# NAG FL Interfacef08zsf (zggqrf)

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## 1Purpose

f08zsf computes a generalized $QR$ factorization of a complex matrix pair $\left(A,B\right)$, where $A$ is an $n×m$ matrix and $B$ is an $n×p$ matrix.

## 2Specification

Fortran Interface
 Subroutine f08zsf ( n, m, p, a, lda, taua, b, ldb, taub, work, info)
 Integer, Intent (In) :: n, m, p, lda, ldb, lwork Integer, Intent (Out) :: info Complex (Kind=nag_wp), Intent (Inout) :: a(lda,*), b(ldb,*) Complex (Kind=nag_wp), Intent (Out) :: taua(min(n,m)), taub(min(n,p)), work(max(1,lwork))
#include <nag.h>
 void f08zsf_ (const Integer *n, const Integer *m, const Integer *p, Complex a[], const Integer *lda, Complex taua[], Complex b[], const Integer *ldb, Complex taub[], Complex work[], const Integer *lwork, Integer *info)
The routine may be called by the names f08zsf, nagf_lapackeig_zggqrf or its LAPACK name zggqrf.

## 3Description

f08zsf forms the generalized $QR$ factorization of an $n×m$ matrix $A$ and an $n×p$ matrix $B$
 $A =QR , B=QTZ ,$
where $Q$ is an $n×n$ unitary matrix, $Z$ is a $p×p$ unitary matrix and $R$ and $T$ are of the form
 $R = { mmR11n-m0() , if ​n≥m; nm-nnR11R12() , if ​n
with ${R}_{11}$ upper triangular,
 $T = { p-nnn0T12() , if ​n≤p, pn-pT11pT21() , if ​n>p,$
with ${T}_{12}$ or ${T}_{21}$ upper triangular.
In particular, if $B$ is square and nonsingular, the generalized $QR$ factorization of $A$ and $B$ implicitly gives the $QR$ factorization of ${B}^{-1}A$ as
 $B-1A= ZH (T-1R) .$
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 https://www.netlib.org/lapack/lug
Anderson E, Bai Z and Dongarra J (1992) Generalized $QR$ factorization and its applications Linear Algebra Appl. (Volume 162–164) 243–271
Hammarling S (1987) The numerical solution of the general Gauss-Markov linear model Mathematics in Signal Processing (eds T S Durrani, J B Abbiss, J E Hudson, R N Madan, J G McWhirter and T A Moore) 441–456 Oxford University Press
Paige C C (1990) Some aspects of generalized $QR$ factorizations . In Reliable Numerical Computation (eds M G Cox and S Hammarling) 73–91 Oxford University Press

