# NAG FL Interfacef08jcf (dstevd)

## ▸▿ Contents

Settings help

FL Name Style:

FL Specification Language:

## 1Purpose

f08jcf computes all the eigenvalues and, optionally, all the eigenvectors of a real symmetric tridiagonal matrix. If the eigenvectors are requested, then it uses a divide-and-conquer algorithm to compute eigenvalues and eigenvectors. However, if only eigenvalues are required, then it uses the Pal–Walker–Kahan variant of the $QL$ or $QR$ algorithm.

## 2Specification

Fortran Interface
 Subroutine f08jcf ( job, n, d, e, z, ldz, work, info)
 Integer, Intent (In) :: n, ldz, lwork, liwork Integer, Intent (Out) :: iwork(max(1,liwork)), info Real (Kind=nag_wp), Intent (Inout) :: d(*), e(*), z(ldz,*) Real (Kind=nag_wp), Intent (Out) :: work(max(1,lwork)) Character (1), Intent (In) :: job
#include <nag.h>
 void f08jcf_ (const char *job, const Integer *n, double d[], double e[], double z[], const Integer *ldz, double work[], const Integer *lwork, Integer iwork[], const Integer *liwork, Integer *info, const Charlen length_job)
The routine may be called by the names f08jcf, nagf_lapackeig_dstevd or its LAPACK name dstevd.

## 3Description

f08jcf computes all the eigenvalues and, optionally, all the eigenvectors of a real symmetric tridiagonal matrix $T$. In other words, it can compute the spectral factorization of $T$ as
 $T=ZΛZT,$
where $\Lambda$ is a diagonal matrix whose diagonal elements are the eigenvalues ${\lambda }_{i}$, and $Z$ is the orthogonal matrix whose columns are the eigenvectors ${z}_{i}$. Thus
 $Tzi=λizi, i=1,2,…,n.$

## 4References

Golub G H and Van Loan C F (1996) Matrix Computations (3rd Edition) Johns Hopkins University Press, Baltimore

## 5Arguments

1: $\mathbf{job}$Character(1) Input
On entry: indicates whether eigenvectors are computed.
${\mathbf{job}}=\text{'N'}$
Only eigenvalues are computed.
${\mathbf{job}}=\text{'V'}$
Eigenvalues and eigenvectors are computed.
Constraint: ${\mathbf{job}}=\text{'N'}$ or $\text{'V'}$.
2: $\mathbf{n}$Integer Input
On entry: $n$, the order of the matrix $T$.
Constraint: ${\mathbf{n}}\ge 0$.
3: $\mathbf{d}\left(*\right)$Real (Kind=nag_wp) array Input/Output
Note: the dimension of the array d must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
On entry: the $n$ diagonal elements of the tridiagonal matrix $T$.
On exit: the eigenvalues of the matrix $T$ in ascending order.
4: $\mathbf{e}\left(*\right)$Real (Kind=nag_wp) array Input/Output
Note: the dimension of the array e must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
On entry: the $n-1$ off-diagonal elements of the tridiagonal matrix $T$. The $n$th element of this array is used as workspace.
On exit: e is overwritten with intermediate results.
5: $\mathbf{z}\left({\mathbf{ldz}},*\right)$Real (Kind=nag_wp) array Output
Note: the second dimension of the array z must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$ if ${\mathbf{job}}=\text{'V'}$ and at least $1$ if ${\mathbf{job}}=\text{'N'}$.
On exit: if ${\mathbf{job}}=\text{'V'}$, z is overwritten by the orthogonal matrix $Z$ which contains the eigenvectors of $T$.
If ${\mathbf{job}}=\text{'N'}$, z is not referenced.
6: $\mathbf{ldz}$Integer Input
On entry: the first dimension of the array z as declared in the (sub)program from which f08jcf is called.
Constraints:
• if ${\mathbf{job}}=\text{'V'}$, ${\mathbf{ldz}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$;
• if ${\mathbf{job}}=\text{'N'}$, ${\mathbf{ldz}}\ge 1$.
7: $\mathbf{work}\left(\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{lwork}}\right)\right)$Real (Kind=nag_wp) array Workspace
On exit: if ${\mathbf{info}}={\mathbf{0}}$, ${\mathbf{work}}\left(1\right)$ contains the required minimal size of lwork.
8: $\mathbf{lwork}$Integer Input
On entry: the dimension of the array work as declared in the (sub)program from which f08jcf is called.
If ${\mathbf{lwork}}=-1$, a workspace query is assumed; the routine only calculates the minimum dimension of the work array, returns this value as the first entry of the work array, and no error message related to lwork is issued.
Constraints:
• if ${\mathbf{job}}=\text{'N'}$ or ${\mathbf{n}}\le 1$, ${\mathbf{lwork}}\ge 1$ or ${\mathbf{lwork}}=-1$;
• if ${\mathbf{job}}=\text{'V'}$ and ${\mathbf{n}}>1$, ${\mathbf{lwork}}\ge {{\mathbf{n}}}^{2}+4×{\mathbf{n}}+1$ or ${\mathbf{lwork}}=-1$.
9: $\mathbf{iwork}\left(\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{liwork}}\right)\right)$Integer array Workspace
On exit: if ${\mathbf{info}}={\mathbf{0}}$, ${\mathbf{iwork}}\left(1\right)$ contains the required minimal size of liwork.
10: $\mathbf{liwork}$Integer Input
On entry: the dimension of the array iwork as declared in the (sub)program from which f08jcf is called.
If ${\mathbf{liwork}}=-1$, a workspace query is assumed; the routine only calculates the minimum dimension of the iwork array, returns this value as the first entry of the iwork array, and no error message related to liwork is issued.
Constraints:
• if ${\mathbf{job}}=\text{'N'}$ or ${\mathbf{n}}\le 1$, ${\mathbf{liwork}}\ge 1$ or ${\mathbf{liwork}}=-1$;
• if ${\mathbf{job}}=\text{'V'}$ and ${\mathbf{n}}>1$, ${\mathbf{liwork}}\ge 5×{\mathbf{n}}+3$ or ${\mathbf{liwork}}=-1$.
11: $\mathbf{info}$Integer Output
On exit: ${\mathbf{info}}=0$ unless the routine detects an error (see Section 6).

