Choosing Regularization Parameters in Iterative Methods for Ill-Posed Problems
Author:
Publisher
Society for Industrial & Applied Mathematics (SIAM)
Subject
Analysis
Link
http://epubs.siam.org/doi/pdf/10.1137/S0895479899345960
Reference28 articles.
1. A bidiagonalization algorithm for solving large and sparse ill-posed systems of linear equations
2. An implicit shift bidiagonalization algorithm for ill-posed systems
3. Galerkin Projection Methods for Solving Multiple Linear Systems
4. The “Minimum Reconstruction Error” Choice of Regularization Parameters: Some More Efficient Methods and Their Application to Deconvolution Problems
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