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154
PKorat: Parallel Generation of Structurally Complex Test Inputs
"... Constraint solving lies at the heart of several specificationbased approaches to automated testing. Korat is a previously developed algorithm for solving constraints in Java programs. Given a Java predicate that represents the desired constraints and a bound on the input size, Korat systematically ..."
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Cited by 2 (1 self)
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that parallelizes the Korat search. PKorat explores the same state space as Korat but considers several candidates in each iteration. These candidates are distributed among parallel workers resulting in an efficient parallel version of Korat. Experimental results using complex structural constraints from a variety
The Complexity of TransformationBased Join Enumeration
, 1997
"... Query optimizers that explore a search space exhaustively using transformation rules usually apply all possible rules on each alternative, and stop when no new information is produced. A memoizing structure was proposed in [McK93] to improve the reuse of common subexpression, thus improving the eff ..."
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Cited by 32 (4 self)
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the efficiency of the search considerably. However, a question that remained open is, what is the complexity of the transformationbased enumeration process ? In particular, with n the number of relations, does it achieve the O(3 n ) lower bound established by [OL90]? In this paper we examine the problem
Iterative Flattening: A Scalable Method for Solving MultiCapacity Scheduling Problems
 In AAAI/IAAI
, 2000
"... One challenge for research in constraintbased scheduling has been to produce scalable solution procedures under fairly general representational assumptions. Quite often, the computational burden of techniques for reasoning about more complex types of temporal and resource capacity constraints ..."
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Cited by 24 (7 self)
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procedure for generating feasible solutions to more complex, multicapacity scheduling problems with maximum time lags. Adapting this procedure to exploit the simpler temporal structure of MCJSSP, we are able to produce a quite efficient solution generator. However, the procedure only indirectly
The Bounding Mesh Algorithm The Bounding Mesh Algorithm
, 2015
"... We present an algorithm to generate a onesided approximation of a given triangular mesh. We refer to such an approximate mesh as a bounding mesh, which includes the original mesh and has fewer vertices. Likewise, an inner bounding mesh is defined as an approximate mesh that is included by a given m ..."
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or bounding spheres and the exact mesh. Furthermore, the bounding mesh algorithm combines well with approximate convex decomposition to generate a bounding set of convexes with very few vertices, which is an efficient data structure for intersection, distance and normal computation, as well as other
Efficient Generation of Test Data Structures using Constraint Logic Programming and Program Transformation
, 2014
"... The goal of BoundedExhaustive Testing (BET) is the automatic generation of all test cases satisfying a given invariant, within a given size bound. When the test cases have a complex structure, the development of correct and efficient generators becomes a very challenging task. In this paper we use ..."
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The goal of BoundedExhaustive Testing (BET) is the automatic generation of all test cases satisfying a given invariant, within a given size bound. When the test cases have a complex structure, the development of correct and efficient generators becomes a very challenging task. In this paper we
Generation of test data structures using Constraint Logic Programming
, 2012
"... The goal of BoundedExhaustive Testing (BET) is the automatic generation of all the test cases satisfying a given invariant, within a given bound. When the input has a complex structure, the development of correct and efficient generators becomes a very challenging task. In this paper we use Constra ..."
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Cited by 4 (1 self)
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The goal of BoundedExhaustive Testing (BET) is the automatic generation of all the test cases satisfying a given invariant, within a given bound. When the input has a complex structure, the development of correct and efficient generators becomes a very challenging task. In this paper we use
Integral projection models for species with complex demography.
 American Naturalist
, 2006
"... abstract: Matrix projection models occupy a central role in population and conservation biology. Matrix models divide a population into discrete classes, even if the structuring trait exhibits continuous variation (e.g., body size). The integral projection model (IPM) avoids discrete classes and po ..."
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Cited by 42 (6 self)
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and then find the dominant pair. For the large matrices representing a complex integral model, it is much more efficient to compute only the dominant pair by iterating the model. Let denote the population state in generation teither n(t) one vector or the set of vectors for each component of X. Choose any
Sporadic model building for efficiency enhancement of hierarchical BOA
, 2007
"... Efficiency enhancement techniques—such as parallelization and hybridization—are among the most important ingredients of practical applications of genetic and evolutionary algorithms and that is why this research area represents an important niche of evolutionary computation. This paper describes and ..."
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Cited by 17 (9 self)
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and analyzes sporadic model building, which can be used to enhance the efficiency of the hierarchical Bayesian optimization algorithm (hBOA) and other estimation of distribution algorithms (EDAs) that use complex multivariate probabilistic models. With sporadic model building, the structure
Multiloop Position Analysis via Iterated Linear Programming
"... Abstract — This paper presents a numerical method able to isolate all configurations that an arbitrary loop linkage can adopt, within given ranges for its degrees of freedom. The procedure is general, in the sense that it can be applied to single or multiple intermingled loops of arbitrary topology. ..."
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. It is also complete, meaning that all possible solutions get accurately bounded, irrespectively of whether the analyzed linkage is rigid or mobile. The problem is tackled by formulating a system of linear, parabolic, and hyperbolic equations, which is here solved by a new strategy exploiting its structure
Multiloop Position Analysis via Iterated Linear Programming
"... Abstract — This paper presents a numerical method able to isolate all configurations that an arbitrary loop linkage can adopt, within given ranges for its degrees of freedom. The procedure is general, in the sense that it can be applied to single or multiple intermingled loops of arbitrary topology, ..."
Abstract
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, and complete, in the sense that all possible solutions get accurately bounded, irrespectively of whether the analyzed linkage is rigid or mobile. The problem is tackled by formulating a system of linear, parabolic, and hyperbolic equations, which is here solved by a new strategy exploiting its structure
Results 1  10
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154