NAG CL Interface
f07mdc (dsytrf)
1
Purpose
f07mdc computes the Bunch–Kaufman factorization of a real symmetric indefinite matrix.
2
Specification
void 
f07mdc (Nag_OrderType order,
Nag_UploType uplo,
Integer n,
double a[],
Integer pda,
Integer ipiv[],
NagError *fail) 

The function may be called by the names: f07mdc, nag_lapacklin_dsytrf or nag_dsytrf.
3
Description
f07mdc factorizes a real symmetric matrix $A$, using the Bunch–Kaufman diagonal pivoting method. $A$ is factorized as either $A=PUD{U}^{\mathrm{T}}{P}^{\mathrm{T}}$ if ${\mathbf{uplo}}=\mathrm{Nag\_Upper}$ or $A=PLD{L}^{\mathrm{T}}{P}^{\mathrm{T}}$ if ${\mathbf{uplo}}=\mathrm{Nag\_Lower}$, where $P$ is a permutation matrix, $U$ (or $L$) is a unit upper (or lower) triangular matrix and $D$ is a symmetric block diagonal matrix with $1$ by $1$ and $2$ by $2$ diagonal blocks; $U$ (or $L$) has $2$ by $2$ unit diagonal blocks corresponding to the $2$ by $2$ blocks of $D$. Row and column interchanges are performed to ensure numerical stability while preserving symmetry.
This method is suitable for symmetric matrices which are not known to be positive definite. If $A$ is in fact positive definite, no interchanges are performed and no $2$ by $2$ blocks occur in $D$.
4
References
Golub G H and Van Loan C F (1996) Matrix Computations (3rd Edition) Johns Hopkins University Press, Baltimore
5
Arguments

1:
$\mathbf{order}$ – Nag_OrderType
Input

On entry: the
order argument specifies the twodimensional storage scheme being used, i.e., rowmajor ordering or columnmajor 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:
$\mathbf{uplo}$ – Nag_UploType
Input

On entry: specifies whether the upper or lower triangular part of
$A$ is stored and how
$A$ is to be factorized.
 ${\mathbf{uplo}}=\mathrm{Nag\_Upper}$
 The upper triangular part of $A$ is stored and $A$ is factorized as $PUD{U}^{\mathrm{T}}{P}^{\mathrm{T}}$, where $U$ is upper triangular.
 ${\mathbf{uplo}}=\mathrm{Nag\_Lower}$
 The lower triangular part of $A$ is stored and $A$ is factorized as $PLD{L}^{\mathrm{T}}{P}^{\mathrm{T}}$, where $L$ is lower triangular.
Constraint:
${\mathbf{uplo}}=\mathrm{Nag\_Upper}$ or $\mathrm{Nag\_Lower}$.

3:
$\mathbf{n}$ – Integer
Input

On entry: $n$, the order of the matrix $A$.
Constraint:
${\mathbf{n}}\ge 0$.

4:
$\mathbf{a}\left[\mathit{dim}\right]$ – double
Input/Output

Note: the dimension,
dim, of the array
a
must be at least
$\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{pda}}\times {\mathbf{n}}\right)$.
On entry: the
$n$ by
$n$ symmetric indefinite matrix
$A$.
If ${\mathbf{order}}=\mathrm{Nag\_ColMajor}$, ${A}_{ij}$ is stored in ${\mathbf{a}}\left[\left(j1\right)\times {\mathbf{pda}}+i1\right]$.
If ${\mathbf{order}}=\mathrm{Nag\_RowMajor}$, ${A}_{ij}$ is stored in ${\mathbf{a}}\left[\left(i1\right)\times {\mathbf{pda}}+j1\right]$.
If ${\mathbf{uplo}}=\mathrm{Nag\_Upper}$, the upper triangular part of $A$ must be stored and the elements of the array below the diagonal are not referenced.
If ${\mathbf{uplo}}=\mathrm{Nag\_Lower}$, the lower triangular part of $A$ must be stored and the elements of the array above the diagonal are not referenced.
On exit: the upper or lower triangle of
$A$ is overwritten by details of the block diagonal matrix
$D$ and the multipliers used to obtain the factor
$U$ or
$L$ as specified by
uplo.

