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Many algorithms have been developed to efficiently mine high-utility itemsets (HUIs) from a static database.
The experimental methodologies for extracting the essential chemistry in CVD reactors had been developed to efficiently design CVD reactors.
Dynamically adaptive numerical methods have been developed to efficiently solve differential equations whose solutions are intermittent in both space and time.
There are many algorithms that have been developed to efficiently mine frequent patterns, such as Apriori, Eclat, FP-Growth, PrePost, and FIN.
The multiscale finite volume (MsFV) method has been developed to efficiently solve large heterogeneous problems (elliptic or parabolic); it is usually employed for pressure equations and delivers conservative flux fields to be used in transport problems.
Even though a number of algorithms have been developed to efficiently parallelize frequent pattern discovery algorithms that are based on the candidate-generation-and-counting framework, the problem of parallelizing the more efficient projection-based algorithms has received relatively little attention and existing parallel formulations have been targeted only toward shared-memory architectures.
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A strategy is developed to efficiently simulate the faulty solutions until their moment of detection.
A Lagrangian relaxation and decomposition solution approach is developed to efficiently solve this problem.
An aggregation and greedy algorithm is developed to efficiently solve the pricing subproblem.
A modified nonconvex generalized Benders decomposition (NGBD) method is developed to efficiently solve this problem.
A customized solution algorithm based on Lagrangian relaxation was developed to efficiently solve this model.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com