f07fpc (Nag_OrderType order, Nag_FactoredFormType fact, Nag_UploType uplo, Integer n, Integer nrhs, Complex a[], Integer pda, Complex af[], Integer pdaf, Nag_EquilibrationType *equed, double s[], Complex b[], Integer pdb, Complex x[], Integer pdx, double *rcond, double ferr[], double berr[], NagError *fail)

The function may be called by the names: f07fpc, nag_lapacklin_zposvx or nag_zposvx.

3 Description

f07fpc performs the following steps:

1.If $fact = Nag_EquilibrateAndFactor$ , real diagonal scaling factors, $D_{S}$ , are computed to equilibrate the system:

$(D_{S} A D_{S}) (D_{S}^{- 1} X) = D_{S} B .$

Whether or not the system will be equilibrated depends on the scaling of the matrix $A$ , but if equilibration is used, $A$ is overwritten by $D_{S} A D_{S}$ and $B \times D_{S} B$ .
2.If $fact = Nag_NotFactored$ or $Nag_EquilibrateAndFactor$ , the Cholesky decomposition is used to factor the matrix $A$ (after equilibration if $fact = Nag_EquilibrateAndFactor$ ) as $A = U^{H} U$ if $uplo = Nag_Upper$ or $A = L L^{H}$ if $uplo = Nag_Lower$ , where $U$ is an upper triangular matrix and $L$ is a lower triangular matrix.
3.If the leading $i \times i$ principal minor of $A$ is not positive definite, then the function returns with $fail . errnum = i$ and $fail . code =$ NE_MAT_NOT_POS_DEF. Otherwise, the factored form of $A$ is used to estimate the condition number of the matrix $A$ . If the reciprocal of the condition number is less than machine precision, $fail . code =$ NE_SINGULAR_WP is returned as a warning, but the function still goes on to solve for $X$ and compute error bounds as described below.
4.The system of equations is solved for $X$ using the factored form of $A$ .
5.Iterative refinement is applied to improve the computed solution matrix and to calculate error bounds and backward error estimates for it.
6.If equilibration was used, the matrix $X$ is premultiplied by $D_{S}$ so that it solves the original system before equilibration.

4 References

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

Higham N J (2002) Accuracy and Stability of Numerical Algorithms (2nd Edition) SIAM, Philadelphia

5 Arguments

1: $order$ – 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

order = 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:

order = Nag_RowMajor

Nag_ColMajor

2: $fact$ – Nag_FactoredFormType Input

On entry: specifies whether or not the factorized form of the matrix

A

is supplied on entry, and if not, whether the matrix

A

should be equilibrated before it is factorized.

$fact = Nag_Factored$: af contains the factorized form of $A$ . If $equed = Nag_Equilibrated$ , the matrix $A$ has been equilibrated with scaling factors given by s. a and af will not be modified.
$fact = Nag_NotFactored$: The matrix $A$ will be copied to af and factorized.
$fact = Nag_EquilibrateAndFactor$: The matrix $A$ will be equilibrated if necessary, then copied to af and factorized.

Constraint:

fact = Nag_Factored

Nag_NotFactored

Nag_EquilibrateAndFactor

3: $uplo$ – Nag_UploType Input

On entry: if

uplo = Nag_Upper

, the upper triangle of

A

is stored.

uplo = Nag_Lower

, the lower triangle of

A

is stored.

Constraint:

uplo = Nag_Upper

Nag_Lower

4: $n$ – Integer Input

On entry:

n

, the number of linear equations, i.e., the order of the matrix

A

Constraint:

n \geq 0

5: $nrhs$ – Integer Input

On entry:

r

, the number of right-hand sides, i.e., the number of columns of the matrix

B

Constraint:

nrhs \geq 0

6: $a [\dim]$ – Complex Input/Output

Note: the dimension, dim, of the array a must be at least

\max (1, pda \times n)

