Contents
Preface
Chapter 1 Introduction
1.1 Basic symbols
1.2 Basic problems in NLA
1.3 Why shall we study numerical methods?
1.4 Matrix factorizations (decompositions)
1.5 Perturbation and error analysis
1.6 Operation cost and convergence rate
Exercises
Chapter 2 Direct Methods for Linear Systems
2.1 Triangular linear systems and LU factorization
2.2 LU factorization with pivoting
2,3 Cholesky factorization
Exercises
Chapter 3 Perturbation and Error Analysis
31I Vector and matrix norms
3.2 Perturbation analysis for linear systems
3.3 Error analysis on floating point arithmetic
3.4 Error analysison partial pivoting
Exercises
Chapter 4 Least Squares Problems
4.1 Least squares problems
4.2 Orthogonal transformations
4.3 QR decomposition
Exercises
Chapter 5 Classical Iterative Methods
5.1 Jacobi and Gauss-Seidel method
5.2 Convergence analysis
5.3 Convergence rate
5.4 SOR method
Exercises
Chapter 6 Krylov Subspace Methods
6.1 Steepest descent method
6.2 Conjugate gradient method
6.3 Practical CG method and convergence analysis
6.4 Preconditioning
6.5 GMRES method
Exercises
Chapter 7 Nonsymmetric Eigenvalue Problems
7.1 Basic properties
7.2 Power method
7.3 Inverse power method
7.4 QR method
Exercises
Chapter 8 Symmetric Eigenvalue Problems
8.1 Basic spectral properties
8.2 Symmetric QR method
8.3 Jacobi method
8.4 Bisection method
8.5 Divide-and-conquer method
Exercises
Chapter 9 Applications
9.1 Introduction
9.2 Background of BVMs
9.3 Strang-type preconditioner for ODEs
9.4 Strang-type preconditioner for DDEs
9.5 Strang-type preconditioner for NDDEs
9.6 Strang-type preconditioner for SPDDEs
Bibliography
Index