void	nag_complex_sym_packed_lin_solve (Nag_OrderType order, Nag_UploType uplo, Integer n, Integer nrhs, Complex ap[], Integer ipiv[], Complex b[], Integer pdb, double rcond, double errbnd, NagError *fail)

3

Description

The diagonal pivoting method is used to factor

A

A = U D U^{T}

, if

uplo = Nag_Upper

, or

A = L D L^{T}

, if

uplo = Nag_Lower

, where

U

(or

L

) is a product of permutation and unit upper (lower) triangular matrices, and

D

is symmetric and block diagonal with

1

1

and

2

2

diagonal blocks. The factored form of

A

is then used to solve the system of equations

A X = B

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 http://www.netlib.org/lapack/lug

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

5

Arguments

1: $order$ – Nag_OrderTypeInput

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 3.3.1.3 in How to Use the NAG Library and its Documentation for a more detailed explanation of the use of this argument.

Constraint:

order = Nag_RowMajor

Nag_ColMajor

2: $uplo$ – Nag_UploTypeInput

On entry: if

uplo = Nag_Upper

, the upper triangle of the matrix

A

is stored.

uplo = Nag_Lower

, the lower triangle of the matrix

A

is stored.

Constraint:

uplo = Nag_Upper

Nag_Lower

3: $n$ – IntegerInput

On entry: the number of linear equations

n

, i.e., the order of the matrix

A

Constraint:

n \geq 0

4: $nrhs$ – IntegerInput

On entry: the number of right-hand sides

r

, i.e., the number of columns of the matrix

B

Constraint:

nrhs \geq 0

5: $ap [\dim]$ – ComplexInput/Output

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

\max (1, n \times (n + 1) / 2)

On entry: the

n

n

symmetric matrix

A

, packed column-wise in a linear array. The

j

th column of the matrix

A

is stored in the array ap as follows:

The storage of elements

A_{i j}

depends on the order and uplo arguments as follows:

if $order = Nag_ColMajor$ and $uplo = Nag_Upper$ ,
$A_{i j}$ is stored in $ap [(j - 1) \times j / 2 + i - 1]$ , for $i \leq j$ ;
if $order = Nag_ColMajor$ and $uplo = Nag_Lower$ ,
$A_{i j}$ is stored in $ap [(2 n - j) \times (j - 1) / 2 + i - 1]$ , for $i \geq j$ ;
if $order = Nag_RowMajor$ and $uplo = Nag_Upper$ ,
$A_{i j}$ is stored in $ap [(2 n - i) \times (i - 1) / 2 + j - 1]$ , for $i \leq j$ ;
if $order = Nag_RowMajor$ and $uplo = Nag_Lower$ ,
$A_{i j}$ is stored in $ap [(i - 1) \times i / 2 + j - 1]$ , for $i \geq j$ .

On exit: if

fail . code =

NE_NOERROR, the block diagonal matrix

D

and the multipliers used to obtain the factor

U

L

from the factorization

A = U D U^{T}

A = L D L^{T}

as computed by nag_zsptrf (f07qrc), stored as a packed triangular matrix in the same storage format as

A

6: $ipiv [n]$ – IntegerOutput

On exit: if no constraints are violated, details of the interchanges and the block structure of

D

, as determined by nag_zsptrf (f07qrc).

If $ipiv [k - 1] > 0$ , then rows and columns $k$ and $ipiv [k - 1]$ were interchanged, and $d_{k k}$ is a $1$ by $1$ diagonal block;
if $uplo = Nag_Upper$ and $ipiv [k - 1] = ipiv [k - 2] < 0$ , then rows and columns $k - 1$ and $- ipiv [k - 1]$ were interchanged and $d_{k - 1 : k, k - 1 : k}$ is a $2$ by $2$ diagonal block;
if $uplo = Nag_Lower$ and $ipiv [k - 1] = ipiv [k] < 0$ , then rows and columns $k + 1$ and $- ipiv [k - 1]$ were interchanged and $d_{k : k + 1, k : k + 1}$ is a $2$ by $2$ diagonal block.

7: $b [\dim]$ – ComplexInput/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

r

matrix of right-hand sides

B

On exit: if

fail . code =

NE_NOERROR or NE_RCOND, the

n

r

solution matrix

X

8: $pdb$ – IntegerInput

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 $order = Nag_RowMajor$ , $pdb \geq \max (1, nrhs)$ .

