nag_dstedc (f08jhc) (PDF version)
f08 Chapter Contents
f08 Chapter Introduction
NAG C Library Manual

NAG Library Function Document

nag_dstedc (f08jhc)

+ Contents

    1  Purpose
    7  Accuracy

1  Purpose

nag_dstedc (f08jhc) computes all the eigenvalues and, optionally, all the eigenvectors of a real n by n symmetric tridiagonal matrix, or of a real full or banded symmetric matrix which has been reduced to tridiagonal form.

2  Specification

#include <nag.h>
#include <nagf08.h>
void  nag_dstedc (Nag_OrderType order, Nag_ComputeEigVecsType compz, Integer n, double d[], double e[], double z[], Integer pdz, NagError *fail)

3  Description

nag_dstedc (f08jhc) computes all the eigenvalues and, optionally, the eigenvectors of a real symmetric tridiagonal matrix T. That is, the function computes the spectral factorization of T given by
T = Z Λ ZT ,
where Λ is a diagonal matrix whose diagonal elements are the eigenvalues, λi, of T and Z is an orthogonal matrix whose columns are the eigenvectors, zi, of T. Thus
Tzi = λi zi ,   i = 1,2,,n .
The function may also be used to compute all the eigenvalues and vectors of a real full, or banded, symmetric matrix A which has been reduced to tridiagonal form T as
A = QTQT ,
where Q is orthogonal. The spectral factorization of A is then given by
A = QZ Λ QZT .
In this case Q must be formed explicitly and passed to nag_dstedc (f08jhc) in the array z, and the function called with compz=Nag_OrigEigVecs. Functions which may be called to form T and Q are
full matrix nag_dsytrd (f08fec) and nag_dorgtr (f08ffc)
full matrix, packed storage nag_dsptrd (f08gec) and nag_dopgtr (f08gfc)
band matrix nag_dsbtrd (f08hec), with vect=Nag_FormQ
When only eigenvalues are required then this function calls nag_dsterf (f08jfc) to compute the eigenvalues of the tridiagonal matrix T, but when eigenvectors of T are also required and the matrix is not too small, then a divide and conquer method is used, which can be much faster than nag_dsteqr (f08jec), although more storage is required.

4  References

Anderson E, Bai Z, Bischof C, Blackford S, Demmel J, Dongarra J J, Du Croz J J, Greenbaum A, Hammarling S, McKenney A and Sorensen D (1999) LAPACK Users' Guide (3rd Edition) SIAM, Philadelphia http://www.netlib.org/lapack/lug
Golub G H and Van Loan C F (1996) Matrix Computations (3rd Edition) Johns Hopkins University Press, Baltimore