On entry: the

n \times n

Hermitian matrix

A

fact = Nag_Factored

and

equed = Nag_Equilibrated

, a must have been equilibrated by the scaling factor in s as

D_{S} A D_{S}

order = Nag_ColMajor

A_{i j}

is stored in

a [(j - 1) \times pda + i - 1]

order = Nag_RowMajor

A_{i j}

is stored in

a [(i - 1) \times pda + j - 1]

uplo = Nag_Upper

, the upper triangular part of

A

must be stored and the elements of the array below the diagonal are not referenced.

uplo = 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: if

fact = Nag_Factored

Nag_NotFactored

, or if

fact = Nag_EquilibrateAndFactor

and

equed = Nag_NoEquilibration

, a is not modified.

fact = Nag_EquilibrateAndFactor

and

equed = Nag_Equilibrated

, a is overwritten by

D_{S} A D_{S}

7: $pda$ – 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:

pda \geq \max (1, n)

8: $af [\dim]$ – Complex Input/Output

Note: the dimension, dim, of the array af must be at least

\max (1, pdaf \times n)

The

(i, j)

th element of the matrix is stored in

$af [(j - 1) \times pdaf + i - 1]$ when $order = Nag_ColMajor$ ;
$af [(i - 1) \times pdaf + j - 1]$ when $order = Nag_RowMajor$ .

On entry: if

fact = Nag_Factored

, af contains the triangular factor

U

L

from the Cholesky factorization

A = U^{H} U

A = L L^{H}

, in the same storage format as a. If

equed \neq Nag_NoEquilibration

, af is the factorized form of the equilibrated matrix

D_{S} A D_{S}

On exit: if

fact = Nag_NotFactored

, af returns the triangular factor

U

L

from the Cholesky factorization

A = U^{H} U

A = L L^{H}

of the original matrix

A

fact = Nag_EquilibrateAndFactor

, af returns the triangular factor

U

L

from the Cholesky factorization

A = U^{H} U

A = L L^{H}

of the equilibrated matrix

A

(see the description of a for the form of the equilibrated matrix).

9: $pdaf$ – Integer Input

On entry: the stride separating row or column elements (depending on the value of order) of the matrix

A

in the array af.

Constraint:

pdaf \geq \max (1, n)

10: $equed$ – Nag_EquilibrationType * Input/Output

On entry: if

fact = Nag_NotFactored

Nag_EquilibrateAndFactor

, equed need not be set.

fact = Nag_Factored

, equed must specify the form of the equilibration that was performed as follows:

if $equed = Nag_NoEquilibration$ , no equilibration;
if $equed = Nag_Equilibrated$ , equilibration was performed, i.e., $A$ has been replaced by $D_{S} A D_{S}$ .

On exit: if

fact = Nag_Factored

, equed is unchanged from entry.

Otherwise, if no constraints are violated, equed specifies the form of the equilibration that was performed as specified above.

Constraint: if

fact = Nag_Factored

equed = Nag_NoEquilibration

Nag_Equilibrated

11: $s [\dim]$ – double Input/Output

Note: the dimension, dim, of the array s must be at least

\max (1, n)

On entry: if

fact = Nag_NotFactored

Nag_EquilibrateAndFactor

, s need not be set.

fact = Nag_Factored

and

equed = Nag_Equilibrated

, s must contain the scale factors,

D_{S}

, for

A

; each element of s must be positive.

On exit: if

fact = Nag_Factored

, s is unchanged from entry.

Otherwise, if no constraints are violated and

equed = Nag_Equilibrated

, s contains the scale factors,

D_{S}

, for

A

; each element of s is positive.

12: $b [\dim]$ – Complex Input/Output

Note: the dimension, dim, of the array b must be at least

$\max (1, pdb \times nrhs)$ when $order = Nag_ColMajor$ ;
$\max (1, n \times pdb)$ when $order = Nag_RowMajor$ .

The

(i, j)

th element of the matrix

B

is stored in

$b [(j - 1) \times pdb + i - 1]$ when $order = Nag_ColMajor$ ;
$b [(i - 1) \times pdb + j - 1]$ when $order = Nag_RowMajor$ .

