Abstract
Parameter identification from noisy data is an ill-posed inverse problem and data noise leads to poor solutions. Regularization methods are necessary to obtain stable solutions. In this paper we introduce the regularization by means of an iteratively weighted constraint and define an algorithm to compute the weights and solve the constrained problems using as prior information the given measurements. Although this approach is general, in the present work we prove the convergence in the case of least squares data fit with ℓ2 regularization term. The data reported in the numerical experiments prove the efficiency and good quality of the results.
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