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We present a simple probabilistic algorithm for solving k-SAT, and more generally, for solving constraint satisfaction problems (CSP). The algorithm follows a simple local-search paradigm (cf. [9]): randomly guess an initial assignment and then, guided by those clauses (constraints) that are not satisfied, by successively choosing a random literal from such(More)
Local search is widely used for solving the propositional satisÿability problem. Papadim-itriou (1991) showed that randomized local search solves 2-SAT in polynomial time. Recently, Sch oning (1999) proved that a close algorithm for k-SAT takes time (2 − 2=k) n up to a polynomial factor. This is the best known worst-case upper bound for randomized 3-SAT(More)
In [?], Schöning proposed a simple yet efficient randomized algorithm for solving the k-SAT problem. In the case of 3-SAT, the algorithm has an expected running time of poly(n) · (4/3) n = O(1.3334 n) when given a formula F on n variables. This was the up to now best running time known for an algorithm solving 3-SAT. In this paper, we describe an algorithm(More)
A simple probabilistic algorithm for solving the NP-complete problem k-SAT is reconsidered. This algorithm follows a well-known local-search paradigm: randomly guess an initial assignment and then, guided by those clauses that are not satisfied, by successively choosing a random literal from such a clause and changing the corresponding truth value, try to(More)
We show that satisfiability of formulas in k-CNF can be decided deterministically in time close to (2k/(k + 1)) n , where n is the number of variables in the input formula. This is the best known worst-case upper bound for deterministic k-SAT algorithms. Our algorithm can be viewed as a derandomized version of Schöning's probabilistic algorithm presented in(More)