Integer type:  int32  int64  nag_int  show int32  show int32  show int64  show int64  show nag_int  show nag_int

Chapter Contents
Chapter Introduction
NAG Toolbox

# NAG Toolbox: nag_lapack_zgeevx (f08np)

## Purpose

nag_lapack_zgeevx (f08np) computes the eigenvalues and, optionally, the left and/or right eigenvectors for an $n$ by $n$ complex nonsymmetric matrix $A$.
Optionally, it also computes a balancing transformation to improve the conditioning of the eigenvalues and eigenvectors, reciprocal condition numbers for the eigenvalues, and reciprocal condition numbers for the right eigenvectors.

## Syntax

[a, w, vl, vr, ilo, ihi, scale, abnrm, rconde, rcondv, info] = f08np(balanc, jobvl, jobvr, sense, a, 'n', n)
[a, w, vl, vr, ilo, ihi, scale, abnrm, rconde, rcondv, info] = nag_lapack_zgeevx(balanc, jobvl, jobvr, sense, a, 'n', n)

## Description

The right eigenvector ${v}_{j}$ of $A$ satisfies
 $A vj = λj vj$
where ${\lambda }_{j}$ is the $j$th eigenvalue of $A$. The left eigenvector ${u}_{j}$ of $A$ satisfies
 $ujH A = λj ujH$
where ${u}_{j}^{\mathrm{H}}$ denotes the conjugate transpose of ${u}_{j}$.
Balancing a matrix means permuting the rows and columns to make it more nearly upper triangular, and applying a diagonal similarity transformation $DA{D}^{-1}$, where $D$ is a diagonal matrix, with the aim of making its rows and columns closer in norm and the condition numbers of its eigenvalues and eigenvectors smaller. The computed reciprocal condition numbers correspond to the balanced matrix. Permuting rows and columns will not change the condition numbers (in exact arithmetic) but diagonal scaling will. For further explanation of balancing, see Section 4.8.1.2 of Anderson et al. (1999).
Following the optional balancing, the matrix $A$ is first reduced to upper Hessenberg form by means of unitary similarity transformations, and the $QR$ algorithm is then used to further reduce the matrix to upper triangular Schur form, $T$, from which the eigenvalues are computed. Optionally, the eigenvectors of $T$ are also computed and backtransformed to those of $A$.

## 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

## Parameters

### Compulsory Input Parameters

1:     $\mathrm{balanc}$ – string (length ≥ 1)
Indicates how the input matrix should be diagonally scaled and/or permuted to improve the conditioning of its eigenvalues.
${\mathbf{balanc}}=\text{'N'}$
Do not diagonally scale or permute.
${\mathbf{balanc}}=\text{'P'}$
Perform permutations to make the matrix more nearly upper triangular. Do not diagonally scale.
${\mathbf{balanc}}=\text{'S'}$
Diagonally scale the matrix, i.e., replace $A$ by $DA{D}^{-1}$, where $D$ is a diagonal matrix chosen to make the rows and columns of $A$ more equal in norm. Do not permute.
${\mathbf{balanc}}=\text{'B'}$
Both diagonally scale and permute $A$.
Computed reciprocal condition numbers will be for the matrix after balancing and/or permuting. Permuting does not change condition numbers (in exact arithmetic), but balancing does.
Constraint: ${\mathbf{balanc}}=\text{'N'}$, $\text{'P'}$, $\text{'S'}$ or $\text{'B'}$.
2:     $\mathrm{jobvl}$ – string (length ≥ 1)
If ${\mathbf{jobvl}}=\text{'N'}$, the left eigenvectors of $A$ are not computed.
If ${\mathbf{jobvl}}=\text{'V'}$, the left eigenvectors of $A$ are computed.
If ${\mathbf{sense}}=\text{'E'}$ or $\text{'B'}$, jobvl must be set to ${\mathbf{jobvl}}=\text{'V'}$.
Constraint: ${\mathbf{jobvl}}=\text{'N'}$ or $\text{'V'}$.
3:     $\mathrm{jobvr}$ – string (length ≥ 1)
If ${\mathbf{jobvr}}=\text{'N'}$, the right eigenvectors of $A$ are not computed.
If ${\mathbf{jobvr}}=\text{'V'}$, the right eigenvectors of $A$ are computed.
If ${\mathbf{sense}}=\text{'E'}$ or $\text{'B'}$, jobvr must be set to ${\mathbf{jobvr}}=\text{'V'}$.
Constraint: ${\mathbf{jobvr}}=\text{'N'}$ or $\text{'V'}$.
4:     $\mathrm{sense}$ – string (length ≥ 1)
Determines which reciprocal condition numbers are computed.
${\mathbf{sense}}=\text{'N'}$
None are computed.
${\mathbf{sense}}=\text{'E'}$
Computed for eigenvalues only.
${\mathbf{sense}}=\text{'V'}$
Computed for right eigenvectors only.
${\mathbf{sense}}=\text{'B'}$
Computed for eigenvalues and right eigenvectors.
If ${\mathbf{sense}}=\text{'E'}$ or $\text{'B'}$, both left and right eigenvectors must also be computed (${\mathbf{jobvl}}=\text{'V'}$ and ${\mathbf{jobvr}}=\text{'V'}$).
Constraint: ${\mathbf{sense}}=\text{'N'}$, $\text{'E'}$, $\text{'V'}$ or $\text{'B'}$.
5:     $\mathrm{a}\left(\mathit{lda},:\right)$ – complex array
The first dimension of the array a must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
The second dimension of the array a must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
The $n$ by $n$ matrix $A$.

