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The cophenetic correlation coefficient between the dendrogram and the original similarity matrix was large and significant for RAPD (r = 0.84), ISSR (r = 0.73) and AFLP (r = 0.98), giving a good degree of confidence in the association obtained for the CPTs.
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When the size of a matrix is large special numerical methods are necessary for obtaining eigenvalues and eigenvectors.
In our parallel hybrid DC (PHDC) algorithm, MMM is accelerated by using HSS matrix techniques when the intermediate matrix is large.
It is argued that if some of the singular values of the sensitivity matrix are large, then vibration localization can possibly occur.
The numerical method for solving large sparse linear least-squares estimation problems is tested and found to perform well, even when the n×k design matrix is large (nk="O(108)).
It is expected that the linear system comprising of (7), (8), and (9) is ill-conditioned, especially since the system matrix is large.
The superior convergence behavior of LS type algorithms when the size of the adjustable code matrix is large is the reason for its utilization.
When the value of (sigma) is small, the characteristic length of the matrix is large, which leads to a small density of the fracture network.
Most of the developed algorithms thus far, however, rely on the direct computation of the inverse of the input correlation (or covariance) matrix, which can be computationally very expensive particularly when the dimension of the matrix is large.
So the sparsity S of the Gabor coefficients recovered for signal representation is very small and the column dimension to row number ratio r of the measurement matrix is large enough to result in good RIP.
Although the above mentioned model can effectively improve the rate performance, it imposes additional complexity in implementing the existing (ZF or DFE) receivers, since the size of the effective channel matrix is large (2N × N).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com