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A Mixed Regularization Method for Ill-Posed Problems
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In this paper we propose a mixed regularization method for ill-posed problems. This method combines iterative regularization methods and continuous regularization methods effectively. First it applies iterative regularization methods in which there is no continuous regularization parameter to solve the normal equation of the ill-posed problem. Then continuous regularization methods are applied to solve its residual problem. The presented mixed regularization algorithm is a general framework. Any iterative regularization method and continuous regularization method can be combined together to construct a mixed regularization method. Our theoretical analysis shows that the new mixed regularization method is with optimal order of error estimation and can reach the optimal order under a much wider range of the regularization parameter than the continuous regularization method such as Tikhobov regularization. Moreover, the new mixed regularization method can reduce the sensitivity of the regularization parameter and improve the solution of continuous regularization methods or iterative regularization methods. This advantage is helpful when the optimal regularization parameter is hard to choose. The numerical computations illustrate the effectiveness of our new mixed regularization method.
Global Science Press
Title: A Mixed Regularization Method for Ill-Posed Problems
Description:
In this paper we propose a mixed regularization method for ill-posed problems.
This method combines iterative regularization methods and continuous regularization methods effectively.
First it applies iterative regularization methods in which there is no continuous regularization parameter to solve the normal equation of the ill-posed problem.
Then continuous regularization methods are applied to solve its residual problem.
The presented mixed regularization algorithm is a general framework.
Any iterative regularization method and continuous regularization method can be combined together to construct a mixed regularization method.
Our theoretical analysis shows that the new mixed regularization method is with optimal order of error estimation and can reach the optimal order under a much wider range of the regularization parameter than the continuous regularization method such as Tikhobov regularization.
Moreover, the new mixed regularization method can reduce the sensitivity of the regularization parameter and improve the solution of continuous regularization methods or iterative regularization methods.
This advantage is helpful when the optimal regularization parameter is hard to choose.
The numerical computations illustrate the effectiveness of our new mixed regularization method.
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