NAG Library Function Document

nag_zhpmv (f16sec)

void	nag_zhpmv (Nag_OrderType order, Nag_UploType uplo, Integer n, Complex alpha, const Complex ap[], const Complex x[], Integer incx, Complex beta, Complex y[], Integer incy, NagError *fail)

3 Description

nag_zhpmv (f16sec) performs the matrix-vector operation

y \leftarrow α A x + β y,

where

A

is an

n

n

complex Hermitian matrix stored in packed form,

x

and

y

are

n

-element complex vectors, and

α

and

β

are complex scalars.

4 References

Basic Linear Algebra Subprograms Technical (BLAST) Forum (2001) Basic Linear Algebra Subprograms Technical (BLAST) Forum Standard University of Tennessee, Knoxville, Tennessee http://www.netlib.org/blas/blast-forum/blas-report.pdf

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 3.2.1.3 in the Essential Introduction for a more detailed explanation of the use of this argument.

Constraint:

order = Nag_RowMajor

Nag_ColMajor

2: $uplo$ – Nag_UploTypeInput

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

A

is stored.

$uplo = Nag_Upper$: The upper triangular part of $A$ is stored.
$uplo = Nag_Lower$: The lower triangular part of $A$ is stored.

Constraint:

uplo = Nag_Upper

Nag_Lower

3: $n$ – IntegerInput

On entry:

n

, the order of the matrix

A

Constraint:

n \geq 0

4: $alpha$ – ComplexInput

On entry: the scalar

α

5: $ap [\dim]$ – const ComplexInput

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

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

On entry: the

n

n

Hermitian matrix

A

, packed by rows or columns.

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 $i \geq j$ .

6: $x [\dim]$ – const ComplexInput

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

\max (1, 1 + (n - 1) |incx|)

On entry: the vector

x

7: $incx$ – IntegerInput

On entry: the increment in the subscripts of x between successive elements of

x

Constraint:

incx \neq 0

8: $beta$ – ComplexInput

On entry: the scalar

β

9: $y [\dim]$ – ComplexInput/Output

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

\max (1, 1 + (n - 1) |incy|)

On entry: the vector

y

beta = 0

, y need not be set.

On exit: the updated vector

y

10: $incy$ – IntegerInput

On entry: the increment in the subscripts of y between successive elements of

y

Constraint:

incy \neq 0

11: $fail$ – NagError *Input/Output

The NAG error argument (see Section 3.6 in the Essential Introduction).

6 Error Indicators and Warnings

NE_ALLOC_FAIL: Dynamic memory allocation failed.
See Section 3.2.1.2 in the Essential Introduction for further information.
NE_BAD_PARAM: On entry, argument $〈value〉$ had an illegal value.
NE_INT: On entry, $incx = 〈value〉$ .
Constraint: $incx \neq 0$ .

On entry, $incy = 〈value〉$ .
Constraint: $incy \neq 0$ .

On entry, $n = 〈value〉$ .
Constraint: $n \geq 0$ .
NE_INTERNAL_ERROR: An unexpected error has been triggered by this function. Please contact NAG.
See Section 3.6.6 in the Essential Introduction for further information.
NE_NO_LICENCE: Your licence key may have expired or may not have been installed correctly.
See Section 3.6.5 in the Essential Introduction for further information.

7 Accuracy

The BLAS standard requires accurate implementations which avoid unnecessary over/underflow (see Section 2.7 of Basic Linear Algebra Subprograms Technical (BLAST) Forum (2001)).

8 Parallelism and Performance

Not applicable.

9 Further Comments

None.

10 Example

This example computes the matrix-vector product

y = α A x + β y

where

A = (\begin{array}{r} 1.0 + 0.0 i & 2.0 - 1.0 i & 3.0 - 1.0 i & 4.0 - 1.0 i \\ 2.0 + 1.0 i & 2.0 + 0.0 i & 3.0 - 2.0 i & 4.0 - 2.0 i \\ 3.0 + 1.0 i & 3.0 + 2.0 i & 3.0 + 0.0 i & 4.0 - 3.0 i \\ 4.0 + 1.0 i & 4.0 + 1.0 i & 4.0 + 3.0 i & 4.0 + 0.0 i \end{array}),

x = (\begin{array}{r} - 1.0 + 1.0 i \\ 2.0 - 3.0 i \\ - 3.0 + 2.0 i \\ 1.0 - 1.0 i \end{array}),

y = (\begin{array}{r} 2.5 + 2.5 i \\ 2.5 + 1.5 i \\ 2.5 + 5.0 i \\ 6.0 + 9.0 i \end{array}),

α = 1.0 + 0.0 i and β = 2.0 + 0.0 i .

NAG Library Function Documentnag_zhpmv (f16sec)

▸▿ 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 Library Function Document

nag_zhpmv (f16sec)