Sparse variational regularization with oversmoothing penalty term in the scale of sequence spaces

Abstract

In this work, we consider a class of linear ill-posed problems with operators that map from the sequence space $ \ell_r $ ($r \ge 1$) into a Banach space and in addition satisfy a conditional stability estimate in the scale of sequence spaces $ \ell_q, \, q \ge 0 $. For the regularization of such problems in the presence of deterministic noise, we consider variational regularization with a penalty functional either of the form $ \mathcal{R} =\Vert \cdot \Vert_p^p $ for some $ p > 0 $ or in form of the counting measure $\mathcal{R}_0 = \Vert \cdot \Vert_0 $. The latter case guarantees sparsity of the corresponding regularized solutions. In this framework, we present first stability and then convergence rates for suitable a priori parameter choices. The results cover the oversmoothing situation, where the desired solution does not belong to the domain of definition of the considered penalty functional. The analysis of the oversmoothing case utilizes auxiliary elements that are defined by means of hard thresholding. Such technique can also be used for post processing to guarantee sparsity.