Newton Methods for Nonlinear Problems : Affine Invariance and Adaptive Algorithms
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The work Newton Methods for Nonlinear Problems : Affine Invariance and Adaptive Algorithms represents a distinct intellectual or artistic creation found in University of Oklahoma Libraries. This resource is a combination of several types including: Work, Language Material, Books.
The Resource
Newton Methods for Nonlinear Problems : Affine Invariance and Adaptive Algorithms
Resource Information
The work Newton Methods for Nonlinear Problems : Affine Invariance and Adaptive Algorithms represents a distinct intellectual or artistic creation found in University of Oklahoma Libraries. This resource is a combination of several types including: Work, Language Material, Books.
 Label
 Newton Methods for Nonlinear Problems : Affine Invariance and Adaptive Algorithms
 Title remainder
 Affine Invariance and Adaptive Algorithms
 Statement of responsibility
 by Peter Deuflhard
 Subject

 Optimization
 Differential Equations
 Computer science
 Mathematical optimization
 Ordinary Differential Equations
 Computational Mathematics and Numerical Analysis
 Math Applications in Computer Science
 Engineering mathematics
 Computational Science and Engineering
 Computer science  Mathematics
 Mathematical and Computational Engineering
 Language

 eng
 eng
 Summary
 This book deals with the efficient numerical solution of challenging nonlinear problems in science and engineering, both in finite dimension (algebraic systems) and in infinite dimension (ordinary and partial differential equations). Its focus is on local and global Newton methods for direct problems or GaussNewton methods for inverse problems. The term 'affine invariance' means that the presented algorithms and their convergence analysis are invariant under one out of four subclasses of affine transformations of the problem to be solved. Compared to traditional textbooks, the distinguishing affine invariance approach leads to shorter theorems and proofs and permits the construction of fully adaptive algorithms. Lots of numerical illustrations, comparison tables, and exercises make the text useful in computational mathematics classes. At the same time, the book opens many directions for possible future research
 Dewey number
 518
 http://bibfra.me/vocab/relation/httpidlocgovvocabularyrelatorsaut
 HpJ_ljXNic
 Image bit depth
 0
 Language note
 English
 LC call number
 QA7190
 Literary form
 non fiction
 Nature of contents
 dictionaries
 Series statement
 Springer Series in Computational Mathematics,
 Series volume
 35
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