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Related to this fact, we propose to jointly optimize the prediction filters by using an algorithm that alternates between the optimization of the filters and the computation of the weights.
The close relations between the optimization of recombinant antibodies by evolutionary strategies and genetic algorithms are considered.
This study highlights the significance of the driving conditions and the conflict between the optimization of investment cost, efficiency and life cycle impact (LCA) in powertrain design optimization of these kinds of vehicles.
We start from an energy functional accounting for bulk and surface contributions coming from both constituents of the material; the relaxed energy densities, obtained via a blow-up method, are determined by a delicate interplay between the optimization of sharp interfaces and the diffusion of microcracks.
The method proposed achieves seamless integration between the optimization of structural parameters and the 3D modelling.
Using the MSE duality, the algorithm's approach is to iterate between the optimization of the uplink power using geometric programming, uplink receive filters, and downlink receive filters.
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Despite an implicit relationship between them, the optimization of multiple performance metrics in a single solution is considered an NP-complete problem [5, 6].
The use of noncrystallographic symmetry restraints between protomers and the optimization of target weights for geometry and B factor restraints greatly improved model quality and refinement statistics.
A crucial step that allows multicenter data to be combined and enables technical reproducibility of results between laboratories is the optimization of sample quality and minimization of variability due to nonbiologic (artefactual) processes.
Also the correlations between various components in the optimization of the component concentrations such as lactate, NAD+, and CaCl2 in electrolyte was observed by response surface methodology (RS M.
For the special finite dimensional case of R n in (83), the search algorithm is an iterative algorithm that alternates between data partition and the optimization of a simpler least squares problem.
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