# NAG CL Interfacef08yuc (ztgsen)

## 1Purpose

f08yuc reorders the generalized Schur factorization of a complex matrix pair in generalized Schur form, so that a selected cluster of eigenvalues appears in the leading elements on the diagonal of the generalized Schur form. The function also, optionally, computes the reciprocal condition numbers of the cluster of eigenvalues and/or corresponding deflating subspaces.

## 2Specification

 #include
 void f08yuc (Nag_OrderType order, Integer ijob, Nag_Boolean wantq, Nag_Boolean wantz, const Nag_Boolean select[], Integer n, Complex a[], Integer pda, Complex b[], Integer pdb, Complex alpha[], Complex beta[], Complex q[], Integer pdq, Complex z[], Integer pdz, Integer *m, double *pl, double *pr, double dif[], NagError *fail)
The function may be called by the names: f08yuc, nag_lapackeig_ztgsen or nag_ztgsen.

## 3Description

f08yuc factorizes the generalized complex $n$ by $n$ matrix pair $\left(S,T\right)$ in generalized Schur form, using a unitary equivalence transformation as
 $S = Q^ S^ Z^H , T= Q^ T^ Z^H ,$
where $\left(\stackrel{^}{S},\stackrel{^}{T}\right)$ are also in generalized Schur form and have the selected eigenvalues as the leading diagonal elements. The leading columns of $Q$ and $Z$ are the generalized Schur vectors corresponding to the selected eigenvalues and form orthonormal subspaces for the left and right eigenspaces (deflating subspaces) of the pair $\left(S,T\right)$.
The pair $\left(S,T\right)$ are in generalized Schur form if $S$ and $T$ are upper triangular as returned, for example, by f08xnc, or f08xsc with ${\mathbf{job}}=\mathrm{Nag_Schur}$. The diagonal elements define the generalized eigenvalues $\left({\alpha }_{\mathit{i}},{\beta }_{\mathit{i}}\right)$, for $\mathit{i}=1,2,\dots ,n$, of the pair $\left(S,T\right)$. The eigenvalues are given by
 $λi = αi / βi ,$
but are returned as the pair $\left({\alpha }_{i},{\beta }_{i}\right)$ in order to avoid possible overflow in computing ${\lambda }_{i}$. Optionally, the function returns reciprocals of condition number estimates for the selected eigenvalue cluster, $p$ and $q$, the right and left projection norms, and of deflating subspaces, ${\mathrm{Dif}}_{u}$ and ${\mathrm{Dif}}_{l}$. For more information see Sections 2.4.8 and 4.11 of Anderson et al. (1999).
If $S$ and $T$ are the result of a generalized Schur factorization of a matrix pair $\left(A,B\right)$
 $A = QSZH , B= QTZH$
then, optionally, the matrices $Q$ and $Z$ can be updated as $Q\stackrel{^}{Q}$ and $Z\stackrel{^}{Z}$. Note that the condition numbers of the pair $\left(S,T\right)$ are the same as those of the pair $\left(A,B\right)$.

