- •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
20 |
CHAPTER 1. INTRODUCTION |
Figure 1.8: The piecewise quadratic basis functions corresponding to the edge midpoints [3]
It is possible to have xTAx = 0 for a nonzero vector x. This is the case if the constant function u = 1 is contained in S. This happens if Neumann boundary conditions ∂n∂u = 0 are posed on the whole boundary ∂Ω. Then,
X
u(x, y) = 1 = φi(x, y),
i
i.e., we have xTAx = 0 for x = [1, 1, . . . , 1].
1.6.3A numerical example
We want to determine the acoustic eigenfrequencies and corresponding modes in the interior of a car. This is of interest in the manufacturing of cars, since an appropriate shape of the form of the interior can suppress the often unpleasant droning of the motor. The problem is three-dimensional, but by separation of variables the problem can be reduced to two dimensions. If rigid, acoustically hard walls are assumed, the mathematical model of the problem is again the Laplace eigenvalue problem (1.24) together with Neumann boundary conditions. The domain is given in Fig. 1.9 where three finite element triangulations are shown with 87 (grid1), 298 (grid2), and 1095 (grid3) vertices (nodes), respectively. The
Finite element method
k |
λk(grid1) |
λk(grid2) |
λk(grid3) |
1 |
0.0000 |
-0.0000 |
0.0000 |
2 |
0.0133 |
0.0129 |
0.0127 |
3 |
0.0471 |
0.0451 |
0.0444 |
4 |
0.0603 |
0.0576 |
0.0566 |
5 |
0.1229 |
0.1182 |
0.1166 |
6 |
0.1482 |
0.1402 |
0.1376 |
7 |
0.1569 |
0.1462 |
0.1427 |
8 |
0.2162 |
0.2044 |
0.2010 |
9 |
0.2984 |
0.2787 |
0.2726 |
10 |
0.3255 |
0.2998 |
0.2927 |
|
|
|
|
|
|
|
|
Table 1.2: Numerical solutions of acoustic vibration problem
results obtained with piecewise linear polynomials are listed in Table 1.2. From the results we notice the quadratic convergence rate. The smallest eigenvalue is always zero. The corresponding eigenfunction is the constant function. This function can be represented exactly by the finite element spaces, whence its value is correct (up to rounding error).
