Numerical Linear Algebra

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Format: Hardcover
Pub. Date: 2007-12-01
Publisher(s): SPRINGER - VERLAG
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Summary

This book distinguishes itself from the many other textbook on the topic of linear algebra by including mathematical and computational chapters along with examples and exercises with Matlab. In recent years, the use of computers in many areas of engineering and science has made it essential for students to get training in numerical methods and computer programming.

Author Biography

Gregoire Allaire is a professor of Applied Mathematics at Ecole Polytechnique, France.

Table of Contents

Introductionp. 1
Discretization of a Differential Equationp. 1
Least Squares Fittingp. 4
Vibrations of a Mechanical Systemp. 8
The Vibrating Stringp. 10
Image Compression by the SVD Factorizationp. 12
Definition and Properties of Matricesp. 15
Gram-Schmidt Orthonormalization Processp. 15
Matricesp. 17
Trace and Determinantp. 19
Special Matricesp. 20
Rows and Columnsp. 21
Row and Column Permutationp. 22
Block Matricesp. 22
Spectral Theory of Matricesp. 23
Matrix Triangularizationp. 26
Matrix Diagonalizationp. 28
Min-Max Principlep. 31
Singular Values of a Matrixp. 33
Exercisesp. 38
Matrix Norms, Sequences, and Seriesp. 45
Matrix Norms and Subordinate Normsp. 45
Subordinate Norms for Rectangular Matricesp. 52
Matrix Sequences and Seriesp. 54
Exercisesp. 57
Introduction to Algorithmicsp. 61
Algorithms and pseudolanguagep. 61
Operation Count and Complexityp. 64
The Strassen Algorithmp. 65
Equivalence of Operationsp. 67
Exercisesp. 69
Linear Systemsp. 71
Square Linear Systemsp. 71
Over- and Underdetermined Linear Systemsp. 75
Numerical Solutionp. 76
Floating-Point Systemp. 77
Matrix Conditioningp. 79
Conditioning of a Finite Difference Matrixp. 85
Approximation of the Condition Numberp. 88
Preconditioningp. 91
Exercisesp. 92
Direct Methods for Linear Systemsp. 97
Gaussian Elimination Methodp. 97
LU Decomposition Methodp. 103
Practical Computation of the LU Factorizationp. 107
Numerical Algorithmp. 108
Operation Countp. 108
The Case of Band Matricesp. 110
Cholesky Methodp. 112
Practical Computation of the Cholesky Factorizationp. 113
Numerical Algorithmp. 114
Operation Countp. 115
QR Factorization Methodp. 116
Operation Countp. 118
Exercisesp. 119
Least Squares Problemsp. 125
Motivationp. 125
Main Resultsp. 126
Numerical Algorithmsp. 128
Conditioning of Least Squares Problemsp. 128
Normal Equation Methodp. 131
QR Factorization Methodp. 132
Householder Algorithmp. 136
Exercisesp. 140
Simple Iterative Methodsp. 143
General Settingp. 143
Jacobi, Gauss-Seidel, and Relaxation Methodsp. 147
Jacobi Methodp. 147
Gauss-Seidel Methodp. 148
Successive Overrelaxation Method (SOR)p. 149
The Special Case of Tridiagonal Matricesp. 150
Discrete Laplacianp. 154
Programming Iterative Methodsp. 156
Block Methodsp. 157
Exercisesp. 159
Conjugate Gradient Methodp. 163
The Gradient Methodp. 163
Geometric Interpretationp. 165
Some Ideas for Further Generalizationsp. 168
Theoretical Definition of the Conjugate Gradient Methodp. 171
Conjugate Gradient Algorithmp. 174
Numerical Algorithmp. 178
Number of Operationsp. 179
Convergence Speedp. 180
Preconditioningp. 182
Chebyshev Polynomialsp. 186
Exercisesp. 189
Methods for Computing Eigenvaluesp. 191
Generalitiesp. 191
Conditioningp. 192
Power Methodp. 194
Jacobi Methodp. 198
Givens-Householder Methodp. 203
QR Methodp. 209
Lanczos Methodp. 214
Exercisesp. 219
Solutions and Programsp. 223
Exercises of Chapter 2p. 223
Exercises of Chapter 3p. 234
Exercises of Chapter 4p. 237
Exercises of Chapter 5p. 241
Exercises of Chapter 6p. 250
Exercises of Chapter 7p. 257
Exercises of Chapter 8p. 258
Exercises of Chapter 9p. 260
Exercises of Chapter 10p. 262
Referencesp. 265
Indexp. 267
Index of Programsp. 272
Table of Contents provided by Ingram. All Rights Reserved.

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