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Worst-case time bounds for MAX-k-SAT w.r.t. the number of variables using local search

机译:MAX-k-SAT w.r.t.的最坏情况时限使用局部搜索的变量数

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During the past three years there was an explosion of algorithms solving MAX-SAT and MAX-2-SAT in worst-case time of the order c^K, where c<2 is a constant, and K is the number of clauses in the input formula. Such bounds w.r.t. the number of variables instead of the number of clauses are not known. Also, it was proved that approximate solutions for these problems (even beyond inapproximability ratios) can be obtained faster than exact solutions. However, the corresponding exponents still depended on the number of clauses in the input formula. In this paper we give a randomized (1-epsilon)-approximation algorithm for MAX-k-SAT. This algorithm runs in time of the order c_{k,epsilon}^N, where N is the number of variables, and c_{k,epsilon}<2 is a constant depending on k and epsilon.
机译:在过去的三年中,在最坏情况下以c ^ K的阶数求解MAX-SAT和MAX-2-SAT的算法激增,其中c <2是常数,K是子句中子句的数量输入公式。这样的界限不知道变量数而不是子句数。此外,事实证明,与精确解相比,可以更快地获得这些问题的近似解(甚至超出不可逼近率)。但是,相应的指数仍然取决于输入公式中子句的数量。在本文中,我们给出了MAX-k-SAT的随机(1-ε)近似算法。该算法以c_ {k, epsilon} ^ N的顺序运行,其中N是变量的数量,而c_ {k, epsilon} <2是取决于k和 epsilon的常数。

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