  We investigate continuous regularization methods for linear inverse problems
of static and dynamic type. These methods are based on dynamic programming
approaches for linear quadratic optimal control problems. We prove
regularization properties and also obtain rates of convergence for our methods.
A numerical example concerning a dynamical electrical impedance tomography
(EIT) problem is used to illustrate the theoretical results.
