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Let L : ℝn+1→ ℝ n be a linear transformation.
Let and be fixed complex numbers with and let be a linear transformation defined by (4.1).
Let M ∈ N ∖ { 0 } and L : R n → R M be a linear transformation.
In representational measurement theory, the current theory of all measurements, calibration and sampling processes are assumed to be a linear transformation of the coordinate system, of no effect.
Let (mathbb{C}^{m}) and (mathbb{C}^{n}) be m and n-dimensional inner product spaces over the complex field, respectively, and (Ainmathbb{C}^{mtimes n}) be a linear transformation from (mathbb{C}^{n}) into (mathbb{C}^{m}).
end{aligned} (B.3)The following extends a theorem of Bapat and Sunder [5]: Let (Phi :mathbf{M}_nrightarrow mathbf{M}_n) be a linear transformation such that (Phi (A) ge 0) for all (A ge 0), (Phi (mathbf{1}) = mathbf{1}) and (mathrm{Tr}[Phi (A)] = mathrm{Tr}[A]) for all (A in mathbf{M}_n).
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The z-transformation is a linear transformation whereby the transformed mean is 0 and the standard deviation is 1.
Since the DFT is a linear transformation, averaging the Fourier-transformed time series is equivalent to performing a Fourier transformation on the average of the time series.
One of the underlying assumptions of PCA is linearity, such that the feature space is a linear transformation of the original data.
In (1.4), is a linear transformation.
However, is a linear transformation, moreover; it is obviuos that whenever and hence is injective.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com