A New Approach to Source Conditions in Regularization with General Residual Term

This article addresses Tikhonov-like regularization methods with convex penalty functionals for solving nonlinear ill-posed operator equations formulated in Banach or, more general, topological spaces. We present an approach for proving convergence rates that combines advantages of approximate source conditions and variational inequalities. Precisely, our technique provides both a wide range of convergence rates and the capability to handle general and not necessarily convex residual terms as well as nonsmooth operators. Initially formulated for topological spaces, the approach is extensively discussed for Banach and Hilbert space situations, showing that it generalizes some well-known convergence rates results.

[1]  S. Osher,et al.  Convergence rates of convex variational regularization , 2004 .

[2]  B. Kaltenbacher,et al.  Iterative methods for nonlinear ill-posed problems in Banach spaces: convergence and applications to parameter identification problems , 2009 .

[3]  P. Maass,et al.  Minimization of Tikhonov Functionals in Banach Spaces , 2008 .

[4]  K. Bredies,et al.  Regularization with non-convex separable constraints , 2009 .

[5]  Bernd Hofmann,et al.  On the interplay of source conditions and variational inequalities for nonlinear ill-posed problems , 2010 .

[6]  Radu Ioan Bot,et al.  An extension of the variational inequality approach for nonlinear ill-posed problems , 2009 .

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

[8]  Andreas Neubauer,et al.  On enhanced convergence rates for Tikhonov regularization of nonlinear ill-posed problems in Banach spaces , 2009 .

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

[10]  O. Scherzer,et al.  Sparse regularization with lq penalty term , 2008, 0806.3222.

[11]  O. Scherzer,et al.  A convergence rates result for Tikhonov regularization in Banach spaces with non-smooth operators , 2007 .

[12]  L. Ambrosio,et al.  Gradient Flows: In Metric Spaces and in the Space of Probability Measures , 2005 .

[13]  C. Givens,et al.  A class of Wasserstein metrics for probability distributions. , 1984 .

[14]  Convergence rates for regularization of ill-posed problems in Banach spaces by approximate source conditions , 2008 .

[15]  Tal Schuster,et al.  Nonlinear iterative methods for linear ill-posed problems in Banach spaces , 2006 .

[16]  Torsten Hein,et al.  Tikhonov regularization in Banach spaces—improved convergence rates results , 2009 .

[17]  E. Resmerita Regularization of ill-posed problems in Banach spaces: convergence rates , 2005 .

[18]  Bernd Hofmann,et al.  Approximate source conditions for nonlinear ill-posed problems—chances and limitations , 2009 .

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

[20]  O. Scherzer,et al.  Error estimates for non-quadratic regularization and the relation to enhancement , 2006 .

[21]  Dennis Trede,et al.  Optimal convergence rates for Tikhonov regularization in Besov scales , 2008 .

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

[23]  Otmar Scherzer,et al.  Variational Methods in Imaging , 2008, Applied mathematical sciences.

[24]  Ronny Ramlau,et al.  REGULARIZATION PROPERTIES OF TIKHONOV REGULARIZATION WITH SPARSITY CONSTRAINTS , 2008 .