5:
$\mathbf{pda}$ – Integer
Input
On entry: the stride separating row or column elements (depending on the value of
order) of the matrix
$A$ in the array
a.
Constraint:
${\mathbf{pda}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.

6:
$\mathbf{ipiv}\left[\mathit{dim}\right]$ – Integer
Output

Note: the dimension,
dim, of the array
ipiv
must be at least
$\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
On exit: details of the interchanges and the block structure of
$D$. More precisely,
 if ${\mathbf{ipiv}}\left[i1\right]=k>0$, ${d}_{ii}$ 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 ${\mathbf{uplo}}=\mathrm{Nag\_Upper}$ and ${\mathbf{ipiv}}\left[i2\right]={\mathbf{ipiv}}\left[i1\right]=l<0$, $\left(\begin{array}{cc}{d}_{i1,i1}& {\overline{d}}_{i,i1}\\ {\overline{d}}_{i,i1}& {d}_{ii}\end{array}\right)$ is a $2$ by $2$ pivot block and the $\left(i1\right)$th row and column of $A$ were interchanged with the $l$th row and column;
 if ${\mathbf{uplo}}=\mathrm{Nag\_Lower}$ and ${\mathbf{ipiv}}\left[i1\right]={\mathbf{ipiv}}\left[i\right]=m<0$, $\left(\begin{array}{cc}{d}_{ii}& {d}_{i+1,i}\\ {d}_{i+1,i}& {d}_{i+1,i+1}\end{array}\right)$ is a $2$ by $2$ pivot block and the $\left(i+1\right)$th row and column of $A$ were interchanged with the $m$th row and column.

7:
$\mathbf{fail}$ – NagError *
Input/Output

The NAG error argument (see
Section 7 in the Introduction to the NAG Library CL Interface).
6
Error 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.
 NE_BAD_PARAM

On entry, argument $\u2329\mathit{\text{value}}\u232a$ had an illegal value.
 NE_INT

On entry, ${\mathbf{n}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{n}}\ge 0$.
On entry, ${\mathbf{pda}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{pda}}>0$.
 NE_INT_2

On entry, ${\mathbf{pda}}=\u2329\mathit{\text{value}}\u232a$ and ${\mathbf{n}}=\u2329\mathit{\text{value}}\u232a$.
Constraint: ${\mathbf{pda}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
 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.
 NE_SINGULAR

Element $\u2329\mathit{\text{value}}\u232a$ 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.
7
Accuracy
If
${\mathbf{uplo}}=\mathrm{Nag\_Upper}$, the computed factors
$U$ and
$D$ are the exact factors of a perturbed matrix
$A+E$, where
$c\left(n\right)$ is a modest linear function of
$n$, and
$\epsilon $ is the
machine precision.
If ${\mathbf{uplo}}=\mathrm{Nag\_Lower}$, a similar statement holds for the computed factors $L$ and $D$.
8
Parallelism and Performance
f07mdc 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 implementationspecific information.
The elements of
$D$ overwrite the corresponding elements of
$A$; if
$D$ has
$2$ by
$2$ blocks, only the upper or lower triangle is stored, as specified by
uplo.
The unit diagonal elements of
$U$ or
$L$ and the
$2$ by
$2$ unit diagonal blocks are not stored. The remaining elements of
$U$ or
$L$ are stored in the corresponding columns of the array
a, but additional row interchanges must be applied to recover
$U$ or
$L$ explicitly (this is seldom necessary). If
${\mathbf{ipiv}}\left[\mathit{i}1\right]=\mathit{i}$, for
$\mathit{i}=1,2,\dots ,n$ (as is the case when
$A$ is positive definite), then
$U$ or
$L$ is stored explicitly (except for its unit diagonal elements which are equal to
$1$).
The total number of floatingpoint operations is approximately $\frac{1}{3}{n}^{3}$.
A call to
f07mdc may be followed by calls to the functions:
 f07mec to solve $AX=B$;
 f07mgc to estimate the condition number of $A$;
 f07mjc to compute the inverse of $A$.
The complex analogues of this function are
f07mrc for Hermitian matrices and
f07nrc for symmetric matrices.
10
Example
This example computes the Bunch–Kaufman factorization of the matrix
$A$, where
10.1
Program Text
10.2
Program Data
10.3
Program Results