
- •1 Introduction
- •1.1 What makes eigenvalues interesting?
- •1.2 Example 1: The vibrating string
- •1.2.1 Problem setting
- •1.2.2 The method of separation of variables
- •1.3.3 Global functions
- •1.3.4 A numerical comparison
- •1.4 Example 2: The heat equation
- •1.5 Example 3: The wave equation
- •1.6 The 2D Laplace eigenvalue problem
- •1.6.3 A numerical example
- •1.7 Cavity resonances in particle accelerators
- •1.8 Spectral clustering
- •1.8.1 The graph Laplacian
- •1.8.2 Spectral clustering
- •1.8.3 Normalized graph Laplacians
- •1.9 Other sources of eigenvalue problems
- •Bibliography
- •2 Basics
- •2.1 Notation
- •2.2 Statement of the problem
- •2.3 Similarity transformations
- •2.4 Schur decomposition
- •2.5 The real Schur decomposition
- •2.6 Normal matrices
- •2.7 Hermitian matrices
- •2.8 Cholesky factorization
- •2.9 The singular value decomposition (SVD)
- •2.10 Projections
- •2.11 Angles between vectors and subspaces
- •Bibliography
- •3 The QR Algorithm
- •3.1 The basic QR algorithm
- •3.1.1 Numerical experiments
- •3.2 The Hessenberg QR algorithm
- •3.2.1 A numerical experiment
- •3.2.2 Complexity
- •3.3 The Householder reduction to Hessenberg form
- •3.3.2 Reduction to Hessenberg form
- •3.4 Improving the convergence of the QR algorithm
- •3.4.1 A numerical example
- •3.4.2 QR algorithm with shifts
- •3.4.3 A numerical example
- •3.5 The double shift QR algorithm
- •3.5.1 A numerical example
- •3.5.2 The complexity
- •3.6 The symmetric tridiagonal QR algorithm
- •3.6.1 Reduction to tridiagonal form
- •3.6.2 The tridiagonal QR algorithm
- •3.7 Research
- •3.8 Summary
- •Bibliography
- •4.1 The divide and conquer idea
- •4.2 Partitioning the tridiagonal matrix
- •4.3 Solving the small systems
- •4.4 Deflation
- •4.4.1 Numerical examples
- •4.6 Solving the secular equation
- •4.7 A first algorithm
- •4.7.1 A numerical example
- •4.8 The algorithm of Gu and Eisenstat
- •4.8.1 A numerical example [continued]
- •Bibliography
- •5 LAPACK and the BLAS
- •5.1 LAPACK
- •5.2 BLAS
- •5.2.1 Typical performance numbers for the BLAS
- •5.3 Blocking
- •5.4 LAPACK solvers for the symmetric eigenproblems
- •5.6 An example of a LAPACK routines
- •Bibliography
- •6 Vector iteration (power method)
- •6.1 Simple vector iteration
- •6.2 Convergence analysis
- •6.3 A numerical example
- •6.4 The symmetric case
- •6.5 Inverse vector iteration
- •6.6 The generalized eigenvalue problem
- •6.7 Computing higher eigenvalues
- •6.8 Rayleigh quotient iteration
- •6.8.1 A numerical example
- •Bibliography
- •7 Simultaneous vector or subspace iterations
- •7.1 Basic subspace iteration
- •7.2 Convergence of basic subspace iteration
- •7.3 Accelerating subspace iteration
- •7.4 Relation between subspace iteration and QR algorithm
- •7.5 Addendum
- •Bibliography
- •8 Krylov subspaces
- •8.1 Introduction
- •8.3 Polynomial representation of Krylov subspaces
- •8.4 Error bounds of Saad
- •Bibliography
- •9 Arnoldi and Lanczos algorithms
- •9.2 Arnoldi algorithm with explicit restarts
- •9.3 The Lanczos basis
- •9.4 The Lanczos process as an iterative method
- •9.5 An error analysis of the unmodified Lanczos algorithm
- •9.6 Partial reorthogonalization
- •9.7 Block Lanczos
- •9.8 External selective reorthogonalization
- •Bibliography
- •10 Restarting Arnoldi and Lanczos algorithms
- •10.2 Implicit restart
