$uplo ='U'$: The upper triangular part of $A$ is stored and $A$ is factorized as $P U D U^{T} P^{T}$ , where $U$ is upper triangular.
$uplo ='L'$: The lower triangular part of $A$ is stored and $A$ is factorized as $P L D L^{T} P^{T}$ , where $L$ is lower triangular.

Constraint:

uplo ='U'

'L'

2: $a (lda :)$ – complex array

The first dimension of the array a must be at least

\max (1, n)

The second dimension of the array a must be at least

\max (1, n)

The

n

n

symmetric indefinite 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.

Optional Input Parameters

1: $n$ – int64int32nag_int scalar: Default: the first dimension of the array a and the second dimension of the array a.
$n$ , the order of the matrix $A$ .

Constraint: $n \geq 0$ .

Output Parameters

1: $a (lda :)$ – complex array

The first dimension of the array a will be

\max (1, n)

The second dimension of the array a will be

\max (1, n)

The upper or lower triangle of

A

stores details of the block diagonal matrix

D

and the multipliers used to obtain the factor

U

L

as specified by uplo.

2: $ipiv (:)$ – int64int32nag_int array

The dimension of the array ipiv will be

\max (1, n)

Details of the interchanges and the block structure of

D

. More precisely,

if $ipiv (i) = k > 0$ , $d_{i i}$ is a $1$ by $1$ pivot block and the $i$ th row and column of $A$ were interchanged with the $k$ th row and column;
if $uplo ='U'$ and $ipiv (i - 1) = ipiv (i) = - l < 0$ , $(\begin{matrix} d_{i - 1, i - 1} & {\bar{d}}_{i, i - 1} \\ {\bar{d}}_{i, i - 1} & d_{i i} \end{matrix})$ is a $2$ by $2$ pivot block and the $(i - 1)$ th row and column of $A$ were interchanged with the $l$ th row and column;
if $uplo ='L'$ and $ipiv (i) = ipiv (i + 1) = - m < 0$ , $(\begin{matrix} d_{i i} & d_{i + 1, i} \\ d_{i + 1, i} & d_{i + 1, i + 1} \end{matrix})$ is a $2$ by $2$ pivot block and the $(i + 1)$ th row and column of $A$ were interchanged with the $m$ th row and column.

3: $info$ – int64int32nag_int scalar

info = 0

unless the function detects an error (see Error Indicators and Warnings).

Error Indicators and Warnings

Cases prefixed with W are classified as warnings and do not generate an error of type NAG:error_n. See nag_issue_warnings.

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

W $info > 0$: Element $_$ of the diagonal is exactly zero. The factorization has been completed, but the block diagonal matrix $D$ is exactly singular, and division by zero will occur if it is used to solve a system of equations.

Accuracy

uplo ='U'

, the computed factors

U

and

D

are the exact factors of a perturbed matrix

A + E

, where

|E| \leq c (n) ε P |U| |D| |U^{T}| P^{T},

c (n)

is a modest linear function of

n

, and

ε

is the machine precision.

uplo ='L'

, a similar statement holds for the computed factors

L

and

D

Further Comments

The elements of

D

overwrite the corresponding elements of

A

; if

D

has

2

2

blocks, only the upper or lower triangle is stored, as specified by uplo.

The unit diagonal elements of

U

L

and the

2

2

unit diagonal blocks are not stored. The remaining elements of

U

L

are stored in the corresponding columns of the array a, but additional row interchanges must be applied to recover

U

L

explicitly (this is seldom necessary). If

ipiv (i) = i

, for

i = 1, 2, \dots, n

, then

U

L

is stored explicitly (except for its unit diagonal elements which are equal to

1

The total number of real floating-point operations is approximately

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

A call to nag_lapack_zsytrf (f07nr) may be followed by calls to the functions:

nag_lapack_zsytrs (f07ns) to solve $A X = B$ ;
nag_lapack_zsycon (f07nu) to estimate the condition number of $A$ ;
nag_lapack_zsytri (f07nw) to compute the inverse of $A$ .

The real analogue of this function is nag_lapack_dsytrf (f07md).

Example

This example computes the Bunch–Kaufman factorization of the matrix

A

, where

A = (\begin{array}{r} - 0.39 - 0.71 i & 5.14 - 0.64 i & - 7.86 - 2.96 i & 3.80 + 0.92 i \\ 5.14 - 0.64 i & 8.86 + 1.81 i & - 3.52 + 0.58 i & 5.32 - 1.59 i \\ - 7.86 - 2.96 i & - 3.52 + 0.58 i & - 2.83 - 0.03 i & - 1.54 - 2.86 i \\ 3.80 + 0.92 i & 5.32 - 1.59 i & - 1.54 - 2.86 i & - 0.56 + 0.12 i \end{array}) .

Open in the MATLAB editor: f07nr_example

function f07nr_example


fprintf('f07nr example results\n\n');

% Symmetric indefinite matrix A (Upper triangular part stored)
uplo = 'L';
a = [-0.39 - 0.71i,  0    + 0i,     0    + 0i,     0    + 0i;
      5.14 - 0.64i,  8.86 + 1.81i,  0    + 0i,     0    + 0i;
     -7.86 - 2.96i, -3.52 + 0.58i, -2.83 - 0.03i,  0    + 0i;
      3.80 + 0.92i,  5.32 - 1.59i, -1.54 - 2.86i, -0.56 + 0.12i];

[af, ipiv, info] = f07nr( ...
                         uplo, a);

[ifail] = x04da( ...
                 uplo, 'Non-unit', af, 'Details of factorization');

fprintf('\nPivot indices\n   ');
fprintf('%11d', ipiv);
fprintf('\n');

f07nr example results

 Details of factorization
             1          2          3          4
 1     -0.3900
       -0.7100

 2     -7.8600    -2.8300
       -2.9600    -0.0300

 3      0.5279    -0.6078     4.4079
       -0.3715     0.2811     5.3991

 4      0.4426    -0.4823    -0.1071    -2.0954
        0.1936     0.0150    -0.3157    -2.2011

Pivot indices
            -3         -3          3          4

PDF version (NAG web site, 64-bit version, 64-bit version)

Chapter Contents

Chapter Introduction

NAG Toolbox