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The combination that minimizes this function is kept as the best transformation.
Then, the function Ψ = E | h | 2 ε + ( 1 - ε ) e - θ m r ̄ (14). is strictly convex in ε, and therefore, the optimal value of ε that minimizes this function or equivalently maximizes the effective rate in (13) is unique.
Then, the function Ψ p = E | h | 2 ( ε + ( 1 - ε ) e - θ m r ̄ p ) 2 (33). is strictly convex in ε, and therefore, the optimal value of ε that minimizes this function or equivalently maximizes the effective rate in (32) is unique.
Similar(57)
The binding to find must minimize this function.
(9)The goal then is to minimize this function; therefore, we must deduce a back-propagation learning algorithm using the present cost function.
In particular, Kiwiel [9] proposed an alternating linearization bundle method for the sum of two convex functions and one of them is "simple" (i.e., minimizing this function plus a separable convex quadratic function is "easy").
To minimize this function, we used the Levenberg Marquardt algorithm.
To minimize this function, we used the Levenberg-Marquardt algorithm [ 15].
The value of x that achieves the best possible worst return is found by minimizing this function.
A key discovery that we made was that the current genomic locations of all the operons in E. coli K12 tend to minimize this function in comparison with artificially-generated alternatives [19].
The goodness of the estimated target genome is measured by a cost function, and we search for an optimal set of chromosomes that minimizes this cost function.
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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