Regularization Theory for Ill-posed Problems

Selected Topics
Buch | Hardcover
XIV, 289 Seiten
2013
De Gruyter (Verlag)
978-3-11-028646-5 (ISBN)
179,95 inkl. MwSt
The Inverse and Ill-Posed Problems Series is a series of monographs publishing postgraduate level information on inverse and ill-posed problems for an international readership of professional scientists and researchers. The series aims to publish works which involve both theory and applications in, e.g., physics, medicine, geophysics, acoustics, electrodynamics, tomography, and ecology.

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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