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In the next section we present a general regularization method for VI (1.1) with the regularizer being a Lipschitz continuous and strongly monotone operator.
We provide a general regularization method for monotone variational inequalities, where the regularizer is a Lipschitz continuous and strongly monotone operator.
Recently, Xu and Xu [22] provided a general regularization method for solving MVIP (1.1), where the regularizer is a Lipschitz continuous and strongly monotone operator.
The main aim of this paper is to present a general regularization method and investigate the error estimate between the regularized solution and the exact solution.
Theorem 1 (A general regularization method).
In general, regularization is introduced to prevent over fitting when the number of observations is not sufficiently larger than that of unknown parameters.
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In theoretical results, we have suggested a general filter regularization method of regularized solution (Section 2.2).
Considering the sparsity of force in the time domain or in other basis space, we develop a general sparse regularization method based on minimizing l1-norm of the coefficient vector of basis functions.
Actually, the general alternative regularization algorithm is a variant of the viscosity algorithm (1).
According to the above two sections, our general iterative regularization procedure can be formulated as follows.
Here our research intends on focusing a sharp boundary, following general Tikhonov regularization inversion within the framework of L2 norm, which is justified by general Gaussian distribution of EM data (Fournier and Febrer 1976; Weaver et al. 2000).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com