We identify a new class of hard 3-SAT instances, namely a random 3-SAT problems having exactly one solution and as few clauses as possible. It is numerically shown that the running time of complete methods as well as of local search algorithms for such problems is larger than for random instances around the phase transition point. We therefore provide instances with an exponential complexity in the so-called easy'' region, below the critical value of m n. This puts a new light on the connection between the phase transition phenomenon and NP-completeness.
There has been numerical evidence @cite_29 @cite_30 @cite_11 that below @math short instances of 3-SAT as well as of graph coloring @cite_51 can be hard. With respect to the formula size an interesting rigorous result is @cite_48 @cite_21 that an ordered DPLL algorithm needs an exponential time @math to find a resolution proof of an unsatisfiable 3-SAT instance. Note that the coefficient of the exponential growth increases with decreasing @math , i.e. short formulas are harder. For our ensemble of single-solution formulas we will find the same result.
@cite_30 @cite_48 @cite_29 @cite_21 @cite_51 @cite_11
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For each k ≤ 4, we give τ k > 0 such that a random k-CNF formula F with n variables and ⌊r k n⌋ clauses is satisfiable with high probability, but ORDERED-DLL takes exponential time on F with uniformly positive probability. Using results of [2], this can be strengthened to a high probability result for certain natural backtracking schemes and extended to many other DPLL algorithms. We present an algorithm for solving 3SAT instances. Several algorithms have been proved to work whp (with high probability) for various SAT distributions. However, an algorithm that works whp has a drawback. Indeed for typical instances it works well, however for some rare inputs it does not provide a solution at all. Alternatively, one could require that the algorithm always produce a correct answer but perform well on average. Expected polynomial time formalizes this notion. We prove that for some natural distribution on 3CNF formulas, called planted 3SAT, our algorithm has expected polynomial (in fact, almost linear) running time. The planted 3SAT distribution is the set of satisfiable 3CNF formulas generated in the following manner. First, a truth assignment is picked uniformly at random. Then, each clause satisfied by it is included in the formula with probability p. Extending previous work for the planted 3SAT distribution, we present, for the first time for a satisfiable SAT distribution, an expected polynomial time algorithm. Namely, it solves all 3SAT instances, and over the planted distribution (with p = d n2, d > 0 a sufficiently large constant) it runs in expected polynomial time. Our results extend to k-SAT for any constant k. par>We prove some non-approximability results for restrictions of basic combinatorial optimization problems to instances of bounded “degreeror bounded “width.” Specifically: We prove that the Max 3SAT problem on instances where each variable occurs in at most B clauses, is hard to approximate to within a factor @math , unless @math . H stad [18] proved that the problem is approximable to within a factor @math in polynomial time, and that is hard to approximate to within a factor @math . Our result uses a new randomized reduction from general instances of Max 3SAT to bounded-occurrences instances. The randomized reduction applies to other Max SNP problems as well. We observe that the Set Cover problem on instances where each set has size at most B is hard to approximate to within a factor @math unless @math . The result follows from an appropriate setting of parameters in Feige's reduction [11]. This is essentially tight in light of the existence of @math -approximate algorithms [20, 23, 9] We present a new PCP construction, based on applying parallel repetition to the inner verifier,'' and we provide a tight analysis for it. Using the new construction, and some modifications to known reductions from PCP to Hitting Set, we prove that Hitting Set with sets of size B is hard to approximate to within a factor @math . The problem can be approximated to within a factor B [19], and it is the Vertex Cover problem for B =2. The relationship between hardness of approximation and set size seems to have not been explored before. We observe that the Independent Set problem on graphs having degree at most B is hard to approximate to within a factor @math , unless P = NP . This follows from a comination of results by Clementi and Trevisan [28] and Reingold, Vadhan and Wigderson [27]. It had been observed that the problem is hard to approximate to within a factor @math unless P = NP [1]. An algorithm achieving factor @math is also known [21, 2, 30, 16 . Experiments on solvingr-SAT random formulae have provided evidence of a satisfiability threshold phenomenon with respect to the ratio of the number of clauses to the number of variables of formulae. Presently, only the threshold of 2-SAT formulae has been proved to exist and has been computed to be equal to 1. For 3-SAT formulae and more generally forr-SAT formulae, lower and upper bounds of the threshold have been established. The best established bounds concern 3-SAT. For an observed threshold of about 4.25, the best lower bound is 3.003 and the best upper bound 4.76. In this paper we establish a general upper bound of the threshold forr-SAT formulae giving a value for 3-SAT of 4.64, significantly improving the previous best upper bound. For this we have defined a more restrictive structure than a satisfying truth assignment for characterizing the satisfiability of a SAT formula which we have called negatively prime solution (NPS). By merely applying the first moment method to negatively prime solutions of a randomr-SAT formula we obtain our bound.