## 6Error Indicators and Warnings

${\mathbf{info}}<0$
If ${\mathbf{info}}=-i$, argument $i$ had an illegal value. An explanatory message is output, and execution of the program is terminated.
${\mathbf{info}}>0$
The algorithm failed to converge; $⟨\mathit{\text{value}}⟩$ off-diagonal elements of e did not converge to zero.

## 7Accuracy

The computed eigenvalues and eigenvectors are exact for a nearby matrix $\left(T+E\right)$, where
 $‖E‖2 = O(ε) ‖T‖2 ,$
and $\epsilon$ is the machine precision.
If ${\lambda }_{i}$ is an exact eigenvalue and ${\stackrel{~}{\lambda }}_{i}$ is the corresponding computed value, then
 $|λ~i-λi| ≤ c (n) ε ‖T‖2 ,$
where $c\left(n\right)$ is a modestly increasing function of $n$.
If ${z}_{i}$ is the corresponding exact eigenvector, and ${\stackrel{~}{z}}_{i}$ is the corresponding computed eigenvector, then the angle $\theta \left({\stackrel{~}{z}}_{i},{z}_{i}\right)$ between them is bounded as follows:
 $θ (z~i,zi) ≤ c (n) ε ‖T‖2 min i≠j |λi-λj| .$
Thus the accuracy of a computed eigenvector depends on the gap between its eigenvalue and all the other eigenvalues.

## 8Parallelism and Performance

f08jcf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
f08jcf makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.
Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this routine. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

There is no complex analogue of this routine.

## 10Example

This example computes all the eigenvalues and eigenvectors of the symmetric tridiagonal matrix $T$, where
 $T = ( 1.0 1.0 0.0 0.0 1.0 4.0 2.0 0.0 0.0 2.0 9.0 3.0 0.0 0.0 3.0 16.0 ) .$

### 10.1Program Text

Program Text (f08jcfe.f90)

### 10.2Program Data

Program Data (f08jcfe.d)

### 10.3Program Results

Program Results (f08jcfe.r)