Sentence examples for method the regularization from inspiring English sources

In our proposed GM-SMCC method, the regularization parameters α and β quantify the importance of the network regularizer and label regularizer in the objective function (4).

In the second method, the regularization is constructed from a one-dimensional regularization that is extended to multi-dimensions with a variable support depending on the orientation of the singularity relative to the computational grid.

For the a priori Fourier method, the regularization parameter is selected according to Theorem 3.1.

This algorithm is based on Korpelevich's extragradient method, the viscosity approximation method, the hybrid steepest-descent method, the regularization method, and the averaged mapping approach to the GPA.

In this paper, we introduce and analyze a relaxed iterative algorithm by virtue of Korpelevich's extragradient method, the viscosity approximation method, the hybrid steepest-descent method, the regularization method, and the averaged mapping approach to the gradient-projection algorithm.

Motivated and inspired by the above facts, we introduce and analyze a relaxed iterative algorithm by virtue of Korpelevich's extragradient method, the viscosity approximation method, the hybrid steepest-descent method, the regularization method, and the averaged mapping approach to the GPA.

The purpose of this paper is to present the general iterative method combining the regularization method and the averaged mapping approach.

In fact, both the reflection method and the regularization method are effective for both the PH problem on the real axis and the PH problem on the circumference.

A widely used and efficient method is the regularization method introduced by Liskovets [7] using the perturbative mixed variational inequality: (6).

Tables 1 and 2 gives the comparisons of the numerical results of the truncate regularization method, the Tikhonov regularization method and the quasi-boundary regularization method under the a posteriori choice rule for different ε.

Firstly, we use Examples 1 and Examples 2 to compare the numerical effects among the truncate regularization method, the Tikhonov regularization method and quasi-boundary value regularization method under the a posteriori choice rule.