## 4References

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

## 5Arguments

1: $\mathbf{order}$Nag_OrderType Input
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 ${\mathbf{order}}=\mathrm{Nag_RowMajor}$. See Section 3.1.3 in the Introduction to the NAG Library CL Interface for a more detailed explanation of the use of this argument.
Constraint: ${\mathbf{order}}=\mathrm{Nag_RowMajor}$ or $\mathrm{Nag_ColMajor}$.
2: $\mathbf{ijob}$Integer Input
On entry: specifies whether condition numbers are required for the cluster of eigenvalues ($p$ and $q$) or the deflating subspaces (${\mathrm{Dif}}_{u}$ and ${\mathrm{Dif}}_{l}$).
${\mathbf{ijob}}=0$
Only reorder with respect to select. No extras.
${\mathbf{ijob}}=1$
Reciprocal of norms of ‘projections’ onto left and right eigenspaces with respect to the selected cluster ($p$ and $q$).
${\mathbf{ijob}}=2$
The upper bounds on ${\mathrm{Dif}}_{u}$ and ${\mathrm{Dif}}_{l}$. $F$-norm-based estimate (stored in ${\mathbf{dif}}\left[0\right]$ and ${\mathbf{dif}}\left[1\right]$ respectively).
${\mathbf{ijob}}=3$
Estimate of ${\mathrm{Dif}}_{u}$ and ${\mathrm{Dif}}_{l}$. $1$-norm-based estimate (stored in ${\mathbf{dif}}\left[0\right]$ and ${\mathbf{dif}}\left[1\right]$ respectively). About five times as expensive as ${\mathbf{ijob}}=2$.
${\mathbf{ijob}}=4$
Compute pl, pr and dif as in ${\mathbf{ijob}}=0$, $1$ and $2$. Economic version to get it all.
${\mathbf{ijob}}=5$
Compute pl, pr and dif as in ${\mathbf{ijob}}=0$, $1$ and $3$.
Constraint: $0\le {\mathbf{ijob}}\le 5$.
3: $\mathbf{wantq}$Nag_Boolean Input
On entry: if ${\mathbf{wantq}}=\mathrm{Nag_TRUE}$, update the left transformation matrix $Q$.
If ${\mathbf{wantq}}=\mathrm{Nag_FALSE}$, do not update $Q$.
4: $\mathbf{wantz}$Nag_Boolean Input
On entry: if ${\mathbf{wantz}}=\mathrm{Nag_TRUE}$, update the right transformation matrix $Z$.
If ${\mathbf{wantz}}=\mathrm{Nag_FALSE}$, do not update $Z$.
5: $\mathbf{select}\left[{\mathbf{n}}\right]$const Nag_Boolean Input
On entry: specifies the eigenvalues in the selected cluster. To select an eigenvalue ${\lambda }_{j}$, ${\mathbf{select}}\left[j-1\right]$ must be set to Nag_TRUE.
6: $\mathbf{n}$Integer Input
On entry: $n$, the order of the matrices $S$ and $T$.
Constraint: ${\mathbf{n}}\ge 0$.
7: $\mathbf{a}\left[\mathit{dim}\right]$Complex Input/Output
Note: the dimension, dim, of the array a must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{pda}}×{\mathbf{n}}\right)$.
The $\left(i,j\right)$th element of the matrix $A$ is stored in
• ${\mathbf{a}}\left[\left(j-1\right)×{\mathbf{pda}}+i-1\right]$ when ${\mathbf{order}}=\mathrm{Nag_ColMajor}$;
• ${\mathbf{a}}\left[\left(i-1\right)×{\mathbf{pda}}+j-1\right]$ when ${\mathbf{order}}=\mathrm{Nag_RowMajor}$.
On entry: the matrix $S$ in the pair $\left(S,T\right)$.
On exit: the updated matrix $\stackrel{^}{S}$.
8: $\mathbf{pda}$Integer Input
On entry: the stride separating row or column elements (depending on the value of order) in the array a.
Constraint: ${\mathbf{pda}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
9: $\mathbf{b}\left[\mathit{dim}\right]$Complex Input/Output
Note: the dimension, dim, of the array b must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{pdb}}×{\mathbf{n}}\right)$.
The $\left(i,j\right)$th element of the matrix $B$ is stored in
• ${\mathbf{b}}\left[\left(j-1\right)×{\mathbf{pdb}}+i-1\right]$ when ${\mathbf{order}}=\mathrm{Nag_ColMajor}$;
• ${\mathbf{b}}\left[\left(i-1\right)×{\mathbf{pdb}}+j-1\right]$ when ${\mathbf{order}}=\mathrm{Nag_RowMajor}$.
On entry: the matrix $T$, in the pair $\left(S,T\right)$.
On exit: the updated matrix $\stackrel{^}{T}$
10: $\mathbf{pdb}$Integer Input
On entry: the stride separating row or column elements (depending on the value of order) in the array b.
Constraint: ${\mathbf{pdb}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
11: $\mathbf{alpha}\left[{\mathbf{n}}\right]$Complex Output
12: $\mathbf{beta}\left[{\mathbf{n}}\right]$Complex Output
