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However, the sparsification process may negatively affect the learning speed of SPI-ELM; thus, we introduce an iterative matrix decomposition algorithm to address this issue.
The scheme is solved at a quasi optimal cost using the idea of superposition, a matrix decomposition algorithm, and fast Fourier transforms.
The methods are constructed so that they are: (a) of optimal accuracy, and (b) compact; that is, the collocation equations can be solved using a matrix decomposition algorithm involving only tridiagonal linear systems.
Based on the tradeoff, we can minimize the number of middle switches (under a given packet delay bound) and an overall cost metric (by translating delay into a comparable cost factor), as well as formulating criteria for choosing a proper matrix decomposition algorithm.
Another matrix decomposition algorithm which, among other uses, computes the least squares fitting of data, is the Singular Value Decomposition (SVD).
Using a matrix decomposition algorithm, based on principal component analysis, we captured >90% of the intragenetic variation for each gene.
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Oishi-Tomiyasu, R. et al. Application of matrix decomposition algorithms for singular matrices to Pawley method in Z-Rietveld.
With existing state-of-the-art traffic matrix decomposition algorithms, we can generate a set of permutations, each of which stands for the configuration of a middle switch.
In recent years, a class of tiled matrix decomposition algorithms has been proposed for out-of-memory computations and multi-core architectures including GPU-based heterogeneous systems.
Several studies have suggested the utility of matrix decomposition algorithms.[ 12, 13, 15- 17].
The objective is solved based on penalized matrix decomposition (PMD) algorithm.
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