
doi: 10.1002/nla.1833
handle: 11383/1758469
SUMMARYLarge linear discrete ill‐posed problems with contaminated data are often solved with the aid of Tikhonov regularization. Commonly used regularization matrices are finite difference approximations of a suitable derivative and are rectangular. This paper discusses the design of square regularization matrices that can be used in iterative methods based on the Arnoldi process for large‐scale Tikhonov regularization problems. Copyright © 2012 John Wiley & Sons, Ltd.
Finite difference methods for boundary value problems involving PDEs, Iterative numerical methods for linear systems, Numerical solutions to overdetermined systems, pseudoinverses, Tikhonov regularization, Arnoldi process, smoothing operators, ill-posed problem, pseudoinverse, finite difference matrices, Ill-posedness and regularization problems in numerical linear algebra, regularization operator, numerical experiments, range restricted GMRES
Finite difference methods for boundary value problems involving PDEs, Iterative numerical methods for linear systems, Numerical solutions to overdetermined systems, pseudoinverses, Tikhonov regularization, Arnoldi process, smoothing operators, ill-posed problem, pseudoinverse, finite difference matrices, Ill-posedness and regularization problems in numerical linear algebra, regularization operator, numerical experiments, range restricted GMRES
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