### Optional Input Parameters

1:     $\mathrm{n}$int64int32nag_int scalar
Default: the first dimension of the array a and the second dimension of the array a. (An error is raised if these dimensions are not equal.)
$n$, the order of the matrix $A$.
Constraint: ${\mathbf{n}}\ge 0$.

### Output Parameters

1:     $\mathrm{a}\left(\mathit{lda},:\right)$ – complex array
The first dimension of the array a will be $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
The second dimension of the array a will be $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
a has been overwritten. If ${\mathbf{jobvl}}=\text{'V'}$ or ${\mathbf{jobvr}}=\text{'V'}$, $A$ contains the Schur form of the balanced version of the matrix $A$.
2:     $\mathrm{w}\left(:\right)$ – complex array
The dimension of the array w will be $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$
Contains the computed eigenvalues.
3:     $\mathrm{vl}\left(\mathit{ldvl},:\right)$ – complex array
The first dimension, $\mathit{ldvl}$, of the array vl will be
• if ${\mathbf{jobvl}}=\text{'V'}$, $\mathit{ldvl}=\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$;
• otherwise $\mathit{ldvl}=1$.
The second dimension of the array vl will be $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$ if ${\mathbf{jobvl}}=\text{'V'}$ and $1$ otherwise.
If ${\mathbf{jobvl}}=\text{'V'}$, the left eigenvectors ${u}_{j}$ are stored one after another in the columns of vl, in the same order as their corresponding eigenvalues; that is ${u}_{j}={\mathbf{vl}}\left(:,j\right)$, the $j$th column of vl.
If ${\mathbf{jobvl}}=\text{'N'}$, vl is not referenced.
4:     $\mathrm{vr}\left(\mathit{ldvr},:\right)$ – complex array
The first dimension, $\mathit{ldvr}$, of the array vr will be
• if ${\mathbf{jobvr}}=\text{'V'}$, $\mathit{ldvr}=\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$;
• otherwise $\mathit{ldvr}=1$.
The second dimension of the array vr will be $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$ if ${\mathbf{jobvr}}=\text{'V'}$ and $1$ otherwise.
If ${\mathbf{jobvr}}=\text{'V'}$, the right eigenvectors ${v}_{j}$ are stored one after another in the columns of vr, in the same order as their corresponding eigenvalues; that is ${v}_{j}={\mathbf{vr}}\left(:,j\right)$, the $j$th column of vr.
If ${\mathbf{jobvr}}=\text{'N'}$, vr is not referenced.
5:     $\mathrm{ilo}$int64int32nag_int scalar
6:     $\mathrm{ihi}$int64int32nag_int scalar
ilo and ihi are integer values determined when $A$ was balanced. The balanced $A$ has ${a}_{ij}=0$ if $i>j$ and $j=1,2,\dots ,{\mathbf{ilo}}-1$ or $i={\mathbf{ihi}}+1,\dots ,{\mathbf{n}}$.
7:     $\mathrm{scale}\left(:\right)$ – double array
The dimension of the array scale will be $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$
Details of the permutations and scaling factors applied when balancing $A$.