On entry: the

n \times r

right-hand side matrix

B

On exit: if

equed = Nag_NoEquilibration

, b is not modified.

equed = Nag_Equilibrated

, b is overwritten by

D_{S} B

13: $pdb$ – Integer Input

On entry: the stride separating row or column elements (depending on the value of order) in the array b.

Constraints:

if $order = Nag_ColMajor$ , $pdb \geq \max (1, n)$ ;
if $order = Nag_RowMajor$ , $pdb \geq \max (1, nrhs)$ .

14: $x [\dim]$ – Complex Output

Note: the dimension, dim, of the array x must be at least

$\max (1, pdx \times nrhs)$ when $order = Nag_ColMajor$ ;
$\max (1, n \times pdx)$ when $order = Nag_RowMajor$ .

The

(i, j)

th element of the matrix

X

is stored in

$x [(j - 1) \times pdx + i - 1]$ when $order = Nag_ColMajor$ ;
$x [(i - 1) \times pdx + j - 1]$ when $order = Nag_RowMajor$ .

On exit: if

fail . code =

NE_NOERROR or NE_SINGULAR_WP, the

n \times r

solution matrix

X

to the original system of equations. Note that the arrays

A

and

B

are modified on exit if

equed = Nag_Equilibrated

, and the solution to the equilibrated system is

D_{S}^{- 1} X

15: $pdx$ – Integer Input

On entry: the stride separating row or column elements (depending on the value of order) in the array x.

Constraints:

if $order = Nag_ColMajor$ , $pdx \geq \max (1, n)$ ;
if $order = Nag_RowMajor$ , $pdx \geq \max (1, nrhs)$ .

16: $rcond$ – double * Output

On exit: if no constraints are violated, an estimate of the reciprocal condition number of the matrix

A

(after equilibration if that is performed), computed as

rcond = 1.0 / ({‖ A ‖}_{1} {‖ A^{- 1} ‖}_{1})

17: $ferr [nrhs]$ – double Output

On exit: if

fail . code =

NE_NOERROR or NE_SINGULAR_WP, an estimate of the forward error bound for each computed solution vector, such that

{‖ {\hat{x}}_{j} - x_{j} ‖}_{\infty} / {‖ x_{j} ‖}_{\infty} \leq ferr [j - 1]

where

{\hat{x}}_{j}

is the

j

th column of the computed solution returned in the array x and

x_{j}

is the corresponding column of the exact solution

X

. The estimate is as reliable as the estimate for rcond, and is almost always a slight overestimate of the true error.

18: $berr [nrhs]$ – double Output

On exit: if

fail . code =

NE_NOERROR or NE_SINGULAR_WP, an estimate of the component-wise relative backward error of each computed solution vector

{\hat{x}}_{j}

(i.e., the smallest relative change in any element of

A

B

that makes

{\hat{x}}_{j}

an exact solution).

19: $fail$ – 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 $⟨ value ⟩$ had an illegal value.
NE_INT: On entry, $n = ⟨ value ⟩$ .
Constraint: $n \geq 0$ .

On entry, $nrhs = ⟨ value ⟩$ .
Constraint: $nrhs \geq 0$ .

On entry, $pda = ⟨ value ⟩$ .
Constraint: $pda > 0$ .

On entry, $pdaf = ⟨ value ⟩$ .
Constraint: $pdaf > 0$ .

On entry, $pdb = ⟨ value ⟩$ .
Constraint: $pdb > 0$ .

On entry, $pdx = ⟨ value ⟩$ .
Constraint: $pdx > 0$ .
NE_INT_2: On entry, $pda = ⟨ value ⟩$ and $n = ⟨ value ⟩$ .
Constraint: $pda \geq \max (1, n)$ .

On entry, $pdaf = ⟨ value ⟩$ and $n = ⟨ value ⟩$ .
Constraint: $pdaf \geq \max (1, n)$ .

On entry, $pdb = ⟨ value ⟩$ and $n = ⟨ value ⟩$ .
Constraint: $pdb \geq \max (1, n)$ .

On entry, $pdb = ⟨ value ⟩$ and $nrhs = ⟨ value ⟩$ .
Constraint: $pdb \geq \max (1, nrhs)$ .

