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The problem in (10) to (12) is obtained by linearizing the concave parts of the constraint functions in (8) to (9) around c(0). (Note that this is actually the first-order Taylor expansion).
This is done by linearizing the concave part of (29) at the current iterate (v_{n,k}^{(i)}), where i is the index of the current iteration, obtaining the locally tight convex upper bound begin{array}{*{20}l} log_{2} (-b_{n,k}v_{n,k}+b_{n,k}+N_{0}/T_{s})leq e_{n,k}^{(i)}v_{n,k}+f_{n,k}^{(i }, end{array} (30).
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This is achieved by linearizing the "score" or the pdf approximate of the observation.
Lie symmetries are obtained by linearizing the symmetry condition about the identity, as follows.
Instead of directly solving the unconstrained optimization problem, we use some smooth concave functions to approximate the ℓ 0-norm and then linearize the concave functions.
The linear model is developed by linearizing a nonlinear model at an operating point.
Linearizing the set of Eqs.
Hence, the unknown constants can be found by linearizing Eq. (19) involves Eqs.
The analyzed library was derived from a DNA pool of in vitro SCRaMbLEd pLM495 by linearizing with NotI and SalI.
Eventually, we came to realize that we can linearize the curve by choosing C1 as large as possible.
For simplicity, we first linearize the graph by merging competing models into a common phonetic filter.
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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