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This regularization is performed before any discretization.
We empirically explore the power of this regularization scheme for improving training performance in various GAN formulations.
However, this regularization often suffers from inaccurate results and low computational efficiency in some situations.
Nevertheless, they necessitate the determination of a parameter; the difficulty is to calculate an appropriate value of this regularization parameter.
We study the effect of this regularization on the order of accuracy for a one-dimensional time-dependent problem.
This regularization of the NPS approach ultimately reduces the number of function evaluations required by Δ-DOGS to achieve a specified level of convergence in optimization problems characterized by parameters of varying degrees of significance.
Similar(19)
For this case regularization methods are applied to make the solution numerically stable.
Because of the ill-posedness of this problem, regularization method for example, Tikhonov regularization, is incorporated in our solution scheme.
Due to this, Tikhonov regularization is applied to solve the related matrix system.
This exact regularization property of (1.7) was studied in [19, 20].
This PTV regularization consists in applying a penalty to the MAD obtained in the previous step, directly proportional to the distance between the current and initial PTVs, i.e., the PTV available as input to this module.
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