void	f02gcc (Nag_Select_Eigenvalues crit, Integer n, Complex a[], Integer tda, double wl, double wu, Integer mest, Integer m, Complex w[], Complex v[], Integer tdv, NagError fail)

The function may be called by the names: f02gcc, nag_eigen_complex_gen_eigsys or nag_complex_eigensystem_sel.

3 Description

f02gcc computes selected eigenvalues and the corresponding right eigenvectors of a complex general matrix

A

{A x}_{i} = λ_{i} x_{i} .

Eigenvalues

λ_{i}

may be selected either by modulus, satisfying:

w_{l} \leq | λ_{i} | \leq w_{u},

or by real part, satisfying:

w_{l} \leq Re (λ_{i}) \leq w_{u} .

4 References

Golub G H and Van Loan C F (1996) Matrix Computations (3rd Edition) Johns Hopkins University Press, Baltimore

5 Arguments

1: $crit$ – Nag_Select_Eigenvalues Input

On entry: indicates the criterion for selecting eigenvalues:

if $crit = Nag_Select_Modulus$ , then eigenvalues are selected according to their moduli: $w_{l} \leq | λ_{i} | \leq w_{u}$ .
if $crit = Nag_Select_RealPart$ , then eigenvalues are selected according to their real parts: $w_{l} \leq Re (λ_{i}) \leq w_{u}$ .

Constraint:

crit = Nag_Select_Modulus

Nag_Select_RealPart

2: $n$ – Integer Input

On entry:

n

, the order of the matrix

A

Constraint:

n \geq 0

3: $a [n \times tda]$ – Complex Input/Output

Note: the

(i, j)

th element of the matrix

A

is stored in

a [(i - 1) \times tda + j - 1]

On entry: the

n \times n

general matrix

A

On exit: a contains the Hessenberg form of the balanced input matrix

A^{'}

(see Section 9).

4: $tda$ – Integer Input

On entry: the stride separating matrix column elements in the array a.

Constraint:

tda \geq \max (1, n)

5: $wl$ – double Input

6: $wu$ – double Input

On entry:

w_{l}

and

w_{u}

, the lower and upper bounds on the criterion for the selected eigenvalues.

Constraint:

wu > wl

7: $mest$ – Integer Input

On entry: mest must be an upper bound on

m

, the number of eigenvalues and eigenvectors selected. No eigenvectors are computed if

mest < m

Constraint:

mest \geq \max (1, m)

8: $m$ – Integer * Output

On exit:

m

, the number of eigenvalues actually selected.

9: $w [\max (1, n)]$ – Complex Output

On exit: the first m elements of w hold the selected eigenvalues; elements from the index m to

n - 1

contain the other eigenvalues.

10: $v [n \times tdv]$ – Complex Output

Note: the

(i, j)

th element of the matrix

V

is stored in

v [(i - 1) \times tdv + j - 1]

On exit: v contains the selected eigenvectors, with the

i

th column holding the eigenvector associated with the eigenvalue

λ_{i}

(stored in

w [i - 1]

11: $tdv$ – Integer Input

On entry: the stride separating matrix column elements in the array v.

Constraint:

tdv \geq mest

12: $fail$ – NagError * Input/Output

The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).

6 Error Indicators and Warnings

NE_2_INT_ARG_LT: On entry, $tdv = ⟨ value ⟩$ while $mest = ⟨ value ⟩$ . These arguments must satisfy $tdv \geq mest$ .
NE_2_REAL_ARG_LE: On entry, $wu = ⟨ value ⟩$ while $wl = ⟨ value ⟩$ . These arguments must satisfy $wu > wl$ .
NE_ALLOC_FAIL: Dynamic memory allocation failed.
NE_BAD_PARAM: On entry, argument crit had an illegal value.
NE_EIGVEC: Inverse iteration failed to compute all the specified eigenvectors. If an eigenvector failed to converge, the corresponding column of v is set to zero.
NE_INT_2: On entry, $tda = ⟨ value ⟩$ while $n = ⟨ value ⟩$ .
Constraint: $tda \geq \max (1, n)$ .
NE_INT_ARG_LT: On entry, $mest = ⟨ value ⟩$ .
Constraint: $mest \geq 1$ .

