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It is imperative that physical functioning is optimized and that the physiological reserve required for daily activities is maintained.
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Furthermore, both cognitive and somatic functioning were optimized.
This translator function is optimized through proximal gradient based optimization to find effective transformation matrices and complies with the parsimony principle.
Once a function could be found, this function is "optimized" in the sense that the AM-parameters are varied to find a optimal overall condition for the plant.
In the next step, a fitness function is optimized by the genetic algorithm (GA) program.
The single-objective function is optimized by fmincon routine in Matlab.
Structural shapes are represented by the phase field function defined in the design domain, and this function is optimized by solving a time-dependent reaction diffusion equation.
In the latter, the MIMO ambiguity function is optimized by minimizing the side lobes of the autocorrelations and cross correlations of the waveforms.
In [21], a generalized prediction step, viewed as a mapping function, is optimized by minimizing the detail signal energy given the pixel value probability conditioned to its neighbor pixel values.
The objective function is optimized over the hidden layer H and over the weights W (of the function Ψ) with the two functions below alternatively: mathbf{H}_{k-1} overset{Psi_{k-1}}{longrightarrow} mathbf{H}_{k} overset{Psi_{k}}{longrightarrow} mathbf{H}_{k+1} mathbf{H}_{k} overset{Psi_{k}}{longrightarrow} mathbf{H}_{k+1}.
A general objective function is optimized during the simulation, most generally the "sum of pairs" maximization function introduced in dynamic programming-based MSA methods.
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