# NAG CL Interfacef08jhc (dstedc)

Settings help

CL Name Style:

## 1Purpose

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

## 2Specification

 #include
 void f08jhc (Nag_OrderType order, Nag_ComputeEigVecsType compz, Integer n, double d[], double e[], double z[], Integer pdz, NagError *fail)
The function may be called by the names: f08jhc, nag_lapackeig_dstedc or nag_dstedc.

## 3Description

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 $\Lambda$ is a diagonal matrix whose diagonal elements are the eigenvalues, ${\lambda }_{i}$, of $T$ and $Z$ is an orthogonal matrix whose columns are the eigenvectors, ${z}_{i}$, 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
 $A = (QZ) Λ (QZ)T .$
In this case $Q$ must be formed explicitly and passed to f08jhc in the array z, and the function called with ${\mathbf{compz}}=\mathrm{Nag_OrigEigVecs}$. Functions which may be called to form $T$ and $Q$ are
 full matrix f08fec and f08ffc full matrix, packed storage f08gec and f08gfc band matrix f08hec, with ${\mathbf{vect}}=\mathrm{Nag_FormQ}$
When only eigenvalues are required then this function calls 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 f08jec, although more storage is required.

## 4References

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 https://www.netlib.org/lapack/lug
Golub G H and Van Loan C F (1996) Matrix Computations (3rd Edition) Johns Hopkins University Press, Baltimore

## 5Arguments

1: $\mathbf{order}$Nag_OrderType Input
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 ${\mathbf{order}}=\mathrm{Nag_RowMajor}$. See Section 3.1.3 in the Introduction to the NAG Library CL Interface for a more detailed explanation of the use of this argument.
Constraint: ${\mathbf{order}}=\mathrm{Nag_RowMajor}$ or $\mathrm{Nag_ColMajor}$.
2: $\mathbf{compz}$Nag_ComputeEigVecsType Input
On entry: indicates whether the eigenvectors are to be computed.
${\mathbf{compz}}=\mathrm{Nag_NotEigVecs}$
Only the eigenvalues are computed (and the array z is not referenced).
${\mathbf{compz}}=\mathrm{Nag_OrigEigVecs}$
The eigenvalues and eigenvectors of $A$ are computed (and the array z must contain the matrix $Q$ on entry).
${\mathbf{compz}}=\mathrm{Nag_TridiagEigVecs}$
The eigenvalues and eigenvectors of $T$ are computed (and the array z is initialized by the function).
Constraint: ${\mathbf{compz}}=\mathrm{Nag_NotEigVecs}$, $\mathrm{Nag_OrigEigVecs}$ or $\mathrm{Nag_TridiagEigVecs}$.
3: $\mathbf{n}$Integer Input
On entry: $n$, the order of the symmetric tridiagonal matrix $T$.
Constraint: ${\mathbf{n}}\ge 0$.
4: $\mathbf{d}\left[\mathit{dim}\right]$double Input/Output
Note: the dimension, dim, of the array d must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
On entry: the diagonal elements of the tridiagonal matrix.
On exit: if ${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_NOERROR, the eigenvalues in ascending order.
5: $\mathbf{e}\left[\mathit{dim}\right]$double Input/Output
Note: the dimension, dim, of the array e must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}-1\right)$.
On entry: the subdiagonal elements of the tridiagonal matrix.
On exit: e is overwritten.
6: $\mathbf{z}\left[\mathit{dim}\right]$double Input/Output
Note: the dimension, dim, of the array z must be at least
• $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{pdz}}×{\mathbf{n}}\right)$ when ${\mathbf{compz}}=\mathrm{Nag_OrigEigVecs}$ or $\mathrm{Nag_TridiagEigVecs}$;
• $1$ otherwise.
if ${\mathbf{compz}}=\mathrm{Nag_OrigEigVecs}$ then the$\left(i,j\right)$th element of the matrix $Q$ is stored in
• ${\mathbf{z}}\left[\left(j-1\right)×{\mathbf{pdz}}+i-1\right]$ when ${\mathbf{order}}=\mathrm{Nag_ColMajor}$;
• ${\mathbf{z}}\left[\left(i-1\right)×{\mathbf{pdz}}+j-1\right]$ when ${\mathbf{order}}=\mathrm{Nag_RowMajor}$.
On entry: if ${\mathbf{compz}}=\mathrm{Nag_OrigEigVecs}$, z must contain the orthogonal matrix $Q$ used in the reduction to tridiagonal form.
On exit: if ${\mathbf{compz}}=\mathrm{Nag_OrigEigVecs}$, z contains the orthonormal eigenvectors of the original symmetric matrix $A$, and if ${\mathbf{compz}}=\mathrm{Nag_TridiagEigVecs}$, z contains the orthonormal eigenvectors of the symmetric tridiagonal matrix $T$.
If ${\mathbf{compz}}=\mathrm{Nag_NotEigVecs}$, z is not referenced.
7: $\mathbf{pdz}$Integer Input
On entry: the stride separating row or column elements (depending on the value of order) in the array z.
Constraints:
• if ${\mathbf{compz}}=\mathrm{Nag_OrigEigVecs}$ or $\mathrm{Nag_TridiagEigVecs}$, ${\mathbf{pdz}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$;
• otherwise ${\mathbf{pdz}}\ge 1$.
8: $\mathbf{fail}$NagError * Input/Output
The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).

## 6Error Indicators and Warnings

NE_ALLOC_FAIL
Dynamic memory allocation failed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
On entry, argument $⟨\mathit{\text{value}}⟩$ had an illegal value.
NE_CONVERGENCE
The algorithm failed to compute an eigenvalue while working on the submatrix lying in rows and columns $⟨\mathit{\text{value}}⟩/\left({\mathbf{n}}+1\right)$ through .
NE_ENUM_INT_2
On entry, ${\mathbf{compz}}=⟨\mathit{\text{value}}⟩$, ${\mathbf{pdz}}=⟨\mathit{\text{value}}⟩$ and ${\mathbf{n}}=⟨\mathit{\text{value}}⟩$.
Constraint: if ${\mathbf{compz}}=\mathrm{Nag_OrigEigVecs}$ or $\mathrm{Nag_TridiagEigVecs}$, ${\mathbf{pdz}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$;
otherwise ${\mathbf{pdz}}\ge 1$.
NE_INT
On entry, ${\mathbf{n}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{n}}\ge 0$.
On entry, ${\mathbf{pdz}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{pdz}}>0$.
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.
See Section 7.5 in the Introduction to the NAG Library CL Interface for further information.
NE_NO_LICENCE
Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library CL Interface for further information.

## 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:
 $θ (z~i,zi) ≤ c(n)ε‖T‖2 mini≠j|λi-λj| .$
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 f08flc.

## 8Parallelism and Performance

f08jhc is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
f08jhc 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 function. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

If only eigenvalues are required, the total number of floating-point operations is approximately proportional to ${n}^{2}$. When eigenvectors are required the number of operations is bounded above by approximately the same number of operations as f08jec, but for large matrices f08jhc is usually much faster.
The complex analogue of this function is f08jvc.

## 10Example

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 f08hec.

### 10.1Program Text

Program Text (f08jhce.c)

### 10.2Program Data

Program Data (f08jhce.d)

### 10.3Program Results

Program Results (f08jhce.r)