Linear discrete ill-posed problems are difficult to solve numerically because their solution is
very sensitive to perturbations, which may stem from errors in the data and from round-off …

This paper discusses an application of partial tensor Golub–Kahan bidiagonalization to the
solution of large‐scale linear discrete ill‐posed problems based on the t‐product formalism …

This paper presents a new efficient approach for the solution of the \ell_p-\ell_q minimization
problem based on the application of successive orthogonal projections onto generalized …

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

The L-curve method is a well-known heuristic method for choosing the regularization
parameter for ill-posed problems by selecting it according to the maximal curvature of the L …

Regularization is possibly the most popular method for solving discrete ill-posed problems,
whose solution is less sensitive to the error in the observed vector in the right hand than the …

Generalized cross validation is a popular approach to determining the regularization
parameter in Tikhonov regularization. The regularization parameter is chosen by minimizing …

Tikhonov regularization is one of the most popular methods for computing an approximate
solution of linear discrete ill-posed problems with error-contaminated data. A regularization …

We consider the solution of linear discrete ill-posed systems of equations with a certain
tensor product structure. Two aspects of this kind of problems are investigated: They are …

This paper is concerned with the solution of severely ill-conditioned linear tensor equations.
These kinds of equations may arise when discretizing partial differential equations in many …