NAG Library Function Document

nag_zggev (f08wnc)

void	nag_zggev (Nag_OrderType order, Nag_LeftVecsType jobvl, Nag_RightVecsType jobvr, Integer n, Complex a[], Integer pda, Complex b[], Integer pdb, Complex alpha[], Complex beta[], Complex vl[], Integer pdvl, Complex vr[], Integer pdvr, NagError *fail)

3 Description

A generalized eigenvalue for a pair of matrices

(A, B)

is a scalar

λ

or a ratio

α / β = λ

, such that

A - λ B

is singular. It is usually represented as the pair

(α, β)

, as there is a reasonable interpretation for

β = 0

, and even for both being zero.

The right generalized eigenvector

v_{j}

corresponding to the generalized eigenvalue

λ_{j}

(A, B)

satisfies

A v_{j} = λ_{j} B v_{j} .

The left generalized eigenvector

u_{j}

corresponding to the generalized eigenvalue

λ_{j}

(A, B)

satisfies

u_{j}^{H} A = λ_{j} u_{j}^{H} B,

where

u_{j}^{H}

is the conjugate-transpose of

u_{j}

All the eigenvalues and, if required, all the eigenvectors of the complex generalized eigenproblem

A x = λ B x

, where

A

and

B

are complex, square matrices, are determined using the

Q Z

algorithm. The complex

Q Z

algorithm consists of three stages:

A

is reduced to upper Hessenberg form (with real, non-negative subdiagonal elements) and at the same time

B

is reduced to upper triangular form.

A

is further reduced to triangular form while the triangular form of

B

is maintained and the diagonal elements of

B

are made real and non-negative. This is the generalized Schur form of the pair

(A, B)

This function does not actually produce the eigenvalues

λ_{j}

, but instead returns

α_{j}

and

β_{j}

such that

λ_{j} = α_{j} / β_{j}, j = 1, 2, \dots, n .

The division by

β_{j}

becomes your responsibility, since

β_{j}

may be zero, indicating an infinite eigenvalue.

If the eigenvectors are required they are obtained from the triangular matrices and then transformed back into the original coordinate system.

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

Wilkinson J H (1979) Kronecker's canonical form and the

Q Z

algorithm Linear Algebra Appl. 28 285–303

5 Arguments

1: order – Nag_OrderTypeInput

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

order = Nag_RowMajor

. See Section 3.2.1.3 in the Essential Introduction for a more detailed explanation of the use of this argument.

Constraint:

order = Nag_RowMajor

Nag_ColMajor

2: jobvl – Nag_LeftVecsTypeInput

On entry: if

jobvl = Nag_NotLeftVecs

, do not compute the left generalized eigenvectors.

jobvl = Nag_LeftVecs

, compute the left generalized eigenvectors.

Constraint:

jobvl = Nag_NotLeftVecs

Nag_LeftVecs

3: jobvr – Nag_RightVecsTypeInput

On entry: if

jobvr = Nag_NotRightVecs

, do not compute the right generalized eigenvectors.

jobvr = Nag_RightVecs

, compute the right generalized eigenvectors.

Constraint:

jobvr = Nag_NotRightVecs

Nag_RightVecs

4: n – IntegerInput

On entry:

n

, the order of the matrices

A

and

B

Constraint:

n \geq 0

5: a[ $\dim$ ] – ComplexInput/Output

Note: the dimension, dim, of the array a must be at least

\max (1, pda \times n)

The

(i, j)

th element of the matrix

A

is stored in

$a [(j - 1) \times pda + i - 1]$ when $order = Nag_ColMajor$ ;
$a [(i - 1) \times pda + j - 1]$ when $order = Nag_RowMajor$ .

On entry: the matrix

A

in the pair

(A, B)

On exit: a has been overwritten.

6: pda – IntegerInput

On entry: the stride separating row or column elements (depending on the value of order) in the array a.

Constraint:

pda \geq \max (1, n)

7: b[ $\dim$ ] – ComplexInput/Output

Note: the dimension, dim, of the array b must be at least

\max (1, pdb \times n)

The

(i, j)

th element of the matrix

B

is stored in

$b [(j - 1) \times pdb + i - 1]$ when $order = Nag_ColMajor$ ;
$b [(i - 1) \times pdb + j - 1]$ when $order = Nag_RowMajor$ .