If ${p}_{j}$ is the index of the row and column interchanged with row and column $j$, and ${d}_{j}$ is the scaling factor applied to row and column $j$, then
• ${\mathbf{scale}}\left(\mathit{j}\right)={p}_{\mathit{j}}$, for $\mathit{j}=1,2,\dots ,{\mathbf{ilo}}-1$;
• ${\mathbf{scale}}\left(\mathit{j}\right)={d}_{\mathit{j}}$, for $\mathit{j}={\mathbf{ilo}},\dots ,{\mathbf{ihi}}$;
• ${\mathbf{scale}}\left(\mathit{j}\right)={p}_{\mathit{j}}$, for $\mathit{j}={\mathbf{ihi}}+1,\dots ,{\mathbf{n}}$.
The order in which the interchanges are made is n to ${\mathbf{ihi}}+1$, then $1$ to ${\mathbf{ilo}}-1$.
8:     $\mathrm{abnrm}$ – double scalar
The $1$-norm of the balanced matrix (the maximum of the sum of absolute values of elements of any column).
9:     $\mathrm{rconde}\left(:\right)$ – double array
The dimension of the array rconde will be $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$
${\mathbf{rconde}}\left(j\right)$ is the reciprocal condition number of the $j$th eigenvalue.
10:   $\mathrm{rcondv}\left(:\right)$ – double array
The dimension of the array rcondv will be $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$
${\mathbf{rcondv}}\left(j\right)$ is the reciprocal condition number of the $j$th right eigenvector.
11:   $\mathrm{info}$int64int32nag_int scalar
${\mathbf{info}}=0$ unless the function detects an error (see Error Indicators and Warnings).

## Error Indicators and Warnings

Cases prefixed with W are classified as warnings and do not generate an error of type NAG:error_n. See nag_issue_warnings.

${\mathbf{info}}=-i$
If ${\mathbf{info}}=-i$, parameter $i$ had an illegal value on entry. The parameters are numbered as follows:
1: balanc, 2: jobvl, 3: jobvr, 4: sense, 5: n, 6: a, 7: lda, 8: w, 9: vl, 10: ldvl, 11: vr, 12: ldvr, 13: ilo, 14: ihi, 15: scale, 16: abnrm, 17: rconde, 18: rcondv, 19: work, 20: lwork, 21: rwork, 22: info.
It is possible that info refers to a parameter that is omitted from the MATLAB interface. This usually indicates that an error in one of the other input parameters has caused an incorrect value to be inferred.
W  ${\mathbf{info}}>0$
If ${\mathbf{info}}=i$, the $QR$ algorithm failed to compute all the eigenvalues, and no eigenvectors or condition numbers have been computed; elements $1:{\mathbf{ilo}}-1$ and $i+1:{\mathbf{n}}$ of w contain eigenvalues which have converged.

## Accuracy

The computed eigenvalues and eigenvectors are exact for a nearby matrix $\left(A+E\right)$, where
 $E2 = Oε A2 ,$
and $\epsilon$ is the machine precision. See Section 4.8 of Anderson et al. (1999) for further details.

Each eigenvector is normalized to have Euclidean norm equal to unity and the element of largest absolute value real.
The total number of floating-point operations is proportional to ${n}^{3}$.
The real analogue of this function is nag_lapack_dgeevx (f08nb).