On exit: alpha and beta contain diagonal elements of $\stackrel{^}{S}$ and $\stackrel{^}{T}$, respectively, when the pair $\left(S,T\right)$ has been reduced to generalized Schur form. ${\mathbf{alpha}}\left[\mathit{i}-1\right]/{\mathbf{beta}}\left[\mathit{i}-1\right]$, for $\mathit{i}=1,2,\dots ,{\mathbf{n}}$, are the eigenvalues.
13: $\mathbf{q}\left[\mathit{dim}\right]$Complex Input/Output
Note: the dimension, dim, of the array q must be at least
• $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{pdq}}×{\mathbf{n}}\right)$ when ${\mathbf{wantq}}=\mathrm{Nag_TRUE}$;
• $1$ otherwise.
The $\left(i,j\right)$th element of the matrix $Q$ is stored in
• ${\mathbf{q}}\left[\left(j-1\right)×{\mathbf{pdq}}+i-1\right]$ when ${\mathbf{order}}=\mathrm{Nag_ColMajor}$;
• ${\mathbf{q}}\left[\left(i-1\right)×{\mathbf{pdq}}+j-1\right]$ when ${\mathbf{order}}=\mathrm{Nag_RowMajor}$.
On entry: if ${\mathbf{wantq}}=\mathrm{Nag_TRUE}$, the $n$ by $n$ matrix $Q$.
On exit: if ${\mathbf{wantq}}=\mathrm{Nag_TRUE}$, the updated matrix $Q\stackrel{^}{Q}$.
If ${\mathbf{wantq}}=\mathrm{Nag_FALSE}$, q is not referenced.
14: $\mathbf{pdq}$Integer Input
On entry: the stride separating row or column elements (depending on the value of order) in the array q.
Constraints:
• if ${\mathbf{wantq}}=\mathrm{Nag_TRUE}$, ${\mathbf{pdq}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$;
• otherwise ${\mathbf{pdq}}\ge 1$.
15: $\mathbf{z}\left[\mathit{dim}\right]$Complex Input/Output
Note: the dimension, dim, of the array z must be at least
• $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{pdz}}×{\mathbf{n}}\right)$ when ${\mathbf{wantz}}=\mathrm{Nag_TRUE}$;
• $1$ otherwise.
The $\left(i,j\right)$th element of the matrix $Z$ is stored in
• ${\mathbf{z}}\left[\left(j-1\right)×{\mathbf{pdz}}+i-1\right]$ when ${\mathbf{order}}=\mathrm{Nag_ColMajor}$;
• ${\mathbf{z}}\left[\left(i-1\right)×{\mathbf{pdz}}+j-1\right]$ when ${\mathbf{order}}=\mathrm{Nag_RowMajor}$.
On entry: if ${\mathbf{wantz}}=\mathrm{Nag_TRUE}$, the $n$ by $n$ matrix $Z$.
On exit: if ${\mathbf{wantz}}=\mathrm{Nag_TRUE}$, the updated matrix $Z\stackrel{^}{Z}$.
If ${\mathbf{wantz}}=\mathrm{Nag_FALSE}$, z is not referenced.
16: $\mathbf{pdz}$Integer Input
On entry: the stride separating row or column elements (depending on the value of order) in the array z.
Constraints:
• if ${\mathbf{wantz}}=\mathrm{Nag_TRUE}$, ${\mathbf{pdz}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$;
• otherwise ${\mathbf{pdz}}\ge 1$.
17: $\mathbf{m}$Integer * Output
On exit: the dimension of the specified pair of left and right eigenspaces (deflating subspaces), where m will be in the range $0\le {\mathbf{m}}\le {\mathbf{n}}$.
18: $\mathbf{pl}$double * Output
19: $\mathbf{pr}$double * Output
On exit: if ${\mathbf{ijob}}=1$, $4$ or $5$, pl and pr are lower bounds on the reciprocal of the norm of ‘projections’ $p$ and $q$ onto left and right eigenspace with respect to the selected cluster. $0<{\mathbf{pl}}$, ${\mathbf{pr}}\le 1$.
If ${\mathbf{m}}=0$ or ${\mathbf{m}}={\mathbf{n}}$, ${\mathbf{pl}}={\mathbf{pr}}=1$.
If ${\mathbf{ijob}}=0$, $2$ or $3$, pl and pr are not referenced.
20: $\mathbf{dif}\left[\mathit{dim}\right]$double Output
Note: the dimension, dim, of the array dif must be at least $2$.
On exit: if ${\mathbf{ijob}}\ge 2$, ${\mathbf{dif}}\left[0\right]$ and ${\mathbf{dif}}\left[1\right]$ store the estimates of ${\mathrm{Dif}}_{u}$ and ${\mathrm{Dif}}_{l}$.
If ${\mathbf{ijob}}=2$ or $4$, ${\mathbf{dif}}\left[0\right]$ and ${\mathbf{dif}}\left[1\right]$ are $F$-norm-based upper bounds on ${\mathrm{Dif}}_{u}$ and ${\mathrm{Dif}}_{l}$.
If ${\mathbf{ijob}}=3$ or $5$, ${\mathbf{dif}}\left[0\right]$ and ${\mathbf{dif}}\left[1\right]$ are $1$-norm-based estimates of ${\mathrm{Dif}}_{u}$ and ${\mathrm{Dif}}_{l}$.
If ${\mathbf{m}}=0$ or $n$, ${\mathbf{dif}}\left[0\right]$ and ${\mathbf{dif}}\left[1\right]$ $={‖\left(A,B\right)‖}_{F}$.
If ${\mathbf{ijob}}=0$ or $1$, dif is not referenced.
21: $\mathbf{fail}$NagError * Input/Output
The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).

