
Numerical Linear Algebra
by Allaire, Gregoire; Kaber, Sidi Mahmoud; Trabelsi, KarimBuy Used
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Summary
Author Biography
Table of Contents
Introduction | p. 1 |
Discretization of a Differential Equation | p. 1 |
Least Squares Fitting | p. 4 |
Vibrations of a Mechanical System | p. 8 |
The Vibrating String | p. 10 |
Image Compression by the SVD Factorization | p. 12 |
Definition and Properties of Matrices | p. 15 |
Gram-Schmidt Orthonormalization Process | p. 15 |
Matrices | p. 17 |
Trace and Determinant | p. 19 |
Special Matrices | p. 20 |
Rows and Columns | p. 21 |
Row and Column Permutation | p. 22 |
Block Matrices | p. 22 |
Spectral Theory of Matrices | p. 23 |
Matrix Triangularization | p. 26 |
Matrix Diagonalization | p. 28 |
Min-Max Principle | p. 31 |
Singular Values of a Matrix | p. 33 |
Exercises | p. 38 |
Matrix Norms, Sequences, and Series | p. 45 |
Matrix Norms and Subordinate Norms | p. 45 |
Subordinate Norms for Rectangular Matrices | p. 52 |
Matrix Sequences and Series | p. 54 |
Exercises | p. 57 |
Introduction to Algorithmics | p. 61 |
Algorithms and pseudolanguage | p. 61 |
Operation Count and Complexity | p. 64 |
The Strassen Algorithm | p. 65 |
Equivalence of Operations | p. 67 |
Exercises | p. 69 |
Linear Systems | p. 71 |
Square Linear Systems | p. 71 |
Over- and Underdetermined Linear Systems | p. 75 |
Numerical Solution | p. 76 |
Floating-Point System | p. 77 |
Matrix Conditioning | p. 79 |
Conditioning of a Finite Difference Matrix | p. 85 |
Approximation of the Condition Number | p. 88 |
Preconditioning | p. 91 |
Exercises | p. 92 |
Direct Methods for Linear Systems | p. 97 |
Gaussian Elimination Method | p. 97 |
LU Decomposition Method | p. 103 |
Practical Computation of the LU Factorization | p. 107 |
Numerical Algorithm | p. 108 |
Operation Count | p. 108 |
The Case of Band Matrices | p. 110 |
Cholesky Method | p. 112 |
Practical Computation of the Cholesky Factorization | p. 113 |
Numerical Algorithm | p. 114 |
Operation Count | p. 115 |
QR Factorization Method | p. 116 |
Operation Count | p. 118 |
Exercises | p. 119 |
Least Squares Problems | p. 125 |
Motivation | p. 125 |
Main Results | p. 126 |
Numerical Algorithms | p. 128 |
Conditioning of Least Squares Problems | p. 128 |
Normal Equation Method | p. 131 |
QR Factorization Method | p. 132 |
Householder Algorithm | p. 136 |
Exercises | p. 140 |
Simple Iterative Methods | p. 143 |
General Setting | p. 143 |
Jacobi, Gauss-Seidel, and Relaxation Methods | p. 147 |
Jacobi Method | p. 147 |
Gauss-Seidel Method | p. 148 |
Successive Overrelaxation Method (SOR) | p. 149 |
The Special Case of Tridiagonal Matrices | p. 150 |
Discrete Laplacian | p. 154 |
Programming Iterative Methods | p. 156 |
Block Methods | p. 157 |
Exercises | p. 159 |
Conjugate Gradient Method | p. 163 |
The Gradient Method | p. 163 |
Geometric Interpretation | p. 165 |
Some Ideas for Further Generalizations | p. 168 |
Theoretical Definition of the Conjugate Gradient Method | p. 171 |
Conjugate Gradient Algorithm | p. 174 |
Numerical Algorithm | p. 178 |
Number of Operations | p. 179 |
Convergence Speed | p. 180 |
Preconditioning | p. 182 |
Chebyshev Polynomials | p. 186 |
Exercises | p. 189 |
Methods for Computing Eigenvalues | p. 191 |
Generalities | p. 191 |
Conditioning | p. 192 |
Power Method | p. 194 |
Jacobi Method | p. 198 |
Givens-Householder Method | p. 203 |
QR Method | p. 209 |
Lanczos Method | p. 214 |
Exercises | p. 219 |
Solutions and Programs | p. 223 |
Exercises of Chapter 2 | p. 223 |
Exercises of Chapter 3 | p. 234 |
Exercises of Chapter 4 | p. 237 |
Exercises of Chapter 5 | p. 241 |
Exercises of Chapter 6 | p. 250 |
Exercises of Chapter 7 | p. 257 |
Exercises of Chapter 8 | p. 258 |
Exercises of Chapter 9 | p. 260 |
Exercises of Chapter 10 | p. 262 |
References | p. 265 |
Index | p. 267 |
Index of Programs | p. 272 |
Table of Contents provided by Ingram. All Rights Reserved. |
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