We introduce a greedy local search procedure called GSAT for solving propositional satisfiability problems. Our experiments show that this procedure can be used to solve hard, randomly generated problems that are an order of magnitude larger than those that can be handled by more traditional

We present randomized approximation algorithms for the maximum cut (MAX CUT) and maximum 2-satisfiability (MAX 2SAT) problems that always deliver solutions of expected value at least .87856 times the optimal value. These algorithms use a simple and elegant technique that randomly rounds

. SplitStreamadV esses this problem by striping the content across a forest of interior-nodno# sjoint multicast trees that d stributes the forward ng load among all participating peers. For example, it is possible to construct efficient SplitStream forests in which each peer contributes only as much

The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new

We prove that the set of all reflectance functions (the mapping from surface normals to intensities) produced by Lambertian objects under distant, isotropic lighting lies close to a 9D linear subspace. This implies that, in general, the set of images of a convex Lambertian object obtained under a

bias, strict linear feature-space transformations are inappropriate in this case. Hence, only model-based linear transforms are considered. The paper compares the two possible forms of model-based transforms: (i) unconstrained, where any combination of mean and variance transform may be used, and (ii

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This paper introduces GRASP (Generic seaRch Algorithm for the Satisjiability Problem), an integrated algorithmic framework for SAT that un.$es several previously proposed searchpruning techniques and facilitates ident$cation of additional ones. GRASP is premised on the inevitability of confzicts during search and its most distinguishing feature is the augmentation of basic backtracking search with a powerfil confzict analysis procedure. Analyzing confzicts to determine their cawes enables GRASP to backtrack non-chronologically to earlier levels in the search tree, potentially pruning large portions of the search space. In addition, by “recording ” the causes of conflicts, GRASP can recognize andpreempt the occurrence of similar conficts later on in the search. Finally, straightjwward bookkeeping of the causality chains leading up to conflicts allows GRASP to identifi assignments that are necessary for a solution to be found. fiperimental results obtained from a large number of benchmarks, including many from the $eld of test pattern generation, indicate that application of the proposed confzict analysis techniques to SATalgorithm can be extremely effectivefor a large number of representative classes of SAT instances. 1

In 1970, Knuth, Pratt, and Morris [1] showed how to do basic pattern matching in linear time. Related problems, such as those discussed in [4], have previously been solved by efficient but sub-optimal algorithms. In this paper, we introduce an interesting data structure called a bi-tree. A linear

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