 Solution

A regularization of an inverse problem replaces the unbounded generalized inverse by bounded operators $R_{\alpha }:Y\to X$ satisfying $R_{\alpha }y\to A^{†}y$ as $\alpha \downarrow 0$ on admissible exact data. A spectral regularization method can be writtenwhere $f_{\alpha }(\sigma )\to 1$ for fixed $\sigma >0$ while $f_{\alpha }(\sigma )/\sigma$ remains bounded for each fixed $\alpha$. A uniform bound on $f_{\alpha }$ supplies the usual dominated-convergence argument on exact data.

This defines $f_{\alpha }$ as the dimensionless damping factor. In the alternative convention calling the entire inverse coefficient the spectral filter, that filter is $f_{\alpha }(\sigma )/\sigma$. For data error bounded by $\delta$, a sufficient regularization parameter choice also requires $\delta \Vert R_{\alpha (\delta )}\Vert \to 0$; consistency alone does not control noise.