NAG Library Routine Document

f12aqf (complex_proc)

Note: this routine uses optional parameters to define choices in the problem specification. If you wish to use default settings for all of the optional parameters, then the option setting routine f12arf need not be called. If, however, you wish to reset some or all of the settings please refer to Section 11 in f12arf for a detailed description of the specification of the optional parameters.

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NAG Library Manual, Mark 26

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F12 (sparseig) Chapter Contents

F12 (sparseig) Chapter Introduction

▸▿ 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

1

Purpose

f12aqf is a post-processing routine in a suite of routines consisting of f12anf, f12apf, f12aqf, f12arf and f12asf, that must be called following a final exit from f12aqf.

2

Specification

Fortran Interface

Subroutine f12aqf (

nconv, d, z, ldz, sigma, resid, v, ldv, comm, icomm, ifail)

Integer, Intent (In)	::	ldz, ldv
Integer, Intent (Inout)	::	icomm(*), ifail
Integer, Intent (Out)	::	nconv
Complex (Kind=nag_wp), Intent (In)	::	sigma, resid(*)
Complex (Kind=nag_wp), Intent (Inout)	::	d(), z(ldz,), v(ldv,), comm()

C Header Interface

#include <nagmk26.h>

void	f12aqf_ (Integer nconv, Complex d[], Complex z[], const Integer ldz, const Complex sigma, const Complex resid[], Complex v[], const Integer ldv, Complex comm[], Integer icomm[], Integer *ifail)

3

Description

The suite of routines is designed to calculate some of the eigenvalues,

λ

, (and optionally the corresponding eigenvectors,

x

) of a standard eigenvalue problem

A x = λ x

, or of a generalized eigenvalue problem

A x = λ B x

of order

n

, where

n

is large and the coefficient matrices

A

and

B

are sparse, complex and nonsymmetric. The suite can also be used to find selected eigenvalues/eigenvectors of smaller scale dense, complex and nonsymmetric problems.

Following a call to f12apf, f12aqf returns the converged approximations to eigenvalues and (optionally) the corresponding approximate eigenvectors and/or an orthonormal basis for the associated approximate invariant subspace. The eigenvalues (and eigenvectors) are selected from those of a standard or generalized eigenvalue problem defined by complex nonsymmetric matrices. There is negligible additional cost to obtain eigenvectors; an orthonormal basis is always computed, but there is an additional storage cost if both are requested.

f12aqf is based on the routine zneupd from the ARPACK package, which uses the Implicitly Restarted Arnoldi iteration method. The method is described in Lehoucq and Sorensen (1996) and Lehoucq (2001) while its use within the ARPACK software is described in great detail in Lehoucq et al. (1998). An evaluation of software for computing eigenvalues of sparse nonsymmetric matrices is provided in Lehoucq and Scott (1996). This suite of routines offers the same functionality as the ARPACK software for complex nonsymmetric problems, but the interface design is quite different in order to make the option setting clearer and to simplify some of the interfaces.

f12aqf is a post-processing routine that must be called following a successful final exit from f12apf. f12aqf uses data returned from f12apf and options set either by default or explicitly by calling f12arf, to return the converged approximations to selected eigenvalues and (optionally):

–	the corresponding approximate eigenvectors;
–	an orthonormal basis for the associated approximate invariant subspace;
–	both.

4

References

Lehoucq R B (2001) Implicitly restarted Arnoldi methods and subspace iteration SIAM Journal on Matrix Analysis and Applications 23 551–562

Lehoucq R B and Scott J A (1996) An evaluation of software for computing eigenvalues of sparse nonsymmetric matrices Preprint MCS-P547-1195 Argonne National Laboratory

Lehoucq R B and Sorensen D C (1996) Deflation techniques for an implicitly restarted Arnoldi iteration SIAM Journal on Matrix Analysis and Applications 17 789–821

Lehoucq R B, Sorensen D C and Yang C (1998) ARPACK Users' Guide: Solution of Large-scale Eigenvalue Problems with Implicitly Restarted Arnoldi Methods SIAM, Philidelphia

5

Arguments

1: $nconv$ – IntegerOutput

On exit: the number of converged eigenvalues as found by f12arf.

2: $d (*)$ – Complex (Kind=nag_wp) arrayOutput

Note: the dimension of the array d must be at least

ncv

(see f12anf).

On exit: the first nconv locations of the array d contain the converged approximate eigenvalues.

3: $z (ldz *)$ – Complex (Kind=nag_wp) arrayOutput

Note: the second dimension of the array z must be at least

nev

if the default option

Vectors = RITZ

has been selected and at least

1

if the option

Vectors = NONE

SCHUR

has been selected (see f12anf).

