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The minimization process is based on the direct search algorithm "DIRECT" by Jones et al. in [20].
A first minimization process is run to explore this first chain and to determine the realization associated with the smallest objective function.
The cost functional minimization process is realized by the conjugate gradient method via the finite element method solutions of the continuum direct, sensitivity, and adjoint problems.
In addition, an uncertainty minimization process is used to regulate indirectly the controller's output so that the overvoltage problem is treated in the design step.
The cost functional minimization process is realized through a non-linear conjugate gradient algorithm that utilizes finite element solutions of the continuum direct and sensitivity problems.
The minimization process is made under the stacked regression assumption that each of the two component models can be fitted alone, and then a linear regression determines the weight of each of them.
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Techniques to simplify the minimization process are developed.
The influences of the edges conditions, geometric and material parameters on the minimization process are illustrated.
To solve the convergence problems of the LM algorithm, the effect of the seq alignment on the minimization process was evaluated.
Minimization process was pursued using simulated annealing and energy minimization with CNS [35].
The minimization process was carried out using CHARMM force field.
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