Using the L-curve for determining optimal regularization parameters
Numerische Mathematik
An introduction to the mathematical theory of inverse problems
An introduction to the mathematical theory of inverse problems
Journal of Computational and Applied Mathematics
Data regularization for a backward time-fractional diffusion problem
Computers & Mathematics with Applications
A Dynamical Tikhonov Regularization for Solving Ill-posed Linear Algebraic Systems
Acta Applicandae Mathematicae: an international survey journal on applying mathematics and mathematical applications
Computers & Mathematics with Applications
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When the damped Morozov discrepancy principle is used to determine the Tikhonov regularization parameter, one should theoretically solve a nonlinear equation by some iteration process, which is generally of local convergence with large amount of computations. This paper considers an approximation of the regularization parameter under the model function framework, which solves an approximate Morozov equation with an explicit expression iteratively. For this approximation, three kinds of new model functions are proposed. The corresponding new algorithms for determining the regularization parameters are also established, with the rigorous proof of global convergence under a unified framework. Our work is a generalization and improvement of the earlier model function method [J.L. Xie, J. Zou, Inverse Problems 18 (5) (2002) 631-643]. Numerical implementations for some ill-posed problems are presented to illustrate the validity of the proposed algorithms.