack problem Feature selection in machine learning Structured prediction in computer vision Branch-and-bound may also be a base of various heuristics.