On entry, $pdx = ⟨ value ⟩$ and $n = ⟨ value ⟩$ .
Constraint: $pdx \geq \max (1, n)$ .

On entry, $pdx = ⟨ value ⟩$ and $nrhs = ⟨ value ⟩$ .
Constraint: $pdx \geq \max (1, nrhs)$ .
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_MAT_NOT_POS_DEF: The leading minor of order $⟨ value ⟩$ of $A$ is not positive definite, so the factorization could not be completed, and the solution has not been computed. $rcond = 0.0$ is returned.
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_WP: $U$ (or $L$ ) is nonsingular, but rcond is less than machine precision, meaning that the matrix is singular to working precision. Nevertheless, the solution and error bounds are computed because there are a number of situations where the computed solution can be more accurate than the value of rcond would suggest.

7 Accuracy

For each right-hand side vector

b

, the computed solution

x

is the exact solution of a perturbed system of equations

(A + E) x = b

, where

if $uplo = Nag_Upper$ , $| E | \leq c (n) ε | U^{H} | | U |$ ;
if $uplo = Nag_Lower$ , $| E | \leq c (n) ε | L | | L^{H} |$ ,

c (n)

is a modest linear function of

n

, and

ε

is the machine precision. See Section 10.1 of Higham (2002) for further details.

\hat{x}

is the true solution, then the computed solution

x

satisfies a forward error bound of the form

\frac{{‖ x - \hat{x} ‖}_{\infty}}{{‖ \hat{x} ‖}_{\infty}} \leq w_{c} cond (A, \hat{x}, b)

where

cond (A, \hat{x}, b) = {‖ | A^{- 1} | (| A | | \hat{x} | + | b |) ‖}_{\infty} / {‖ \hat{x} ‖}_{\infty} \leq cond (A) = {‖ | A^{- 1} | | A | ‖}_{\infty} \leq κ_{\infty} (A)

. If

\hat{x}

is the

j

th column of

X

, then

w_{c}

is returned in

berr [j - 1]

and a bound on

{‖ x - \hat{x} ‖}_{\infty} / {‖ \hat{x} ‖}_{\infty}

is returned in

ferr [j - 1]

. See Section 4.4 of Anderson et al. (1999) for further details.

8 Parallelism and Performance

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

f07fpc is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

f07fpc 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.

9 Further Comments

The factorization of

A

requires approximately

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

floating-point operations.

For each right-hand side, computation of the backward error involves a minimum of

16 n^{2}

floating-point operations. Each step of iterative refinement involves an additional

24 n^{2}

operations. At most five steps of iterative refinement are performed, but usually only one or two steps are required. Estimating the forward error involves solving a number of systems of equations of the form

A x = b

; the number is usually

4

5

and never more than

11

. Each solution involves approximately

8 n^{2}

operations.

The real analogue of this function is f07fbc.

10 Example

This example solves the equations

A X = B,

where

A

is the Hermitian positive definite matrix

A = (\begin{array}{r} 3.23 & 1.51 - 1.92 i & 1.90 + 0.84 i & 0.42 + 2.50 i \\ 1.51 + 1.92 i & 3.58 & - 0.23 + 1.11 i & - 1.18 + 1.37 i \\ 1.90 - 0.84 i & - 0.23 - 1.11 i & 4.09 & 2.33 - 0.14 i \\ 0.42 - 2.50 i & - 1.18 - 1.37 i & 2.33 + 0.14 i & 4.29 \end{array})

and

B = (\begin{array}{r} 3.93 - 6.14 i & 1.48 + 6.58 i \\ 6.17 + 9.42 i & 4.64 - 4.75 i \\ - 7.17 - 21.83 i & - 4.91 + 2.29 i \\ 1.99 - 14.38 i & 7.64 - 10.79 i \end{array}) .

Error estimates for the solutions, information on equilibration and an estimate of the reciprocal of the condition number of the scaled matrix

A

are also output.

f07fp: FL CL CPP AD PY MB

NAG CL Interfacef07fpc (zposvx)

▸▿ 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 CL Interface
f07fpc (zposvx)