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At each iteration, we therefore minimise the functional: Phi_{j} = left[ {mathbf d}-{mathbf A}_{d}, {mathbf g}^{j} right]^{t} {mathbf W}^{j} left[ {mathbf d}-{mathbf A}_{d}, {mathbf g}^{j} right] + sum_{i=1}^{3} lambda_{i} left[{mathbf L}_{Bi}, {mathbf g}^{j} right]^{t} left[{mathbf L}_{Bi}, {mathbf g}^{j} right]!, (13).
We note that the set midd ( B ) does not depend on the permutation σ with the property P. We consider the more general Torricelli problem, i.e. to minimise the functional T A : R → R, T A ( x ) = ∑ i = 1 n | x − a i |, where A = ( a 1, …, a n ) ∈ R n in which the points a 1, …, a n are not necessary distinct.
This difference, which can be interpreted as a clinically significant difference as it exceeds the range of 0.074 to 0.080 cited in the literature [ 37, 38], reminds us that much more can still be done to minimise the functional impact of stroke.
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Our aim is to find a unique input function which minimises the functional defined in eq. (2).
The resulting evolution equation that minimises the functional in Equation 9 is as follows: ∂Φ ∂ t = δ μ div ∇ Φ | ∇ Φ | − ν − λ 1 ( I − c 1 ) 2 + λ 2 ( I − c 2 ) 2. (9).
The computational effort has been substantially reduced by using an optimisation based on the planning of the experiments and the response surface technique in order to minimise the error functional.
The solution trajectories Y(t| p, y0) = (y(t| p, y0), x(t| p, y0)) minimise the χ functional for given dynamic parameters p and initial values y0.
This evolution minimises the energy functional E by moving p (i.e. a,b,h,θ) in a direction of decreasing energy.
The point x 0 ∈ X will be called a Torricellian point of the set { a 1, …, a n } if it minimises the Torricellian functional T, i.e. T ( x 0 ) = inf x ∈ X T ( x ) (2). or, equivalently, ∑ i = 1 n ∥ x 0 − a i ∥ ≤ ∑ i = 1 n ∥ x − a i ∥ for all x ∈ X. (3).
The solution of this system minimises the χ functional which plays a central role and is directly associated to the objective function of the original estimation problem.
This review will separate complications into those where intervention may optimise clinical status while maintaining the Fontan circulation the 'failing Fontan' and those conditions which have progressed to a 'failed Fontan', where options are limited to cardiac transplantation or attempts to minimise the impact of irreversible functional deterioration.
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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