Maximal Spaces for Approximation Rates in $\ell^1$-regularization

TL;DR

This paper investigates the maximal subspaces where $ ext{l}^1$-regularization achieves optimal approximation and convergence rates, including oversmoothing cases, with applications to wavelet regularization and inverse problems.

Contribution

It characterizes maximal subspaces for high-order approximation rates in $ ext{l}^1$-regularization and demonstrates optimal convergence in nonlinear inverse problems, including oversmoothing scenarios.

Findings

Achieves arbitrarily high H"older-type approximation rates.

Identifies maximal subspaces where optimal rates are attained.

Numerical simulations confirm theoretical convergence improvements.

Abstract

We study Tikhonov regularization for possibly nonlinear inverse problems with weighted $\ell^1$-penalization. The forward operator, mapping from a sequence space to an arbitrary Banach space, typically an $L^2$-space, is assumed to satisfy a two-sided Lipschitz condition with respect to a weighted $\ell^2$-norm and the norm of the image space. We show that in this setting approximation rates of arbitrarily high H\"older-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 $B^r_{1,1}$. 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.