Variational Source Conditions, Quadratic Inverse Problems, Sparsity Promoting Regularization

The book collects and contributes new results on the theory and practice of ill-posed inverseproblems.Different notions of ill-posedness in Banach spaces for linear and nonlinear inverse problems arediscussed not only in standard settings but also in situations up to now not covered by the literature.Especially, ill-posedness of linear operators with uncomplemented null spaces is examined.Tools for convergence rate analysis of regularization methods are extended to a wider field ofapplicability. It is shown that the tool known as variational source condition always yieldsconvergence rate results.A theory for nonlinear inverse problems with quadratic structure is developed as well ascorresponding regularization methods. The new methods are applied to a difficult inverse problemfrom laser optics.Sparsity promoting regularization is examined in detail from a Banach space point of view. Extensiveconvergence analysis reveals new insights into the behavior of Tikhonov-type regularization withsparsity enforcing penalty.