- •10.3 Convergence criterion
- •10.4 The generalized eigenvalue problem
- •10.5 A numerical example
- •10.6 Another numerical example
- •10.7 The Lanczos algorithm with thick restarts
- •10.8 Krylov–Schur algorithm
- •10.9 The rational Krylov space method
- •Bibliography
- •11 The Jacobi-Davidson Method
- •11.1 The Davidson algorithm
- •11.2 The Jacobi orthogonal component correction
- •11.2.1 Restarts
- •11.2.2 The computation of several eigenvalues
- •11.2.3 Spectral shifts
- •11.3 The generalized Hermitian eigenvalue problem
- •11.4 A numerical example
- •11.6 Harmonic Ritz values and vectors
- •11.7 Refined Ritz vectors
- •11.8 The generalized Schur decomposition
- •11.9.1 Restart
- •11.9.3 Algorithm
- •Bibliography
- •12 Rayleigh quotient and trace minimization
- •12.1 Introduction
- •12.2 The method of steepest descent
- •12.3 The conjugate gradient algorithm
- •12.4 Locally optimal PCG (LOPCG)
- •12.5 The block Rayleigh quotient minimization algorithm (BRQMIN)
- •12.7 A numerical example
- •12.8 Trace minimization
- •Bibliography

8 |
|
|
|
|
CHAPTER 1. INTRODUCTION |
|
or, |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
n |
Z0 |
|
|
|
|
|
X |
1 |
p(x)Ψi′ (x)Ψj′ |
|
||
(1.18) |
i=1 ξi |
(x) + (q(x) − λ)Ψi(x)Ψj (x) dx = 0, for all j. |
|
These last equations are called the Rayleigh–Ritz–Galerkin equations. Unknown are the n values ξi and the eigenvalue λ. In matrix notation (1.18) becomes
(1.19) |
|
Ax = λMx |
|
|
with |
|
p(x)Ψi′ Ψj′ + q(x)ΨiΨj dx and mij = Z0 |
|
|
aij = Z0 |
1 |
1 |
ΨiΨj dx |
For the specific case p(x) = 1 + x and q(x) = 1 we get
akk
ak,k+1
= Z(k−1)h "(1 + x)h2 + |
x − ( h− |
|
|
# dx |
|
|
|
|
|
|
|
|
|
|
|
|||||||||||||||
kh |
|
|
1 |
|
|
|
|
|
k |
|
1)h |
|
2 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
||
+ Zkh |
"(1 + x)h2 + |
|
|
h |
|
− |
|
|
# dx = 2(n + 1 + k) + 3 n + 1 |
|||||||||||||||||||||
|
(k+1)h |
|
1 |
|
(k + 1)h |
|
x |
2 |
|
|
|
|
2 1 |
|||||||||||||||||
Zkh |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|||||
|
h2 |
|
|
|
h |
− |
· |
|
h |
|
− − 2 − |
6 n + 1 |
||||||||||||||||||
(k+1)h |
|
|
|
1 |
|
|
(k + 1)h x |
|
|
x |
− kh |
|
|
3 |
|
|
1 |
|
1 |
|
||||||||||
= |
|
(1 + x) |
|
+ |
|
|
|
|
dx = n |
|
|
k + |
|
|
|
|
In the same way we get |
1 |
4 |
... |
|
|
1 |
|||||
M = 6(n + 1) |
4 |
1 |
... 1 |
||
|
... |
||||
|
|
|
|
1 |
|
|
|
|
|
4 |
|
|
|
|
|
|
|
|
|
|
|
|
|
Notice that both matrices A and M are symmetric tridiagonal and positive definite.
1.3.3Global functions
Formally we proceed as with the finite element method. But now we choose the Ψk(x) to be functions with global support1. We could, e.g., set
Ψk(x) = sin kπx,
functions that are di erentiable and satisfy the homogeneous boundary conditions. The Ψk are eigenfunctions of the nearby problem −u′′(x) = λu(x), u(0) = u(1) = 0 corresponding to the eigenvalue k2π2. The elements of matrix A are given by
|
1 |
|
|
|
|
|
|
3 |
|
|
1 |
|
||
akk = Z0 |
1 (1 + x)k2π2 cos2 kπx + sin2 kπx dx = |
k2π2 |
+ |
, |
||||||||||
|
|
|||||||||||||
4 |
2 |
|||||||||||||
akj = |
Z0 |
(1 + x)kjπ2 cos kπx cos jπx + sin kπx sin jπx |
dx |
|||||||||||
|
|
2 |
2 |
)((−1) |
k+j |
− 1) |
|
|
|
|
|
|
|
|
= |
kj(k |
+ j |
|
, |
k = j. |
|
|
|
|
|
||||
|
|
(k2 − j2)2 |
|
|
|
|
|
|
||||||
|
|
|
|
|
6 |
|
|
|
|
|
1The support of a function f is the set of arguments x for which f(x) =6 0.