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NAG Library Function Document

nag_zgeqlf (f08csc)

▸▿ 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

nag_zgeqlf (f08csc) computes a

Q L

factorization of a complex

m

n

matrix

A

2

Specification

#include <nag.h>

#include <nagf08.h>

void	nag_zgeqlf (Nag_OrderType order, Integer m, Integer n, Complex a[], Integer pda, Complex tau[], NagError *fail)

3

Description

nag_zgeqlf (f08csc) forms the

Q L

factorization of an arbitrary rectangular complex

m

n

matrix.

m \geq n

, the factorization is given by:

A = Q (\begin{array}{r} 0 \\ L \end{array}),

where

L

is an

n

n

lower triangular matrix and

Q

is an

m

m

unitary matrix. If

m < n

the factorization is given by

A = Q L,

where

L

is an

m

n

lower trapezoidal matrix and

Q

is again an

m

m

unitary matrix. In the case where

m > n

the factorization can be expressed as

A = (\begin{array}{r} Q_{1} & Q_{2} \end{array}) (\begin{array}{r} 0 \\ L \end{array}) = Q_{2} L,

where

Q_{1}

consists of the first

m - n

columns of

Q

, and

Q_{2}

the remaining

n

columns.

The matrix

Q

is not formed explicitly but is represented as a product of

\min (m, n)

elementary reflectors (see Section 3.3.6 in the f08 Chapter Introduction for details). Functions are provided to work with

Q

in this representation (see Section 9).

Note also that for any

k < n

, the information returned in the last

k

columns of the array a represents a

Q L

factorization of the last

k

columns of the original matrix

A

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

Golub G H and Van Loan C F (1996) Matrix Computations (3rd Edition) Johns Hopkins University Press, Baltimore

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: $m$ – IntegerInput

On entry:

m

, the number of rows of the matrix

A

Constraint:

m \geq 0

3: $n$ – IntegerInput

On entry:

n

, the number of columns of the matrix

A

Constraint:

n \geq 0

4: $a [\dim]$ – ComplexInput/Output

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

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

Where

A (i, j)

appears in this document, it refers to the array element

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

On entry: the

m

n

matrix

A

On exit: if

m \geq n

, the lower triangle of the subarray

A (m - n + 1 : m, 1 : n)

contains the

n

n

lower triangular matrix

L

m \leq n

, the elements on and below the

(n - m)

th superdiagonal contain the

m

n

lower trapezoidal matrix

L

. The remaining elements, with the array tau, represent the unitary matrix

Q

as a product of elementary reflectors (see Section 3.3.6 in the f08 Chapter Introduction).

5: $pda$ – IntegerInput

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

Constraints:

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

6: $tau [\dim]$ – ComplexOutput

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

\max (1, \min (m, n))

On exit: the scalar factors of the elementary reflectors (see Section 9).

7: $fail$ – 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.
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, $m = 〈value〉$ .
Constraint: $m \geq 0$ .

On entry, $n = 〈value〉$ .
Constraint: $n \geq 0$ .

On entry, $pda = 〈value〉$ .
Constraint: $pda > 0$ .
NE_INT_2: On entry, $pda = 〈value〉$ and $m = 〈value〉$ .
Constraint: $pda \geq \max (1, m)$ .

On entry, $pda = 〈value〉$ and $n = 〈value〉$ .
Constraint: $pda \geq \max (1, n)$ .
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.

7

Accuracy

The computed factorization is the exact factorization of a nearby matrix

(A + E)

, where

{‖E‖}_{2} = O (ε) {‖A‖}_{2},

and

ε

is the machine precision.

8

Parallelism and Performance

nag_zgeqlf (f08csc) 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 total number of real floating-point operations is approximately

\frac{8}{3} n^{2} (3 m - n)

m \geq n

\frac{8}{3} m^{2} (3 n - m)

m < n

To form the unitary matrix

Q

nag_zgeqlf (f08csc) may be followed by a call to nag_zungql (f08ctc):

nag_zungql(order,m,m,MIN(m,n),&a,pda,tau,&fail)

but note that the second dimension of the array a must be at least m, which may be larger than was required by nag_zgeqlf (f08csc).

When

m \geq n

, it is often only the first

n

columns of

Q

that are required, and they may be formed by the call:

nag_zungql(order,m,n,n,&a,pda,tau,&fail)

To apply

Q

to an arbitrary complex rectangular matrix

C

, nag_zgeqlf (f08csc) may be followed by a call to nag_zunmql (f08cuc). For example,

nag_zunmql(order,Nag_LeftSide,Nag_ConjTrans,m,p,MIN(m,n),&a,pda,
  tau,&c,pdc,&fail)

forms

C = Q^{H} C

, where

C

m

p

The real analogue of this function is nag_dgeqlf (f08cec).

10

Example

This example solves the linear least squares problems

\min_{x} {‖b_{j} - A x_{j}‖}_{2}, ​ j = 1, 2

for

x_{1}

and

x_{2}

, where

b_{j}

is the

j

th column of the matrix

B

A = (\begin{array}{r} 0.96 - 0.81 i & - 0.03 + 0.96 i & - 0.91 + 2.06 i & - 0.05 + 0.41 i \\ - 0.98 + 1.98 i & - 1.20 + 0.19 i & - 0.66 + 0.42 i & - 0.81 + 0.56 i \\ 0.62 - 0.46 i & 1.01 + 0.02 i & 0.63 - 0.17 i & - 1.11 + 0.60 i \\ - 0.37 + 0.38 i & 0.19 - 0.54 i & - 0.98 - 0.36 i & 0.22 - 0.20 i \\ 0.83 + 0.51 i & 0.20 + 0.01 i & - 0.17 - 0.46 i & 1.47 + 1.59 i \\ 1.08 - 0.28 i & 0.20 - 0.12 i & - 0.07 + 1.23 i & 0.26 + 0.26 i \end{array})

and

B = (\begin{array}{r} - 2.09 + 1.93 i & 3.26 - 2.70 i \\ 3.34 - 3.53 i & - 6.22 + 1.16 i \\ - 4.94 - 2.04 i & 7.94 - 3.13 i \\ 0.17 + 4.23 i & 1.04 - 4.26 i \\ - 5.19 + 3.63 i & - 2.31 - 2.12 i \\ 0.98 + 2.53 i & - 1.39 - 4.05 i \end{array}) .

The solution is obtained by first obtaining a

Q L

factorization of the matrix

A

NAG Library Function Document

nag_zgeqlf (f08csc)

▸▿ 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