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    ARTICLE

    Optimally Generalized Regularization Methods for Solving Linear Inverse Problems

    Chein-Shan Liu1

    CMC-Computers, Materials & Continua, Vol.29, No.2, pp. 103-128, 2012, DOI:10.3970/cmc.2012.029.103

    Abstract In order to solve ill-posed linear inverse problems, we modify the Tikhonov regularization method by proposing three different preconditioners, such that the resultant linear systems are equivalent to the original one, without dropping out the regularized term on the right-hand side. As a consequence, the new regularization methods can retain both the regularization effect and the accuracy of solution. The preconditioned coefficient matrix is arranged to be equilibrated or diagonally dominated to derive the optimal scales in the introduced preconditioning matrix. Then we apply the iterative scheme to find the solution of ill-posed linear inverse problem. Two theorems are proved… More >

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