Maximal Spaces for Approximation Rates in ℓ^1-regularization
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We study Tikhonov regularization for possibly nonlinear inverse problems with weighted ℓ^1-penalization. The forward operator, mapping from a sequence space to an arbitrary Banach space, typically an L^2-space, is assumed to satisfies a two-sided Lipschitz condition with respect to a weighted l^2-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 convergence 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 B^r_1,1.
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