On entry, $n = ⟨ value ⟩$ .
Constraint: $n \geq 0$ .
NE_QR_FAIL: The QR algorithm failed to compute all the eigenvalues. No eigenvectors have been computed.
NE_REQD_EIGVAL: There are more than mest eigenvalues in the specified range. The actual number of eigenvalues in the range is returned in m. No eigenvectors have been computed.
Rerun with the second dimension of $v = mest \geq m$ .

7 Accuracy

λ_{i}

is an exact eigenvalue, and

{\tilde{λ}}_{i}

is the corresponding computed value, then

| {\tilde{λ}}_{i} - λ_{i} | \leq \frac{c (n) ε {‖ A^{'} ‖}_{2}}{s_{i}},

where

c (n)

is a modestly increasing function of

n

ε

is the machine precision, and

s_{i}

is the reciprocal condition number of

λ_{i}

;

A^{'}

is the balanced form of the original matrix

A

, and

‖ A^{'} ‖ \leq ‖ A ‖

x_{i}

is the corresponding exact eigenvector, and

{\tilde{x}}_{i}

is the corresponding computed eigenvector, then the angle

θ ({\tilde{x}}_{i}, x_{i})

between them is bounded as follows:

θ ({\tilde{x}}_{i}, x_{i}) \leq \frac{c (n) ε {‖ A^{'} ‖}_{2}}{{s e p}_{i}}

where

{s e p}_{i}

is the reciprocal condition number of

x_{i}

8 Parallelism and Performance

Background information to multithreading can be found in the Multithreading documentation.

f02gcc is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.

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.

9 Further Comments

f02gcc first balances the matrix, using a diagonal similarity transformation to reduce its norm; and then reduces the balanced matrix

A^{'}

to upper Hessenberg form

H

, using a unitary similarity transformation:

A^{'} = {Q H Q}^{H}

. The function uses the Hessenberg

Q R

algorithm to compute all the eigenvalues of

H

, which are the same as the eigenvalues of

A

. It computes the eigenvectors of

H

which correspond to the selected eigenvalues, using inverse iteration. It premultiplies the eigenvectors by

Q

to form the eigenvectors of

A^{'}

; and finally transforms the eigenvectors to those of the original matrix

A

Each eigenvector

x

is normalized so that

{‖ x ‖}_{2} = 1

, and the element of largest absolute value is real and positive.

The inverse iteration function may make a small perturbation to the real parts of close eigenvalues, and this may shift their moduli just outside the specified bounds. If you are relying on eigenvalues being within the bounds, you should test them on return from f02gcc.

The time taken by the function is approximately proportional to

n^{3}

The function can be used to compute all eigenvalues and eigenvectors, by setting wl large and negative, and wu large and positive.

10 Example

To compute those eigenvalues of the matrix

A

whose moduli lie in the range

[- 5.5, + 5.5]

, and their corresponding eigenvectors, where

A = (\begin{matrix} - 3.97 - 5.04 i & - 4.11 + 3.70 i & - 0.34 + 1.01 i & 1.29 - 0.86 i \\ 0.34 - 1.50 i & 1.52 - 0.43 i & 1.88 - 5.38 i & 3.36 + 0.65 i \\ 3.31 - 3.85 i & 2.50 + 3.45 i & 0.88 - 1.08 i & 0.64 - 1.48 i \\ - 1.10 + 0.82 i & 1.81 - 1.59 i & 3.25 + 1.33 i & 1.57 - 3.44 i \end{matrix})

f02gc: FL CL CPP AD PY MB

NAG CL Interfacef02gcc (complex_​gen_​eigsys)

▸▿ 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 CL Interface
f02gcc (complex_gen_eigsys)