Abstract

This paper addresses the regularization of linear inverse problems with so-called sparsity constraints in terms of ℓ^P-penalty terms. Error estimates and convergence rates are derived. The application of the generalized gradient projection method and the semismooth Newton method to the according minimization problem is shown. Numerical experiments show the efficiency of the semismooth Newton method.