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This monograph is a valuable contribution to the highly topical and extremely productive field of regularization methods for inverse and ill-posed problems. The author is an internationally outstanding and accepted mathematician in this field. In his book he offers a well-balanced mixture of basic and innovative aspects. He demonstrates new, differentiated viewpoints, and important examples for applications. The book demonstrates the current developments in the field of regularization theory, such as multi parameter regularization and regularization in learning theory. The book is written for graduate and PhDs

Numerical analysis --- Numerical differentiation. --- Graphic differentiation --- Functions --- Improperly posed problems in numerical analysis --- Improperly posed problems. --- Ill-posed problems --- Balancing Principle. --- Blood Glucose Prediction. --- Convergence Rate. --- Discrepancy Principle. --- Error Bound Estimation. --- Ill-posed Problem. --- Learning Theory, Meta-learning. --- Multi-parameter Regularization. --- Regularization Method.