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BLUP (or the mathematical equivalent genomic BLUP, GBLUP) is widely used for genomic prediction because of its computational efficiency and because it performs almost as well as nonlinear methods for within-breed prediction [ 7, 8].
Notwithstanding the opportunity of devising approximate forecast tools, we are left with a universe whose evolution is unpredictable for a reason quite different from the ones commonly adduced by resorting to standard physics, such as quantum effects or random fluctuations: it is unpredictable just because of its computational complexity.
Recently, discriminative correlation filter-based method becomes one of the popular directions in the field of visual tracking because of its computational efficiency and excellent performance, which make it especially suitable for real-time application.
In particular, the KF is popular in visual object tracking, because of its computational efficiency.
Traditional parametric approach is inadequate to analyze these large-scale systems because of its computational inefficiency.
The linear least squares (LLS) technique is widely used in time-difference-of-arrival based positioning because of its computational efficiency.
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Because of its low computational cost, we believe it is well suited for real-time implementation.
This algorithm was chosen because of its light computational weight and because it permits to achieve good accuracies even with incomplete contours [2], which can arise from occlusions or image blurring due to the bone motion.
An adaptive version of such a forward floating selection method was applied here because of its low computational complexity and because it was capable of excluding the most prominent outliers during the sequential approximation process (Fig. 1).
Because of its less computational time i.e. O(nlogn), it can be applied effectively on large scale networks.
While a compressible formulation of the bulk gas equations will yield the most accurate results, it is rather impractical for kinetics calibration because of its long computational time.
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