Accelerated Iterative Regularization via Dual Diagonal Descent

We propose and analyze an accelerated iterative dual diagonal descent algorithm for the solution of linear inverse problems with strongly convex regularization general data-fit functions. develop inertial approach which we both convergence stability properties. Using tools from inexact proximal calculus, prove early stopping results optimal rates additive data terms further consider more cases, such as Kullback--Leibler divergence, different type point approximations hold.