Abstract
We propose a new concept of noise level:
R
(
K
*
)
\mathcal{R}(K^{*})
-noise level for ill-posed linear integral equations in Tikhonov regularization, which extends the range of regularization parameter.
This noise level allows us to choose a more suitable regularization parameter.
Moreover, we also analyze error estimates of the approximate solution with respect to this noise level.
For ill-posed integral equations, finding fast and effective numerical methods is a challenging problem.
For this, we formulate a matrix truncated strategy based on multiscale Galerkin method to generate the linear system of Tikhonov regularization for ill-posed linear integral equations, which greatly reduce the computational complexity.
To further reduce the computational cost, a fast multilevel iteration method for solving the linear system is established.
At the same time, we also prove convergence rates of the approximate solution obtained by this fast method with respect to the
R
(
K
*
)
\mathcal{R}(K^{*})
-noise level under the balance principle.
By numerical results, we show that
R
(
K
*
)
\mathcal{R}(K^{*})
-noise level is very useful and the proposed method is a fast and effective method, respectively.
A family of rules for parameter choice in Tikhonov regularization of ill-posed problems with inexact noise level
