Regularization Theory for Ill-posed Problems
De Gruyter (Verlag)
978-3-11-028646-5 (ISBN)
This monograph is a valuable contribution to the highly topical and extremly productive field of regularisation 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 demontrates the current developments in the field of regularization theory, such as multiparameter regularization and regularization in learning theory.
The book is written for graduate and PhD students and researchers in mathematics, natural sciences, engeneering, and medicine.
Shuai Lu, Fudan University, Shanghai, PR China; Sergei V. Pereverzev, Johann Radon Institute for Computational and Applied Mathematics (RICAM), Austrian Academy of Sciences,Linz, Austria.
| Erscheint lt. Verlag | 17.7.2013 |
|---|---|
| Reihe/Serie | Inverse and Ill-Posed Problems Series ; 58 |
| Zusatzinfo | 35 b/w ill., 24 b/w tbl. |
| Verlagsort | Berlin/Boston |
| Sprache | englisch |
| Maße | 170 x 240 mm |
| Gewicht | 650 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Analysis |
| Mathematik / Informatik ► Mathematik ► Angewandte Mathematik | |
| Schlagworte | Balancing Principle • Blood Glucose Prediction • Convergence Rate • Discrepancy Principle • Error Bound Estimation • ill-posed problem • Ill-posed Problem; Regularization Method; Multi-parameter Regularization; Discrepancy Principle; Balancing Principle; Error Bound Estimation; Convergence Rate; Learning Theory, Meta-learning; Blood Glucose Prediction • Learning theory • Learning Theory, Meta-learning • Meta-learning • Multi-parameter Regularization • regularization method |
| ISBN-10 | 3-11-028646-7 / 3110286467 |
| ISBN-13 | 978-3-11-028646-5 / 9783110286465 |
| Zustand | Neuware |
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