This paper surveys an important class of methods that combine iterative projection methods
and variational regularization methods for large-scale inverse problems. Iterative methods …

Tikhonov regularization is commonly used for the solution of linear discrete ill-posed
problems with error-contaminated data. A regularization parameter that determines the …

Large‐scale linear systems coming from suitable discretizations of linear inverse problems
are challenging to solve. Indeed, since they are inherently ill‐posed, appropriate …

Tikhonov regularization is one of the most popular approaches to solve discrete ill-posed
problems with error-contaminated data. A regularization operator and a suitable value of a …

Tikhonov regularization for large-scale linear ill-posed problems is commonly implemented
by determining a partial Lanczos bidiagonalization of the matrix of the given system of …

We consider Tikhonov regularization of large linear discrete ill-posed problems with a
regularization operator of general form and present an iterative scheme based on a …

Tikhonov regularization of linear discrete ill-posed problems often is applied with a finite
difference regularization operator that approximates a low-order derivative. These operators …

Large linear discrete ill‐posed problems with contaminated data are often solved with the
aid of Tikhonov regularization. Commonly used regularization matrices are finite difference …

The generalized singular value decomposition (GSVD) often is used to solve Tikhonov
regularization problems with a regularization matrix without exploitable structure. This paper …

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 …