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For the noised thermal cycles the solution is stabilized by the iterative regularization.
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We deduce a method to gain the scale parameter from the iterative regularization.
The estimation algorithm is based on the iterative regularization method and on the conjugate gradient and adjoint methods.
An inverse algorithm based on the Iterative Regularization Method (IRM) is applied in this study in determining the unknown time-dependent reaction coefficient for an autocatalytic reaction pathway by using measurements of concentration components.
Therefore the models for the control of the field development are formulated as inverse problems and the iterative regularization methods are offered for their solutions.
The outline of this paper is as follows: Section 2 provides background material about fluorescence tomography and describes the iterative regularization method that is used for the stable solution of the inverse problem.
We consider the a posteriori regularization parameter choice in the Morozov discrepancy and construct regularization solution sequences (f^{m,delta}(x)) by the Landweber iterative regularization method.
Firstly, we obtain a regularization solution by the Landweber iterative regularization method.
Let (f^{m,delta }(x)) given by (3.12) be the Landweber iterative regularization approximation solution.
The weight parameter (betain 0,1)), maintains a balance between the Bregman iterative regularization method and the dual denoising method.
As for (beta = 1), the model becomes the Bregman iterative regularization model.
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