F08 Chapter Contents
F08 Chapter Introduction
NAG Library Manual

# NAG Library Routine DocumentF08CSF (ZGEQLF)

Note:  before using this routine, please read the Users' Note for your implementation to check the interpretation of bold italicised terms and other implementation-dependent details.

## 1  Purpose

F08CSF (ZGEQLF) computes a $QL$ factorization of a complex $m$ by $n$ matrix $A$.

## 2  Specification

 SUBROUTINE F08CSF ( M, N, A, LDA, TAU, WORK, LWORK, INFO)
 INTEGER M, N, LDA, LWORK, INFO COMPLEX (KIND=nag_wp) A(LDA,*), TAU(*), WORK(max(1,LWORK))
The routine may be called by its LAPACK name zgeqlf.

## 3  Description

F08CSF (ZGEQLF) forms the $QL$ factorization of an arbitrary rectangular complex $m$ by $n$ matrix.
If $m\ge n$, the factorization is given by:
 $A = Q 0 L ,$
where $L$ is an $n$ by $n$ lower triangular matrix and $Q$ is an $m$ by $m$ unitary matrix. If $m the factorization is given by
 $A = QL ,$
where $L$ is an $m$ by $n$ lower trapezoidal matrix and $Q$ is again an $m$ by $m$ unitary matrix. In the case where $m>n$ the factorization can be expressed as
 $A = Q1 Q2 0 L = Q2 L ,$
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 $\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left(m,n\right)$ elementary reflectors (see Section 3.3.6 in the F08 Chapter Introduction for details). Routines are provided to work with $Q$ in this representation (see Section 8).
Note also that for any $k, the information returned in the last $k$ columns of the array A represents a $QL$ factorization of the last $\mathrm{k}$ 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  Parameters

1:     M – INTEGERInput
On entry: $m$, the number of rows of the matrix $A$.
Constraint: ${\mathbf{M}}\ge 0$.
2:     N – INTEGERInput
On entry: $n$, the number of columns of the matrix $A$.
Constraint: ${\mathbf{N}}\ge 0$.
3:     A(LDA,$*$) – COMPLEX (KIND=nag_wp) arrayInput/Output
Note: the second dimension of the array A must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{N}}\right)$.
On entry: the $m$ by $n$ matrix $A$.
On exit: if $m\ge n$, the lower triangle of the subarray ${\mathbf{A}}\left(m-n+1:m,1:n\right)$ contains the $n$ by $n$ lower triangular matrix $L$.
If $m\le n$, the elements on and below the $\left(n-m\right)$th superdiagonal contain the $m$ by $n$ 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).
4:     LDA – INTEGERInput
On entry: the first dimension of the array A as declared in the (sub)program from which F08CSF (ZGEQLF) is called.
Constraint: ${\mathbf{LDA}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{M}}\right)$.
5:     TAU($*$) – COMPLEX (KIND=nag_wp) arrayOutput
Note: the dimension of the array TAU must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{M}},{\mathbf{N}}\right)\right)$.
On exit: the scalar factors of the elementary reflectors (see Section 8).
6:     WORK($\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{LWORK}}\right)$) – COMPLEX (KIND=nag_wp) arrayWorkspace
On exit: if ${\mathbf{INFO}}={\mathbf{0}}$, the real part of ${\mathbf{WORK}}\left(1\right)$ contains the minimum value of LWORK required for optimal performance.
7:     LWORK – INTEGERInput
On entry: the dimension of the array WORK as declared in the (sub)program from which F08CSF (ZGEQLF) is called.
If ${\mathbf{LWORK}}=-1$, 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, ${\mathbf{LWORK}}\ge {\mathbf{N}}×\mathit{nb}$, where $\mathit{nb}$ is the optimal block size.
Constraint: ${\mathbf{LWORK}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{N}}\right)$.
8:     INFO – INTEGEROutput
On exit: ${\mathbf{INFO}}=0$ unless the routine detects an error (see Section 6).

## 6  Error Indicators and Warnings

Errors or warnings detected by the routine:
${\mathbf{INFO}}<0$
If ${\mathbf{INFO}}=-i$, argument $i$ had an illegal value. An explanatory message is output, and execution of the program is terminated.

## 7  Accuracy

The computed factorization is the exact factorization of a nearby matrix $\left(A+E\right)$, where
 $E2 = Oε A2 ,$
and $\epsilon$ is the machine precision.

The total number of real floating point operations is approximately $\frac{8}{3}{n}^{2}\left(3m-n\right)$ if $m\ge n$ or $\frac{8}{3}{m}^{2}\left(3n-m\right)$ if $m.
To form the unitary matrix $Q$ F08CSF (ZGEQLF) may be followed by a call to F08CTF (ZUNGQL):
```CALL ZUNGQL(M,M,MIN(M,N),A,LDA,TAU,WORK,LWORK,INFO)
```
but note that the second dimension of the array A must be at least M, which may be larger than was required by F08CSF (ZGEQLF).
When $m\ge n$, it is often only the first $n$ columns of $Q$ that are required, and they may be formed by the call:
```CALL ZUNGQL(M,N,N,A,LDA,TAU,WORK,LWORK,INFO)
```
To apply $Q$ to an arbitrary complex rectangular matrix $C$, F08CSF (ZGEQLF) may be followed by a call to F08CUF (ZUNMQL). For example,
```CALL ZUNMQL('Left','Conjugate Transpose',M,P,MIN(M,N),A,LDA,TAU, &
C,LDC,WORK,LWORK,INFO)
```
forms $C={Q}^{\mathrm{H}}C$, where $C$ is $m$ by $p$.
The real analogue of this routine is F08CEF (DGEQLF).

## 9  Example

This example solves the linear least squares problems
 $minx bj - Axj 2 , ​ j=1,2$
for ${x}_{1}$ and ${x}_{2}$, where ${b}_{j}$ is the $j$th column of the matrix $B$,
 $A = 0.96-0.81i -0.03+0.96i -0.91+2.06i -0.05+0.41i -0.98+1.98i -1.20+0.19i -0.66+0.42i -0.81+0.56i 0.62-0.46i 1.01+0.02i 0.63-0.17i -1.11+0.60i -0.37+0.38i 0.19-0.54i -0.98-0.36i 0.22-0.20i 0.83+0.51i 0.20+0.01i -0.17-0.46i 1.47+1.59i 1.08-0.28i 0.20-0.12i -0.07+1.23i 0.26+0.26i$
and
 $B = -2.09+1.93i 3.26-2.70i 3.34-3.53i -6.22+1.16i -4.94-2.04i 7.94-3.13i 0.17+4.23i 1.04-4.26i -5.19+3.63i -2.31-2.12i 0.98+2.53i -1.39-4.05i .$
The solution is obtained by first obtaining a $QL$ factorization of the matrix $A$.
Note that the block size (NB) of $64$ assumed in this example is not realistic for such a small problem, but should be suitable for large problems.

### 9.1  Program Text

Program Text (f08csfe.f90)

### 9.2  Program Data

Program Data (f08csfe.d)

### 9.3  Program Results

Program Results (f08csfe.r)