Abstract
Finding a global minimizer of the Tikhonov function is in general not an easy task. Numerical experience shows that the Tikhonov function has usually many local minima and a descent method for solving the optimization problem may tend to get stuck especially for severely ill-posed problems. Since furthermore, the computation of an appropriate regularization parameter can require high computational effort, iterative regularization methods are an attractive alternative.
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Doicu, A., Trautmann, T., Schreier, F. (2010). Iterative regularization methods for nonlinear problems. In: Numerical Regularization for Atmospheric Inverse Problems. Springer Praxis Books(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05439-6_7
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DOI: https://doi.org/10.1007/978-3-642-05439-6_7
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