8,028 research outputs found
Regularization matrices for discrete ill-posed problems in several space-dimensions
Many applications in science and engineering require the solution of large linear discrete ill-posed problems that are obtained by the discretization of a Fredholm integral equation of the first kind in several space dimensions. The matrix that defines these problems is very ill conditioned and generally numerically singular, and the right-hand side, which represents measured data, is typically contaminated by measurement error. Straightforward solution of these problems is generally not meaningful due to severe error propagation. Tikhonov regularization seeks to alleviate this difficulty by replacing the given linear discrete ill-posed problem by a penalized least-squares problem, whose solution is less sensitive to the error in the right-hand side and to roundoff errors introduced during the computations. This paper discusses the construction of penalty terms that are determined by solving a matrix nearness problem. These penalty terms allow partial transformation to standard form of Tikhonov regularization problems that stem from the discretization of integral equations on a cube in several space dimensions
Regularization matrices determined by matrix nearness problems
This paper is concerned with the solution of large-scale linear discrete
ill-posed problems with error-contaminated data. Tikhonov regularization is a
popular approach to determine meaningful approximate solutions of such
problems. The choice of regularization matrix in Tikhonov regularization may
significantly affect the quality of the computed approximate solution. This
matrix should be chosen to promote the recovery of known important features of
the desired solution, such as smoothness and monotonicity. We describe a novel
approach to determine regularization matrices with desired properties by
solving a matrix nearness problem. The constructed regularization matrix is the
closest matrix in the Frobenius norm with a prescribed null space to a given
matrix. Numerical examples illustrate the performance of the regularization
matrices so obtained
Application of Fredholm integral equations inverse theory to the radial basis function approximation problem
This paper reveals and examines the relationship between the solution and stability of Fredholm integral equations and radial basis function approximation or interpolation. The underlying system (kernel) matrices are shown to have a smoothing property which is dependent on the choice of kernel. Instead of using the condition number to describe the ill-conditioning, hence only looking at the largest and smallest singular values of the matrix, techniques from inverse theory, particularly the Picard condition, show that it is understanding the exponential decay of the singular values which is critical for interpreting and mitigating instability. Results on the spectra of certain classes of kernel matrices are reviewed, verifying the exponential decay of the singular values. Numerical results illustrating the application of integral equation inverse theory are also provided and demonstrate that interpolation weights may be regarded as samplings of a weighted solution of an integral equation. This is then relevant for mapping from one set of radial basis function centers to another set. Techniques for the solution of integral equations can be further exploited in future studies to find stable solutions and to reduce the impact of errors in the data
A GCV based Arnoldi-Tikhonov regularization method
For the solution of linear discrete ill-posed problems, in this paper we
consider the Arnoldi-Tikhonov method coupled with the Generalized Cross
Validation for the computation of the regularization parameter at each
iteration. We study the convergence behavior of the Arnoldi method and its
properties for the approximation of the (generalized) singular values, under
the hypothesis that Picard condition is satisfied. Numerical experiments on
classical test problems and on image restoration are presented
Some matrix nearness problems suggested by Tikhonov regularization
The numerical solution of linear discrete ill-posed problems typically
requires regularization, i.e., replacement of the available ill-conditioned
problem by a nearby better conditioned one. The most popular regularization
methods for problems of small to moderate size are Tikhonov regularization and
truncated singular value decomposition (TSVD). By considering matrix nearness
problems related to Tikhonov regularization, several novel regularization
methods are derived. These methods share properties with both Tikhonov
regularization and TSVD, and can give approximate solutions of higher quality
than either one of these methods
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