9: $rcond$ – double *Output

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

A

, computed as

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

10: $errbnd$ – double *Output

On exit: if

fail . code =

NE_NOERROR or NE_RCOND, an estimate of the forward error bound for a computed solution

\hat{x}

, such that

{‖\hat{x} - x‖}_{1} / {‖x‖}_{1} \leq errbnd

, where

\hat{x}

is a column of the computed solution returned in the array b and

x

is the corresponding column of the exact solution

X

. If rcond is less than machine precision, errbnd is returned as unity.

11: $fail$ – NagError *Input/Output

The NAG error argument (see Section 3.7 in How to Use the NAG Library and its Documentation).

6

Error Indicators and Warnings

NE_ALLOC_FAIL: Dynamic memory allocation failed.
The double allocatable memory required is n, and the Complex allocatable memory required is $2 \times n$ . Allocation failed before the solution could be computed.
See Section 2.3.1.2 in How to Use the NAG Library and its Documentation 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, $pdb = 〈value〉$ .
Constraint: $pdb > 0$ .
NE_INT_2: On entry, $pdb = 〈value〉$ and $n = 〈value〉$ .
Constraint: $pdb \geq \max (1, n)$ .

On entry, $pdb = 〈value〉$ and $nrhs = 〈value〉$ .
Constraint: $pdb \geq \max (1, nrhs)$ .
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 2.7.6 in How to Use the NAG Library and its Documentation for further information.
NE_NO_LICENCE: Your licence key may have expired or may not have been installed correctly.
See Section 2.7.5 in How to Use the NAG Library and its Documentation for further information.
NE_RCOND: A solution has been computed, but rcond is less than machine precision so that the matrix $A$ is numerically singular.
NE_SINGULAR: Diagonal block $〈value〉$ of the block diagonal matrix is zero. The factorization has been completed, but the solution could not be computed.

7

Accuracy

The computed solution for a single right-hand side,

\hat{x}

, satisfies an equation of the form

(A + E) \hat{x} = b,

where

{‖E‖}_{1} = O (ε) {‖A‖}_{1}

and

ε

is the machine precision. An approximate error bound for the computed solution is given by

\frac{{‖\hat{x} - x‖}_{1}}{{‖x‖}_{1}} \leq κ (A) \frac{{‖E‖}_{1}}{{‖A‖}_{1}},

where

κ (A) = {‖A^{- 1}‖}_{1} {‖A‖}_{1}

, the condition number of

A

with respect to the solution of the linear equations. nag_complex_sym_packed_lin_solve (f04djc) uses the approximation

{‖E‖}_{1} = ε {‖A‖}_{1}

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

8

Parallelism and Performance

nag_complex_sym_packed_lin_solve (f04djc) 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 packed storage scheme is illustrated by the following example when

n = 4

and

uplo = Nag_Upper

. Two-dimensional storage of the symmetric matrix

A

\begin{matrix} a_{11} & a_{12} & a_{13} & a_{14} \\ a_{22} & a_{23} & a_{24} \\ a_{33} & a_{34} \\ a_{44} \end{matrix} (a_{i j} = a_{j i})

Packed storage of the upper triangle of

A

ap = [\begin{matrix} a_{11}, & a_{12}, & a_{22}, & a_{13}, & a_{23}, & a_{33}, & a_{14}, & a_{24}, & a_{34}, & a_{44} \end{matrix}]

The total number of floating-point operations required to solve the equations

A X = B

is proportional to

(\frac{1}{3} n^{3} + 2 n^{2} r)

. The condition number estimation typically requires between four and five solves and never more than eleven solves, following the factorization.

In practice the condition number estimator is very reliable, but it can underestimate the true condition number; see Section 15.3 of Higham (2002) for further details.

Function nag_herm_packed_lin_solve (f04cjc) is for complex Hermitian matrices, and the real analogue of nag_complex_sym_packed_lin_solve (f04djc) is nag_real_sym_packed_lin_solve (f04bjc).

10

Example

This example solves the equations

A X = B,

where

A

is the symmetric indefinite matrix

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

and

B = (\begin{array}{r} - 6.43 + 19.24 i & - 4.59 - 35.53 i \\ - 0.49 - 1.47 i & 6.95 + 20.49 i \\ - 48.18 + 66.00 i & - 12.08 - 27.02 i \\ - 55.64 + 41.22 i & - 19.09 - 35.97 i \end{array}) .

An estimate of the condition number of

A

and an approximate error bound for the computed solutions are also printed.

NAG Library Function Document

nag_complex_sym_packed_lin_solve (f04djc)

▸▿ 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

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