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The comparative study indicates that a convenient and robust tool for regularized modeling of the entire localization process is provided by the implicit gradient approach combined with a suitable form of the hardening/softening law.
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This numerical analytical approach, namely Regularized Model Updating (RMU), is developed based on a hybrid constrained optimization algorithm.
Also, it is noted that the smaller the positive number ϵ is, the closer this regularized model is to its original version ((mathcal{M}_{0})).
In [5], the authors established an (l_{p}) ((0< p<1)) regularized minimization model: begin{aligned} min_{xin R^{n}} f(x):= frac {1}{2}bigl| Phi_{FB}(x bigr| ^{2}+lambda|x|_{p}^{p} end{aligned} (28) and designed a sequential smoothing gradient (SSG) method to solve the (l_{p}) regularized model and get a sparse solution of (operatorname{LCP} q,M)).
Provided the use of an additional penalty term in the numerical treatment, this model is quite efficient to regularize problems modelling behaviors exhibiting plastic volumetric strain such as the ones of geomaterials.
A Gaussian regularized dipolar model has been developed for carbon sp2 structures.
Specifically, HOD selects those samples which minimize the parameter covariance matrix of the Hessian regularized regression model.
In this paper, we propose a novel multiple graph regularized generative model to exploit the manifold structure in multiple views.
We propose a straightforward linear Gaussian approach, where decoding relies on the inversion of properly regularized encoding models, which can still be solved analytically.
We evaluated the predictive power of an elastic net regularized regression model using a receiver operating characteristic (ROC) curve, which compares the likelihood of correctly and incorrectly predicting re-arrest.
Mass-action kinetics also regularized the model parameters.
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