## 5Arguments

1: $\mathbf{n}$Integer Input
On entry: $n$, the number of rows of the matrices $A$ and $B$.
Constraint: ${\mathbf{n}}\ge 0$.
2: $\mathbf{m}$Integer Input
On entry: $m$, the number of columns of the matrix $A$.
Constraint: ${\mathbf{m}}\ge 0$.
3: $\mathbf{p}$Integer Input
On entry: $p$, the number of columns of the matrix $B$.
Constraint: ${\mathbf{p}}\ge 0$.
4: $\mathbf{a}\left({\mathbf{lda}},*\right)$Complex (Kind=nag_wp) array Input/Output
Note: the second dimension of the array a must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{m}}\right)$.
On entry: the $n×m$ matrix $A$.
On exit: the elements on and above the diagonal of the array contain the $\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left(n,m\right)×m$ upper trapezoidal matrix $R$ ($R$ is upper triangular if $n\ge m$); the elements below the diagonal, with the array taua, represent the unitary matrix $Q$ as a product of $\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left(n,m\right)$ elementary reflectors (see Section 3.3.6 in the F08 Chapter Introduction).
5: $\mathbf{lda}$Integer Input
On entry: the first dimension of the array a as declared in the (sub)program from which f08zsf is called.
Constraint: ${\mathbf{lda}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
6: $\mathbf{taua}\left(\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{n}},{\mathbf{m}}\right)\right)$Complex (Kind=nag_wp) array Output
On exit: the scalar factors of the elementary reflectors which represent the unitary matrix $Q$.
7: $\mathbf{b}\left({\mathbf{ldb}},*\right)$Complex (Kind=nag_wp) array Input/Output
Note: the second dimension of the array b must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{p}}\right)$.
On entry: the $n×p$ matrix $B$.
On exit: if $n\le p$, the upper triangle of the subarray ${\mathbf{b}}\left(1:n,p-n+1:p\right)$ contains the $n×n$ upper triangular matrix ${T}_{12}$.
If $n>p$, the elements on and above the $\left(n-p\right)$th subdiagonal contain the $n×p$ upper trapezoidal matrix $T$; the remaining elements, with the array taub, represent the unitary matrix $Z$ as a product of elementary reflectors (see Section 3.3.6 in the F08 Chapter Introduction).
8: $\mathbf{ldb}$Integer Input
On entry: the first dimension of the array b as declared in the (sub)program from which f08zsf is called.
Constraint: ${\mathbf{ldb}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
9: $\mathbf{taub}\left(\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{n}},{\mathbf{p}}\right)\right)$Complex (Kind=nag_wp) array Output
On exit: the scalar factors of the elementary reflectors which represent the unitary matrix $Z$.
10: $\mathbf{work}\left(\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{lwork}}\right)\right)$Complex (Kind=nag_wp) array Workspace
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.
11: $\mathbf{lwork}$Integer Input
On entry: the dimension of the array work as declared in the (sub)program from which f08zsf 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, ${\mathbf{lwork}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{n}},{\mathbf{m}},{\mathbf{p}}\right)×\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(\mathit{nb1},\mathit{nb2},\mathit{nb3}\right)$, where $\mathit{nb1}$ is the optimal block size for the $QR$ factorization of an $n×m$ matrix, $\mathit{nb2}$ is the optimal block size for the $RQ$ factorization of an $n×p$ matrix, and $\mathit{nb3}$ is the optimal block size for a call of f08auf.
Constraint: ${\mathbf{lwork}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}},{\mathbf{m}},{\mathbf{p}}\right)$ or ${\mathbf{lwork}}=-1$.
12: $\mathbf{info}$Integer Output
On exit: ${\mathbf{info}}=0$ unless the routine detects an error (see Section 6).

## 6Error Indicators and Warnings

${\mathbf{info}}<0$
If ${\mathbf{info}}=-i$, argument $i$ had an illegal value. An explanatory message is output, and execution of the program is terminated.

## 7Accuracy

The computed generalized $QR$ factorization is the exact factorization for nearby matrices $\left(A+E\right)$ and $\left(B+F\right)$, where
 $‖E‖2 = O⁡ε ‖A‖2 and ‖F‖2= O⁡ε ‖B‖2 ,$
and $\epsilon$ is the machine precision.

## 8Parallelism and Performance

f08zsf is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
f08zsf 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 routine. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

The unitary matrices $Q$ and $Z$ may be formed explicitly by calls to f08atf and f08cwf respectively. f08auf may be used to multiply $Q$ by another matrix and f08cxf may be used to multiply $Z$ by another matrix.
The real analogue of this routine is f08zef.

## 10Example

This example solves the general Gauss–Markov linear model problem
 $minx ‖y‖2 subject to d=Ax+By$
where
 $A = ( 0.96-0.81i -0.03+0.96i -0.91+2.06i -0.98+1.98i -1.20+0.19i -0.66+0.42i 0.62-0.46i 1.01+0.02i 0.63-0.17i 1.08-0.28i 0.20-0.12i -0.07+1.23i ) ,$
 $B = ( 0.5-1.0i 0.0i+0.0 0.0i+0.0 0.0i+0.0 0.0i+0.0 1.0-2.0i 0.0i+0.0 0.0i+0.0 0.0i+0.0 0.0i+0.0 2.0-3.0i 0.0i+0.0 0.0i+0.0 0.0i+0.0 0.0i+0.0 5.0-4.0i )$
and
 $d = ( 6.00-0.40i -5.27+0.90i 2.72-2.13i -1.30-2.80i ) .$
The solution is obtained by first computing a generalized $QR$ factorization of the matrix pair $\left(A,B\right)$. The example illustrates the general solution process, although the above data corresponds to a simple weighted least squares problem.
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.

### 10.1Program Text

Program Text (f08zsfe.f90)

### 10.2Program Data

Program Data (f08zsfe.d)

### 10.3Program Results

Program Results (f08zsfe.r)