## 6Error Indicators and Warnings

NE_ALLOC_FAIL
Dynamic memory allocation failed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
On entry, argument $〈\mathit{\text{value}}〉$ had an illegal value.
NE_CONSTRAINT
Constraint: if ${\mathbf{wantq}}=\mathrm{Nag_TRUE}$, ${\mathbf{pdq}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$;
otherwise ${\mathbf{pdq}}\ge 1$.
Constraint: if ${\mathbf{wantz}}=\mathrm{Nag_TRUE}$, ${\mathbf{pdz}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$;
otherwise ${\mathbf{pdz}}\ge 1$.
NE_INT
On entry, ${\mathbf{ijob}}=〈\mathit{\text{value}}〉$.
Constraint: $0\le {\mathbf{ijob}}\le 5$.
On entry, ${\mathbf{n}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{n}}\ge 0$.
On entry, ${\mathbf{pda}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{pda}}>0$.
On entry, ${\mathbf{pdb}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{pdb}}>0$.
On entry, ${\mathbf{pdq}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{pdq}}>0$.
On entry, ${\mathbf{pdz}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{pdz}}>0$.
NE_INT_2
On entry, ${\mathbf{pda}}=〈\mathit{\text{value}}〉$ and ${\mathbf{n}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{pda}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
On entry, ${\mathbf{pdb}}=〈\mathit{\text{value}}〉$ and ${\mathbf{n}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{pdb}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
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 7.5 in the Introduction to the NAG Library CL Interface for further information.
NE_NO_LICENCE
Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library CL Interface for further information.
NE_SCHUR
Reordering of $\left(S,T\right)$ failed because the transformed matrix pair would be too far from generalized Schur form; the problem is very ill-conditioned. $\left(S,T\right)$ may have been partially reordered. If requested, $0$ is returned in ${\mathbf{dif}}\left[0\right]$ and ${\mathbf{dif}}\left[1\right]$, pl and pr.

## 7Accuracy

The computed generalized Schur form is nearly the exact generalized Schur form for nearby matrices $\left(S+E\right)$ and $\left(T+F\right)$, where
 $E2 = O⁡ε S2 and F2= O⁡ε T2 ,$
and $\epsilon$ is the machine precision. See Section 4.11 of Anderson et al. (1999) for further details of error bounds for the generalized nonsymmetric eigenproblem, and for information on the condition numbers returned.

## 8Parallelism and Performance

f08yuc 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.

The real analogue of this function is f08ygc.

## 10Example

This example reorders the generalized Schur factors $S$ and $T$ and update the matrices $Q$ and $Z$ given by
 $S = 4.0+4.0i 1.0+1.0i 1.0+1.0i 2.0-1.0i 0.0i+0.0 2.0+1.0i 1.0+1.0i 1.0+1.0i 0.0i+0.0 0.0i+0.0 2.0-1.0i 1.0+1.0i 0.0i+0.0 0.0i+0.0 0.0i+0.0 6.0-2.0i ,$
 $T = 2.0 1.0+1.0i 1.0+1.0i 3.0-1.0i 0.0 1.0i+0.0 2.0+1.0i 1.0+1.0i 0.0 0.0i+0.0 1.0i+0.0 1.0+1.0i 0.0 0.0i+0.0 0.0i+0.0 2.0i+0.0 ,$
 $Q = 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 and Z= 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 ,$
selecting the second and third generalized eigenvalues to be moved to the leading positions. Bases for the left and right deflating subspaces, and estimates of the condition numbers for the eigenvalues and Frobenius norm based bounds on the condition numbers for the deflating subspaces are also output.

### 10.1Program Text

Program Text (f08yuce.c)

### 10.2Program Data

Program Data (f08yuce.d)

### 10.3Program Results

Program Results (f08yuce.r)