Integer, Intent (In)	::	n, lda, lwork
Integer, Intent (Out)	::	info
Real (Kind=nag_wp), Intent (Inout)	::	a(lda,), d(), e(), tau()
Real (Kind=nag_wp), Intent (Out)	::	work(max(1,lwork))
Character (1), Intent (In)	::	uplo

C Header Interface

#include <nag.h>

void	f08fef_ (const char uplo, const Integer n, double a[], const Integer lda, double d[], double e[], double tau[], double work[], const Integer lwork, Integer *info, const Charlen length_uplo)

The routine may be called by the names f08fef, nagf_lapackeig_dsytrd or its LAPACK name dsytrd.

3 Description

f08fef reduces a real symmetric matrix

A

to symmetric tridiagonal form

T

by an orthogonal similarity transformation:

A = Q T Q^{T}

The matrix

Q

is not formed explicitly but is represented as a product of

n - 1

elementary reflectors (see the F08 Chapter Introduction for details). Routines are provided to work with

Q

in this representation (see Section 9).

4 References

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

5 Arguments

1: $uplo$ – Character(1) Input

On entry: indicates whether the upper or lower triangular part of

A

is stored.

$uplo ='U'$: The upper triangular part of $A$ is stored.
$uplo ='L'$: The lower triangular part of $A$ is stored.

Constraint:

uplo ='U'

'L'

2: $n$ – Integer Input

On entry:

n

, the order of the matrix

A

Constraint:

n \geq 0

3: $a (lda, *)$ – Real (Kind=nag_wp) array Input/Output

Note: the second dimension of the array a must be at least

\max (1, n)

On entry: the

n \times n

symmetric matrix

A

If $uplo ='U'$ , the upper triangular part of $A$ must be stored and the elements of the array below the diagonal are not referenced.
If $uplo ='L'$ , the lower triangular part of $A$ must be stored and the elements of the array above the diagonal are not referenced.

On exit: a is overwritten by the tridiagonal matrix

T

and details of the orthogonal matrix

Q

as specified by uplo.

4: $lda$ – Integer Input

On entry: the first dimension of the array a as declared in the (sub)program from which f08fef is called.

Constraint:

lda \geq \max (1, n)

5: $d (*)$ – Real (Kind=nag_wp) array Output

Note: the dimension of the array d must be at least

\max (1, n)

On exit: the diagonal elements of the tridiagonal matrix

T

6: $e (*)$ – Real (Kind=nag_wp) array Output

Note: the dimension of the array e must be at least

\max (1, n - 1)

On exit: the off-diagonal elements of the tridiagonal matrix

T

7: $tau (*)$ – Real (Kind=nag_wp) array Output

Note: the dimension of the array tau must be at least

\max (1, n - 1)

On exit: further details of the orthogonal matrix

Q

8: $work (\max (1, lwork))$ – Real (Kind=nag_wp) array Workspace

On exit: if

info = 0

work (1)

contains the minimum value of lwork required for optimal performance.

9: $lwork$ – Integer Input

On entry: the dimension of the array work as declared in the (sub)program from which f08fef is called.

lwork = −1

, a workspace query is assumed; the routine only calculates the optimal size of the work array, returns this value as the first entry of the work array, and no error message related to lwork is issued.

Suggested value: for optimal performance,

lwork \geq n \times nb

, where

nb

is the optimal block size.

Constraint:

lwork \geq 1

lwork = −1

10: $info$ – Integer Output

On exit:

info = 0

unless the routine detects an error (see Section 6).

6 Error Indicators and Warnings

$info < 0$: If $info = - i$ , argument $i$ had an illegal value. An explanatory message is output, and execution of the program is terminated.

7 Accuracy

The computed tridiagonal matrix

T

is exactly similar to a nearby matrix

(A + E)

, where

{‖ E ‖}_{2} \leq c (n) ε {‖ A ‖}_{2},

c (n)

is a modestly increasing function of

n

, and

ε

is the machine precision.

The elements of

T

themselves may be sensitive to small perturbations in

A

or to rounding errors in the computation, but this does not affect the stability of the eigenvalues and eigenvectors.

8 Parallelism and Performance

Background information to multithreading can be found in the Multithreading documentation.

f08fef is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

f08fef 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.

9 Further Comments

The total number of floating-point operations is approximately

\frac{4}{3} n^{3}

To form the orthogonal matrix

Q

f08fef may be followed by a call to f08fff :

Call dorgtr(uplo,n,a,lda,tau,work,lwork,info)

To apply

Q

to an

n \times p

real matrix

C

f08fef may be followed by a call to f08fgf . For example,

Call dormtr('Left',uplo,'No Transpose',n,p,a,lda,tau,c,ldc, &
              work,lwork,info)

forms the matrix product

Q C

The complex analogue of this routine is f08fsf.

10 Example

This example reduces the matrix

A

to tridiagonal form, where

A = (\begin{array}{r} 2.07 & 3.87 & 4.20 & - 1.15 \\ 3.87 & - 0.21 & 1.87 & 0.63 \\ 4.20 & 1.87 & 1.15 & 2.06 \\ - 1.15 & 0.63 & 2.06 & - 1.81 \end{array}) .

f08fe: FL CL CPP AD PY MB

NAG FL Interfacef08fef (dsytrd)

▸▿ Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

NAG FL Interface
f08fef (dsytrd)