Maximal spaces for approximation rates in ℓ1-regularization

Abstract

We study Tikhonov regularization for possibly nonlinear inverse problems with weighted ℓ1 -penalization. The forward operator, mapping from a sequence space to an arbitrary Banach space, typically an L2 -space, is assumed to satisfy a two-sided Lipschitz condition with respect to a weighted ℓ2 -norm and the norm of the image space. We show that in this setting approximation rates of arbitrarily high Hölder-type order in the regularization parameter can be achieved, and we characterize maximal subspaces of sequences on which these rates are attained. On these subspaces the method also converges with optimal rates in terms of the noise level with the discrepancy principle as parameter choice rule. Our analysis includes the case that the penalty term is not finite at the exact solution (’oversmoothing’). As a standard example we discuss wavelet regularization in Besov spaces Br1,1 . In this setting we demonstrate in numerical simulations for a parameter identification problem in a differential equation that our theoretical results correctly predict improved rates of convergence for piecewise smooth unknown coefficients.