NAG Library Routine Document

F08AWF (ZUNGLQ)

+− Contents

1 Purpose

2 Specification

3 Description

4 References

5 Parameters

6 Error Indicators and Warnings

7 Accuracy

8 Further Comments

+− 9 Example

9.1 Program Text

9.2 Program Data

9.3 Program Results

1 Purpose

F08AWF (ZUNGLQ) generates all or part of the complex unitary matrix

Q

from an

L Q

factorization computed by F08AVF (ZGELQF).

2 Specification

SUBROUTINE F08AWF (

M, N, K, A, LDA, TAU, WORK, LWORK, INFO)

INTEGER	M, N, K, LDA, LWORK, INFO
COMPLEX (KIND=nag_wp)	A(LDA,), TAU(), WORK(max(1,LWORK))

The routine may be called by its LAPACK name zunglq.

3 Description

F08AWF (ZUNGLQ) is intended to be used after a call to F08AVF (ZGELQF), which performs an

L Q

factorization of a complex matrix

A

. The unitary matrix

Q

is represented as a product of elementary reflectors.

This routine may be used to generate

Q

explicitly as a square matrix, or to form only its leading rows.

Usually

Q

is determined from the

L Q

factorization of a

p

n

matrix

A

with

p \leq n

. The whole of

Q

may be computed by:

CALL ZUNGLQ(N,N,P,A,LDA,TAU,WORK,LWORK,INFO)

(note that the array A must have at least

n

rows) or its leading

p

rows by:

CALL ZUNGLQ(P,N,P,A,LDA,TAU,WORK,LWORK,INFO)

The rows of

Q

returned by the last call form an orthonormal basis for the space spanned by the rows of

A

; thus F08AVF (ZGELQF) followed by F08AWF (ZUNGLQ) can be used to orthogonalize the rows of

A

The information returned by the

L Q

factorization routines also yields the

L Q

factorization of the leading

k

rows of

A

, where

k < p

. The unitary matrix arising from this factorization can be computed by:

CALL ZUNGLQ(N,N,K,A,LDA,TAU,WORK,LWORK,INFO)

or its leading

k

rows by:

CALL ZUNGLQ(K,N,K,A,LDA,TAU,WORK,LWORK,INFO)

4 References

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

5 Parameters

1: M – INTEGERInput: On entry: $m$ , the number of rows of the matrix $Q$ .
Constraint: $M \geq 0$ .
2: N – INTEGERInput: On entry: $n$ , the number of columns of the matrix $Q$ .
Constraint: $N \geq M$ .
3: K – INTEGERInput: On entry: $k$ , the number of elementary reflectors whose product defines the matrix $Q$ .
Constraint: $M \geq K \geq 0$ .
4: A(LDA, $*$ ) – COMPLEX (KIND=nag_wp) arrayInput/Output: Note: the second dimension of the array A must be at least $\max (1, N)$ .
On entry: details of the vectors which define the elementary reflectors, as returned by F08AVF (ZGELQF).

On exit: the $m$ by $n$ matrix $Q$ .
5: LDA – INTEGERInput: On entry: the first dimension of the array A as declared in the (sub)program from which F08AWF (ZUNGLQ) is called.
Constraint: $LDA \geq \max (1, M)$ .
6: TAU( $*$ ) – COMPLEX (KIND=nag_wp) arrayInput: Note: the dimension of the array TAU must be at least $\max (1, K)$ .
On entry: further details of the elementary reflectors, as returned by F08AVF (ZGELQF).
7: WORK( $\max (1, LWORK)$ ) – COMPLEX (KIND=nag_wp) arrayWorkspace: On exit: if $INFO = 0$ , the real part of $WORK (1)$ contains the minimum value of LWORK required for optimal performance.
8: LWORK – INTEGERInput: On entry: the dimension of the array WORK as declared in the (sub)program from which F08AWF (ZUNGLQ) is called.
If $LWORK = - 1$ , a workspace query is assumed; the routine only calculates the optimal size of the WORK array, returns this value as the first entry of the WORK array, and no error message related to LWORK is issued.

Suggested value: for optimal performance, $LWORK \geq M \times nb$ , where $nb$ is the optimal block size.
Constraint: $LWORK \geq \max (1, M)$ or $LWORK = - 1$ .
9: INFO – INTEGEROutput: On exit: $INFO = 0$ unless the routine detects an error (see Section 6).

6 Error Indicators and Warnings

Errors or warnings detected by the routine:

$INFO < 0$: If $INFO = - i$ , argument $i$ had an illegal value. An explanatory message is output, and execution of the program is terminated.

7 Accuracy

The computed matrix

Q

differs from an exactly unitary matrix by a matrix

E

such that

{‖E‖}_{2} = O (ε),

where

ε

is the machine precision.

8 Further Comments

The total number of real floating point operations is approximately

16 m n k - 8 (m + n) k^{2} + \frac{16}{3} k^{3}

; when

m = k

, the number is approximately

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

The real analogue of this routine is F08AJF (DORGLQ).

9 Example

This example forms the leading

4

rows of the unitary matrix

Q

from the

L Q

factorization of the matrix

A