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The matrices are linear bounded operators defined on the Hilbert space (R^{n}).
These two arbitrary correlation matrices are linear and triangular, and they are referred to as Configuration A and Configuration B, respectively, in the current study.
The affine inverse quadratic eigenvalue problem (AQIEP) is the QIEP with an additional constraint that the coefficient matrices belong to an affine family, that is, these matrices are linear combinations of substructured matrices.
Another technique using matrices are linear least squares, a method that approximates a finite set of pairs (x1, y1), (x2, y2),..., (xN, yN), by a linear function :yi ≈ axi + b, i = 1,..., N which can be formulated in terms of matrices, related to the singular value decomposition of matrices.
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where the transformed A-matrices are linear on coefficients N i a Open image in new window as can be obtained from the formulas in the above presented proposition.
The assay for each matrix was linear within the quantification range with determination coefficient (r2) values above 0.995.
Contrary to the quadratic increase in clustering time with the increase of the number of data points (i.e., rows of the data matrix), the dependence of the clustering speed on the number of features (i.e., columns of the data matrix) is linear (Figure 5, top right panel).
On this basis, it can be stated that the measurement of polyether ionophores in the matrix was linear over the actual studied ranges (0.010 2.0 mg/kg).
The proposed feature is obtained by transform matrices that are linear and time-invariant.
Elements of the matrix A are linear functions of the Gauss coefficients for MF, and m is the vector of the flow coefficients (Bloxham, 1988).
The entries of the matrix G are linear combinations of the complex variables x1,…2,x,x k and their conjugates; these variables are actually the source (data) symbols to be transmitted.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com