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We determined that the GRA computation can be easily approximated by the two-point central difference approximation method and that approximation accuracies of less than 5 × 10− 9 can be achieved over the entire wavelength range (301.5-1698.5 nm) with Δλ = 1.5 nm and 12-digit PRA accuracy.
In addition [23], we found that the GRA computation can be easily approximated by the four-point central difference approximation method and that approximation accuracies of less than 30 × 10− 9 can be achieved over the entire wavelength range (320-1680 nm) with a step size of 10 nm.
Take for instance a popular video game character whose shape cannot be easily approximated by someone in a rubber suit.
The PPCA method relaxes the constraint condition of objective function so that the piecewise constant image is easily approximated.
Instead, the Newton Krylov method calculates the matrix vector product, which can be easily approximated by calculating the differences of the original nonlinear function.
We examine anti-windup architectures for linear systems containing what might be broadly called "non-standard actuators" – actuators which may not be easily approximated by simply a saturation or rate-limit, but which are a more complex interconnection between linear elements and sector-bounded nonlinearities.
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DQ method can easily approximate partial derivatives of any order by choosing proper basis functions.
Moreover, by replacing the grid-based discretization using the cell-based discretization, we naturally decompose the interface into segments so that we can easily approximate surface integrals.
(I'm generally no fan of the "Masterpiece Theater" genre on film or television either, but movies and television probably do better justice to novels than theater does. The eye of the film director can more easily approximate the voice of an author because his or her control of the audience's perspective is much tighter).
We can easily approximate the upper bound λ of the eigenvalue interval to the symmetric matrix (A^{T}A) from [1, 3], thus for any (x_{n ge0} ), we can obtain ({tau_{n}} in (0,{2 / lambda}) subset 0,{2 / L})), where L is the largest eigenvalue of (A^{T}A).
This is achieved by assigning integer points to each level of each risk factor so that a clinician can easily approximate ∑ βX for a specific risk factor profile by summing integer points.
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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