id: 06101123
dt: j
an: 06101123
au: Noschese, Silvia; Reichel, Lothar
ti: Inverse problems for regularization matrices.
so: Numer. Algorithms 60, No. 4, 531-544 (2012).
py: 2012
pu: Springer, Dordrecht
la: EN
cc: 
ut: discrete ill-posed problems; Tikhonov regularization; penalized
    least-squares problem; regularization matrix; inverse matrix problems;
    numerical examples
ci: 
li: doi:10.1007/s11075-012-9576-8
ab: Summary: Discrete ill-posed problems are difficult to solve, because their
    solution is very sensitive to errors in the data and to round-off
    errors introduced during the solution process. Tikhonov regularization
    replaces the given discrete ill-posed problem by a nearby penalized
    least-squares problem whose solution is less sensitive to
    perturbations. The penalization term is defined by a regularization
    matrix, whose choice may affect the quality of the computed solution
    significantly. We describe several inverse matrix problems whose
    solution yields regularization matrices adapted to the desired
    solution. Numerical examples illustrate the performance of the
    regularization matrices determined.
rv: