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

. We introduce an abstract definition of pattern search methods for solving nonlinear unconstrained optimization problems. Our definition unifies an important collection of optimization methods that neither computenor explicitly approximate derivatives. We exploit our characterization of pattern

Abstract: Here we want to represent an introduction about searching algorithms. Due to this, first we will discuss on general searching algorithm purposes. These are: virtual search spaces, sub-structures of a given structure and quantum computers. Also, for more information, we introduce some

• Search algorithms are fundamental techniques applied to solve a wide range of optimization problems. • Search algorithms attempt to find all the nodes in a network satisfying a particular property. • Examples – Find nodes that are reachable by directed paths from a source node. – Find nodes

implementation of a multiset data structure, called a “bag, ” in place of the FIFO queue usually employed in serial breadth-first search algorithms. For a variety of benchmark input graphs whose diameters are significantly smaller than the number of vertices — a condition met by many real-world graphs — PBFS

-variable random 3-SAT problems with search trees of size O(2 n=18:7 ). In addition to justifying these claims, this dissertation describes the most significant achievements of other researchers in this area, and discusses all of the widely known general techniques for speeding up SAT search algorithms

. This paper presents the convergence analysis for the multidirectional search algorithm, a direct search method for unconstrained minimization. The analysis follows the classic lines of proofs of convergence for gradient-related methods. The novelty of the argument lies in the fact that explicit

A new suboptimal search strategy for feature selection is presented. It represents a more sophisticated version of "classical" floating search algorithms (Pudil et al., 1994), attempts to remove some of their potential deficiencies and facilitates finding a solution even closer

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Imagine a phone directory containing N names arranged in completely random order. In order to find someone's phone number with a probability of , any classical algorithm (whether deterministic or probabilistic) will need to look at a minimum of names. Quantum mechanical systems can be in a