On entry: the matrix

B

in the pair

(A, B)

On exit: b has been overwritten.

8: pdb – IntegerInput

On entry: the stride separating row or column elements (depending on the value of order) in the array b.

Constraint:

pdb \geq \max (1, n)

9: alpha[n] – ComplexOutput

On exit: see the description of beta.

10: beta[n] – ComplexOutput

On exit:

alpha [j - 1] / beta [j - 1]

, for

j = 1, 2, \dots, n

, will be the generalized eigenvalues.

Note: the quotients

alpha [j - 1] / beta [j - 1]

may easily overflow or underflow, and

beta [j - 1]

may even be zero. Thus, you should avoid naively computing the ratio

α_{j} / β_{j}

. However,

\max (|α_{j}|)

will always be less than and usually comparable with

{‖a‖}_{2}

in magnitude, and

\max (|β_{j}|)

will always be less than and usually comparable with

{‖b‖}_{2}

11: vl[ $\dim$ ] – ComplexOutput

Note: the dimension, dim, of the array vl must be at least

$\max (1, pdvl \times n)$ when $jobvl = Nag_LeftVecs$ ;
$1$ otherwise.

The

i

th element of the

j

th vector is stored in

$vl [(j - 1) \times pdvl + i - 1]$ when $order = Nag_ColMajor$ ;
$vl [(i - 1) \times pdvl + j - 1]$ when $order = Nag_RowMajor$ .

On exit: if

jobvl = Nag_LeftVecs

, the left generalized eigenvectors

u_{j}

are stored one after another in the columns of vl, in the same order as the corresponding eigenvalues. Each eigenvector will be scaled so the largest component will have

|real part| + |imag. part| = 1

jobvl = Nag_NotLeftVecs

, vl is not referenced.

12: pdvl – IntegerInput

On entry: the stride used in the array vl.

Constraints:

if $jobvl = Nag_LeftVecs$ , $pdvl \geq \max (1, n)$ ;
otherwise $pdvl \geq 1$ .

13: vr[ $\dim$ ] – ComplexOutput

Note: the dimension, dim, of the array vr must be at least

$\max (1, pdvr \times n)$ when $jobvr = Nag_RightVecs$ ;
$1$ otherwise.

The

i

th element of the

j

th vector is stored in

$vr [(j - 1) \times pdvr + i - 1]$ when $order = Nag_ColMajor$ ;
$vr [(i - 1) \times pdvr + j - 1]$ when $order = Nag_RowMajor$ .

On exit: if

jobvr = Nag_RightVecs

, the right generalized eigenvectors

v_{j}

are stored one after another in the columns of vr, in the same order as the corresponding eigenvalues. Each eigenvector will be scaled so the largest component will have

|real part| + |imag. part| = 1

jobvr = Nag_NotRightVecs

, vr is not referenced.

14: pdvr – IntegerInput

On entry: the stride used in the array vr.

Constraints:

if $jobvr = Nag_RightVecs$ , $pdvr \geq \max (1, n)$ ;
otherwise $pdvr \geq 1$ .

15: fail – NagError *Input/Output

The NAG error argument (see Section 3.6 in the Essential Introduction).

