Analysis of Profile Functions for General Linear Regularization Methods

The stable approximate solution of ill-posed linear operator equations in Hilbert spaces requires regularization. Tight bounds for the noise-free part of the regularization error are constitutive for bounding the overall error. Norm bounds of the noise-free part which decrease to zero along with the regularization parameter are called profile functions and are the subject of our analysis. The interplay between properties of the regularization and certain smoothness properties of solution sets, which we shall describe in terms of sourcewise representations, is crucial for the decay of associated profile functions. On the one hand, we show that a given decay rate is possible only if the underlying true solution has appropriate smoothness. On the other hand, if smoothness fits the regularization, then decay rates are easily obtained. If smoothness does not fit, then we will measure this in terms of some distance function. Tight bounds for these allow us to obtain profile functions. Finally we study the most realistic case when smoothness is measured with respect to some operator which is related to the one governing the original equation only through a link condition. In many parts the analysis is done on geometric basis, extending classical concepts of linear regularization theory in Hilbert spaces. We emphasize intrinsic features of linear ill-posed problems which are frequently hidden in the classical analysis of such problems.

[1]  Peter Mathé,et al.  Regularization of some linear ill-posed problems with discretized random noisy data , 2006, Math. Comput..

[2]  Bernd Hofmann,et al.  Convergence rates for Tikhonov regularization based on range inclusions , 2005 .

[3]  H. Engl,et al.  Regularization of Inverse Problems , 1996 .

[4]  Markus Hegland,et al.  Variable hilbert scales and their interpolation inequalities with applications to tikhonov regularization , 1995 .

[5]  B. Hofmann,et al.  Stability rates for linear ill-posed problems with compact and non-compact operators. , 1999 .

[6]  Eberhard Schock,et al.  Approximate Solution of Ill-Posed Equations: Arbitrarily Slow Convergence vs. Superconvergence , 1985 .

[7]  Sergei V. Pereverzev,et al.  Regularization in Hilbert scales under general smoothing conditions , 2005 .

[8]  P. Mathé,et al.  Geometry of linear ill-posed problems in variable Hilbert scales Inverse Problems 19 789-803 , 2003 .

[9]  M. Z. Nashed,et al.  A NEW APPROACH TO CLASSIFICATION AND REGULARIZATION OF ILL-POSED OPERATOR EQUATIONS , 1987 .

[10]  Ulrich Tautenhahn,et al.  Optimality for ill-posed problems under general source conditions , 1998 .

[11]  Andreas Neubauer,et al.  On Converse and Saturation Results for Tikhonov Regularization of Linear Ill-Posed Problems , 1997 .

[12]  B. Hofmann,et al.  Some results and a conjecture on the degree of ill-posedness for integration operators with weights , 2005 .

[13]  B. Hofmann,et al.  Range Inclusions and Approximate Source Conditions with General Benchmark Functions , 2007 .

[14]  Bernd Hofmann,et al.  Convergence rates for Tikhonov regularization from different kinds of smoothness conditions , 2006 .

[15]  Bernd Hofmann,et al.  Approximate source conditions in Tikhonov regularization‐new analytical results and some numerical studies , 2006 .

[16]  B. Hofmann Approximate source conditions in Tikhonov–Phillips regularization and consequences for inverse problems with multiplication operators , 2006 .

[17]  Peter Mathé Saturation of Regularization Methods for Linear Ill-Posed Problems in Hilbert Spaces , 2004, SIAM J. Numer. Anal..

[18]  F. Natterer Error bounds for tikhonov regularization in hilbert scales , 1984 .

[19]  J. Baumeister Stable solution of inverse problems , 1987 .

[20]  Markus Hegland,et al.  An optimal order regularization method which does not use additional smoothness assumptions , 1992 .

[21]  Thorsten Hohage,et al.  Regularization of exponentially ill-posed problems , 2000 .

[22]  Ulrich Tautenhahn,et al.  Error Estimates for Regularization Methods in Hilbert Scales , 1996 .

[23]  A. Bakushinsky,et al.  Iterative Methods for Approximate Solution of Inverse Problems , 2005 .

[24]  Peter Mathé,et al.  Interpolation in variable Hilbert scales with application to inverse problems , 2006 .

[25]  Masahiro Yamamoto,et al.  LETTER TO THE EDITOR: One new strategy for a priori choice of regularizing parameters in Tikhonov's regularization , 2000 .

[26]  P. Mathé,et al.  Discretization strategy for linear ill-posed problems in variable Hilbert scales , 2003 .