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1st Edition - January 28, 1978
Author: John Todd
Editors: Ch. Blanc, A. Ghizzetti, P. Henrici
9 7 8 - 1 - 4 8 3 2 - 6 9 4 1 - 2
Basic Numerical Mathematics, Volume II: Numerical Algebra focuses on numerical algebra, with emphasis on the ideas of "controlled computational experiments" and "bad examples".… Read more
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Basic Numerical Mathematics, Volume II: Numerical Algebra focuses on numerical algebra, with emphasis on the ideas of "controlled computational experiments" and "bad examples". The existence of an orthogonal matrix which diagonalizes a real symmetric matrix is highlighted, and partitioned or block matrices are discussed, along with induced norms and inversion problems. Comprised of 12 chapters, this volume begins with an overview of the manipulation of vectors and matrices, followed by an analysis of induced norms. The reader is then introduced to the direct solution of the inversion problem, first in the context of theoretical arithmetic (that is, when round-off is disregarded) and second in the context of practical computation. Various methods of handling the characteristic value problems are also considered, together with several iterative methods for the solution of a system of linear equations. Two applications are described: the solution of a two-point boundary value problem and the solution of least squares curve fitting. The book concludes with an account of the singular value decomposition and pseudo-inverses. This monograph will be of interest to mathematicians and students of mathematics.
Notations and AbbreviationsPrefaceChapter 1. Manipulation of Vectors and MatricesChapter 2. Norms of Vectors and MatricesChapter 3. Induced NormsChapter 4. The Inversion Problem I: Theoretical ArithmeticChapter 5. The Inversion Problem II: Practical ComputationChapter 6. The Characteristic Value Problem — GeneralitiesChapter 7. The Power Method, Deflation, Inverse IterationChapter 8. Characteristic ValuesChapter 9. Iterative Methods for the Solution of Systems Ax=bChapter 10. Application: Solution of a Boundary Value ProblemChapter 11. Application: Least Squares Curve FittingChapter 12. Singular Value Decomposition and Pseudo-InversesSolutions to Selected ProblemsBibliographical RemarksIndex