6 Error Indicators and Warnings

NE_ALLOC_FAIL: Dynamic memory allocation failed.
NE_BAD_PARAM: On entry, argument $⟨value⟩$ had an illegal value.
NE_EIGENVECTORS: A failure occurred in nag_dtgevc (f08ykc) while computing generalized eigenvectors.
NE_ENUM_INT_2: On entry, $jobvl = ⟨value⟩$ , $pdvl = ⟨value⟩$ and $n = ⟨value⟩$ .
Constraint: if $jobvl = Nag_LeftVecs$ , $pdvl \geq \max (1, n)$ ;
otherwise $pdvl \geq 1$ .
On entry, $jobvr = ⟨value⟩$ , $pdvr = ⟨value⟩$ and $n = ⟨value⟩$ .
Constraint: if $jobvr = Nag_RightVecs$ , $pdvr \geq \max (1, n)$ ;
otherwise $pdvr \geq 1$ .
NE_INT: On entry, $n = ⟨value⟩$ .
Constraint: $n \geq 0$ .
On entry, $pda = ⟨value⟩$ .
Constraint: $pda > 0$ .
On entry, $pdb = ⟨value⟩$ .
Constraint: $pdb > 0$ .
On entry, $pdvl = ⟨value⟩$ .
Constraint: $pdvl > 0$ .
On entry, $pdvr = ⟨value⟩$ .
Constraint: $pdvr > 0$ .
NE_INT_2: On entry, $pda = ⟨value⟩$ and $n = ⟨value⟩$ .
Constraint: $pda \geq \max (1, n)$ .
On entry, $pdb = ⟨value⟩$ and $n = ⟨value⟩$ .
Constraint: $pdb \geq \max (1, n)$ .
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.
NE_ITERATION_QZ: The $Q Z$ iteration failed. No eigenvectors have been calculated but alpha and beta should be correct from element $⟨value⟩$ .
The $Q Z$ iteration failed with an unexpected error, please contact NAG.

7 Accuracy

The computed eigenvalues and eigenvectors are exact for a nearby matrices

(A + E)

and

(B + F)

, where

{‖(E, F)‖}_{F} = O (ε) {‖(A, B)‖}_{F},

and

ε

is the machine precision. See Section 4.11 of Anderson et al. (1999) for further details.

Note: interpretation of results obtained with the

Q Z

algorithm often requires a clear understanding of the effects of small changes in the original data. These effects are reviewed in Wilkinson (1979), in relation to the significance of small values of

α_{j}

and

β_{j}

. It should be noted that if

α_{j}

and

β_{j}

are both small for any

j

, it may be that no reliance can be placed on any of the computed eigenvalues

λ_{i} = α_{i} / β_{i}

. You are recommended to study Wilkinson (1979) and, if in difficulty, to seek expert advice on determining the sensitivity of the eigenvalues to perturbations in the data.

8 Parallelism and Performance

nag_zggev (f08wnc) is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

nag_zggev (f08wnc) 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 Users' Note for your implementation for any additional implementation-specific information.

9 Further Comments

The total number of floating-point operations is proportional to

n^{3}

The real analogue of this function is nag_dggev (f08wac).

10 Example

This example finds all the eigenvalues and right eigenvectors of the matrix pair

(A, B)

, where

A = (\begin{array}{r} - 21.10 - 22.50 i & 53.50 - 50.50 i & - 34.50 + 127.50 i & 7.50 + 0.50 i \\ - 0.46 - 7.78 i & - 3.50 - 37.50 i & - 15.50 + 58.50 i & - 10.50 - 1.50 i \\ 4.30 - 5.50 i & 39.70 - 17.10 i & - 68.50 + 12.50 i & - 7.50 - 3.50 i \\ 5.50 + 4.40 i & 14.40 + 43.30 i & - 32.50 - 46.00 i & - 19.00 - 32.50 i \end{array})

and

B = (\begin{array}{r} 1.00 - 5.00 i & 1.60 + 1.20 i & - 3.00 + 0.00 i & 0.00 - 1.00 i \\ 0.80 - 0.60 i & 3.00 - 5.00 i & - 4.00 + 3.00 i & - 2.40 - 3.20 i \\ 1.00 + 0.00 i & 2.40 + 1.80 i & - 4.00 - 5.00 i & 0.00 - 3.00 i \\ 0.00 + 1.00 i & - 1.80 + 2.40 i & 0.00 - 4.00 i & 4.00 - 5.00 i \end{array}) .

NAG Library Function Documentnag_zggev (f08wnc)

+− Contents

1 Purpose

2 Specification

3 Description

4 References

5 Arguments

6 Error Indicators and Warnings

7 Accuracy

8 Parallelism and Performance

9 Further Comments

10 Example

10.1 Program Text

10.2 Program Data

10.3 Program Results

NAG Library Function Document

nag_zggev (f08wnc)