New edition provides emphasis on the algebraic structure of linear iteration, not usually included in most literature
Completely renewed references
Content grew out of a series of lectures given by author
Extensive and useful appendices included
In the second edition of this classic monograph, complete with four new chapters and updated references, readers will now have access to content describing and analysing classical and modern methods with emphasis on the algebraic structure of linear iteration, which is usually ignored in other literature.
The necessary amount of work increases dramatically with the size of systems, so one has to search for algorithms that most efficiently and accurately solve systems of, e.g., several million equations. The choice of algorithms depends on the special properties the matrices in practice have. An important class of large systems arises from the discretization of partial differential equations. In this case, the matrices are sparse (i.e., they contain mostly zeroes) and well-suited to iterative algorithms.
The first edition of this book grew out of a series of lectures given by the author at the Christian-Albrecht University of Kiel to students of mathematics. The second edition includes quite novel approaches.
Topics
Numerical Analysis
Linear and Multilinear Algebras, Matrix Theory
Partial Differential Equations
Author(s): Wolfgang Hackbusch
Series: Applied Mathematical Sciences 95
Edition: 2nd ed. 2016
Publisher: Springer
Year: 2016
Language: English
Pages: C,XXIII,509
Tags: Numerical Analysis; Linear and Multilinear Algebras, Matrix Theory; Partial Differential Equations
Front Matter....Pages i-xxiii
Front Matter....Pages 1-2
Introduction....Pages 3-16
Iterative Methods....Pages 17-34
Classical Linear Iterations in the Positive Definite Case....Pages 35-67
Analysis of Classical Iterations Under Special Structural Conditions....Pages 69-88
Algebra of Linear Iterations....Pages 89-122
Analysis of Positive Definite Iterations....Pages 123-136
Genenration of Iterations....Pages 137-172
Front Matter....Pages 173-174
Semi-Iterative Methods....Pages 175-209
Gradient Method....Pages 211-228
Conjugate Gradient Methods and Generalisations....Pages 229-262
Front Matter....Pages 263-264
Multigrid Iterations....Pages 265-324
Domain Decomposition and Subspace Methods....Pages 325-370
\(\mathcal {H}\) -LU Iteration....Pages 371-384
Tensor-based Methods....Pages 385-400
Back Matter....Pages 401-509