Convergence Analysis of (Statistical) Inverse Problems under Conditional Stability Estimates

Abstract

Conditional stability estimates require additional regularization for obtaining stable approximate solutions if the validity area of such estimates is not completely known. In this context, we consider ill-posed nonlinear inverse problems in Hilbert scales satisfying conditional stability estimates characterized by general concave index functions. For that case, we exploit Tikhonov regularization and provide convergence and convergence rates of regularized solutions for both deterministic and stochastic noise. We further discuss a priori and a posteriori parameter choice rules and illustrate the validity of our assumptions in different model and real world situations.

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• Convergence Rate Analysis of Galerkin Approximation of Inverse Potential Problem
• Convergence analysis of Tikhonov regularization for non-linear statistical inverse learning problems
• Statistical Inverse Problems in Hilbert Scales