## Example

This example finds all the eigenvalues and right eigenvectors of the matrix
 $A = -3.97-5.04i -4.11+3.70i -0.34+1.01i 1.29-0.86i 0.34-1.50i 1.52-0.43i 1.88-5.38i 3.36+0.65i 3.31-3.85i 2.50+3.45i 0.88-1.08i 0.64-1.48i -1.10+0.82i 1.81-1.59i 3.25+1.33i 1.57-3.44i ,$
together with estimates of the condition number and forward error bounds for each eigenvalue and eigenvector. The option to balance the matrix is used. In order to compute the condition numbers of the eigenvalues, the left eigenvectors also have to be computed, but they are not printed out in this example.
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.
function f08np_example

fprintf('f08np example results\n\n');

% Complex matrix A
n = 4;
a = [-3.97 - 5.04i, -4.11 + 3.70i, -0.34 + 1.01i,  1.29 - 0.86i;
0.34 - 1.50i,  1.52 - 0.43i,  1.88 - 5.38i,  3.36 + 0.65i;
3.31 - 3.85i,  2.50 + 3.45i,  0.88 - 1.08i,  0.64 - 1.48i;
-1.10 + 0.82i,  1.81 - 1.59i,  3.25 + 1.33i,  1.57 - 3.44i];

% Eigenvalues and left and right eigenvectors of A after matrix balancing
balanc = 'Balance';
jobvl = 'Vectors (left)';
jobvr = 'Vectors (right)';
sense = 'Both reciprocal condition numbers';
[a, w, vl, vr, ilo, ihi, scale, abnrm, rconde, rcondv, info] = ...
f08np( ...
balanc, jobvl, jobvr, sense, a);

% Normalize eigenvectors: largest elements are real
for i = 1:n
[~,k] = max(abs(real(vr(:,i)))+abs(imag(vr(:,i))));
vr(:,i) = vr(:,i)*conj(vr(k,i))/abs(vr(k,i));
end

disp('Eigenvalues');
fprintf('\n        Eigenvalue                  rcond\n\n');
for j=1:n
fprintf('%3d',j);
if imag(w(j))<0
fprintf('%15.4e - %10.4ei%10.4f\n',real(w(j)),abs(imag(w(j))),rconde(j));
else
fprintf('%15.4e + %10.4ei%10.4f\n',real(w(j)),imag(w(j)),rconde(j));
end
end

fprintf('\nRight Eigenvectors\n\n');
fprintf('        Eigenvector                 rcond\n');
for j = 1:n
fprintf('\n%3d',j);
for l = 1:n
if (l>1)
fprintf('%3s', ' ');
end
if imag(vr(l,k))>0
fprintf('%15.4e + %10.4ei', real(vr(l,k)), imag(vr(l,k)));
else
fprintf('%15.4e - %10.4ei', real(vr(l,k)), abs(imag(vr(l,k))));
end
if l==1
fprintf('%10.4f', rcondv(j));
end
fprintf('\n');
end
end

f08np example results

Eigenvalues

Eigenvalue                  rcond

1    -6.0004e+00 - 6.9998e+00i    0.9932
2    -5.0000e+00 + 2.0060e+00i    0.9964
3     7.9982e+00 - 9.9637e-01i    0.9814
4     3.0023e+00 - 3.9998e+00i    0.9779

Right Eigenvectors

Eigenvector                 rcond

1    -3.5614e-02 - 1.7822e-01i    8.4011
1.2637e-01 + 2.6663e-01i
1.2933e-02 - 2.9657e-01i
8.8982e-01 - 0.0000e+00i

2    -3.5614e-02 - 1.7822e-01i    8.0214
1.2637e-01 + 2.6663e-01i
1.2933e-02 - 2.9657e-01i
8.8982e-01 - 0.0000e+00i

3    -3.5614e-02 - 1.7822e-01i    5.8292
1.2637e-01 + 2.6663e-01i
1.2933e-02 - 2.9657e-01i
8.8982e-01 - 0.0000e+00i

4    -3.5614e-02 - 1.7822e-01i    5.8292
1.2637e-01 + 2.6663e-01i
1.2933e-02 - 2.9657e-01i
8.8982e-01 - 0.0000e+00i