A regularization of an inverse problem is a family of continuous maps , , such that
for every in the domain of the Moore–Penrose inverse of an operator. A linear regularization has each bounded and linear. For example, Tikhonov regularization gives
A nonlinear example is quartic-norm variational regularization:
Its strictly convex norm penalty selects the same minimum-norm least-squares solution as , but the map from data to minimizer is generally nonlinear.
A convergent regularization of an inverse problem additionally has a parameter rule such that, for every admissible exact ,
An a priori regularization parameter choice depends only on and fixed information about the problem.