  In this paper we consider new regularization methods for linear inverse
problems of dynamic type. These methods are based on dynamic programming
techniques for linear quadratic optimal control problems. Two different
approaches are followed: a continuous and a discrete one. We prove
regularization properties and also obtain rates of convergence for the methods
derived from both approaches. A numerical example concerning the dynamic EIT
problem is used to illustrate the theoretical results.