5  Arguments

1:     orderNag_OrderTypeInput
On entry: the order argument specifies the two-dimensional storage scheme being used, i.e., row-major ordering or column-major ordering. C language defined storage is specified by order=Nag_RowMajor. See Section 3.2.1.3 in the Essential Introduction for a more detailed explanation of the use of this argument.
Constraint: order=Nag_RowMajor or Nag_ColMajor.
2:     compzNag_ComputeEigVecsTypeInput
On entry: indicates whether the eigenvectors are to be computed.
compz=Nag_NotEigVecs
Only the eigenvalues are computed (and the array z is not referenced).
compz=Nag_TridiagEigVecs
The eigenvalues and eigenvectors of T are computed (and the array z is initialized by the function).
compz=Nag_OrigEigVecs
The eigenvalues and eigenvectors of A are computed (and the array z must contain the matrix Q on entry).
Constraint: compz=Nag_NotEigVecs, Nag_OrigEigVecs or Nag_TridiagEigVecs.
3:     nIntegerInput
On entry: n, the order of the symmetric tridiagonal matrix T.
Constraint: n0.
4:     d[dim]doubleInput/Output
Note: the dimension, dim, of the array d must be at least max1,n.
On entry: the diagonal elements of the tridiagonal matrix.
On exit: if fail.code= NE_NOERROR, the eigenvalues in ascending order.
5:     e[dim]doubleInput/Output
Note: the dimension, dim, of the array e must be at least max1,n-1.
On entry: the subdiagonal elements of the tridiagonal matrix.
On exit: e is overwritten.
6:     z[dim]doubleInput/Output
Note: the dimension, dim, of the array z must be at least
  • max1,pdz×n when compz=Nag_OrigEigVecs or Nag_TridiagEigVecs;
  • 1 otherwise.
If compz=Nag_OrigEigVecs then thei,jth element of the matrix Q is stored in
  • z[j-1×pdz+i-1] when order=Nag_ColMajor;
  • z[i-1×pdz+j-1] when order=Nag_RowMajor.
On entry: if compz=Nag_OrigEigVecs, z must contain the orthogonal matrix Q used in the reduction to tridiagonal form.
On exit: if compz=Nag_OrigEigVecs, z contains the orthonormal eigenvectors of the original symmetric matrix A, and if compz=Nag_TridiagEigVecs, z contains the orthonormal eigenvectors of the symmetric tridiagonal matrix T.
If compz=Nag_NotEigVecs, z is not referenced.
7:     pdzIntegerInput
On entry: the stride separating row or column elements (depending on the value of order) in the array z.
Constraints:
  • if compz=Nag_OrigEigVecs or Nag_TridiagEigVecs, pdz max1,n ;
  • otherwise pdz1.
8:     failNagError *Input/Output
The NAG error argument (see Section 3.6 in the Essential Introduction).

6  Error Indicators and Warnings

NE_ALLOC_FAIL
Dynamic memory allocation failed.
NE_BAD_PARAM
On entry, argument value had an illegal value.
NE_CONVERGENCE
The algorithm failed to compute an eigenvalue while working on the submatrix lying in rows and columns value/n+1 through value mod n+1.
NE_ENUM_INT_2
On entry, compz=value, pdz=value and n=value.
Constraint: if compz=Nag_OrigEigVecs or Nag_TridiagEigVecs, pdz max1,n ;
otherwise pdz1.
NE_INT
On entry, n=value.
Constraint: n0.
On entry, pdz=value.
Constraint: pdz>0.
NE_INTERNAL_ERROR
An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.

7  Accuracy

The computed eigenvalues and eigenvectors are exact for a nearby matrix T+E, where
E2 = Oε T2 ,
and ε is the machine precision.
If λi is an exact eigenvalue and λ~i is the corresponding computed value, then
λ~i - λi c n ε T2 ,
where cn is a modestly increasing function of n.
If zi is the corresponding exact eigenvector, and z~i is the corresponding computed eigenvector, then the angle θz~i,zi between them is bounded as follows:
θ z~i,zi cnεT2 minijλi-λj .
Thus the accuracy of a computed eigenvector depends on the gap between its eigenvalue and all the other eigenvalues.
See Section 4.7 of Anderson et al. (1999) for further details. See also nag_ddisna (f08flc).

8  Further Comments

If only eigenvalues are required, the total number of floating point operations is approximately proportional to n2. When eigenvectors are required the number of operations is bounded above by approximately the same number of operations as nag_dsteqr (f08jec), but for large matrices nag_dstedc (f08jhc) is usually much faster.
The complex analogue of this function is nag_zstedc (f08jvc).

9  Example

This example finds all the eigenvalues and eigenvectors of the symmetric band matrix
A = 4.99 0.04 0.22 0.00 0.04 1.05 -0.79 1.04 0.22 -0.79 -2.31 -1.30 0.00 1.04 -1.30 -0.43 .
A is first reduced to tridiagonal form by a call to nag_dsbtrd (f08hec).

9.1  Program Text

Program Text (f08jhce.c)

9.2  Program Data

Program Data (f08jhce.d)

9.3  Program Results

Program Results (f08jhce.r)


nag_dstedc (f08jhc) (PDF version)
f08 Chapter Contents
f08 Chapter Introduction
NAG C Library Manual

© The Numerical Algorithms Group Ltd, Oxford, UK. 2012