On exit: if the default option

Vectors = RITZ

(see f12adf) has been selected then z contains the final set of eigenvectors corresponding to the eigenvalues held in d. The complex eigenvector associated with an eigenvalue is stored in the corresponding column of z.

4: $ldz$ – IntegerInput

On entry: the first dimension of the array z as declared in the (sub)program from which f12aqf is called.

Constraints:

if the default option $Vectors = Ritz$ has been selected, $ldz \geq n$ ;
if the option $Vectors = None or Schur$ has been selected, $ldz \geq 1$ .

5: $sigma$ – Complex (Kind=nag_wp)Input

On entry: if one of the Shifted Inverse (see f12arf) modes has been selected then sigma contains the shift used; otherwise sigma is not referenced.

6: $resid (*)$ – Complex (Kind=nag_wp) arrayInput

Note: the dimension of the array resid must be at least

n

(see f12anf).

On entry: must not be modified following a call to f12apf since it contains data required by f12aqf.

7: $v (ldv *)$ – Complex (Kind=nag_wp) arrayInput/Output

Note: the second dimension of the array v must be at least

\max (1, ncv)

(see f12anf).

On entry: the ncv columns of v contain the Arnoldi basis vectors for

OP

as constructed by f12apf.

On exit: if the option

Vectors = SCHUR

RITZ

has been set and a separate array z has been passed (i.e., z does not equal v), then the first nconv columns of v will contain approximate Schur vectors that span the desired invariant subspace.

8: $ldv$ – IntegerInput

On entry: the first dimension of the array v as declared in the (sub)program from which f12aqf is called.

Constraint:

ldv \geq n

9: $comm (*)$ – Complex (Kind=nag_wp) arrayCommunication Array

Note: the dimension of the array comm must be at least

\max (1, lcomm)

(see f12anf).

On initial entry: must remain unchanged from the prior call to f12anf.

On exit: contains data on the current state of the solution.

10: $icomm (*)$ – Integer arrayCommunication Array

Note: the dimension of the array icomm must be at least

\max (1, licomm)

(see f12anf).

On initial entry: must remain unchanged from the prior call to f12anf.

On exit: contains data on the current state of the solution.

11: $ifail$ – IntegerInput/Output

On entry: ifail must be set to

0

- 1 or 1

. If you are unfamiliar with this argument you should refer to Section 3.4 in How to Use the NAG Library and its Documentation for details.

For environments where it might be inappropriate to halt program execution when an error is detected, the value

- 1 or 1

is recommended. If the output of error messages is undesirable, then the value

1

is recommended. Otherwise, if you are not familiar with this argument, the recommended value is

0

. When the value $- 1 or 1$ is used it is essential to test the value of ifail on exit.

On exit:

ifail = 0

unless the routine detects an error or a warning has been flagged (see Section 6).

6

Error Indicators and Warnings

If on entry

ifail = 0

- 1

, explanatory error messages are output on the current error message unit (as defined by x04aaf).

Errors or warnings detected by the routine:

$ifail = 1$: On entry, $ldz < \max (1, n)$ or $ldz < 1$ when no vectors are required.

$ifail = 2$: On entry, the option $Vectors = Select$ was selected, but this is not yet implemented.

$ifail = 3$: The number of eigenvalues found to sufficient accuracy prior to calling f12aqf, as communicated through the argument icomm, is zero.

$ifail = 4$: The number of converged eigenvalues as calculated by f12apf differ from the value passed to it through the argument icomm.

$ifail = 5$: Unexpected error during calculation of a Schur form: there was a failure to compute all the converged eigenvalues. Please contact NAG.

$ifail = 6$: Unexpected error: the computed Schur form could not be reordered by an internal call. Please contact NAG.

$ifail = 7$: Unexpected error in internal call while calculating eigenvectors. Please contact NAG.

$ifail = 8$: Either the solver routine f12apf has not been called prior to the call of this routine or a communication array has become corrupted.

$ifail = 9$: The routine was unable to dynamically allocate sufficient internal workspace. Please contact NAG.

$ifail = 10$: An unexpected error has occurred. Please contact NAG.

$ifail = - 99$: An unexpected error has been triggered by this routine. Please contact NAG.
See Section 3.9 in How to Use the NAG Library and its Documentation for further information.

$ifail = - 399$: Your licence key may have expired or may not have been installed correctly.
See Section 3.8 in How to Use the NAG Library and its Documentation for further information.

$ifail = - 999$: Dynamic memory allocation failed.
See Section 3.7 in How to Use the NAG Library and its Documentation for further information.

7

Accuracy

The relative accuracy of a Ritz value,

λ

, is considered acceptable if its Ritz estimate

\leq Tolerance \times |λ|

. The default Tolerance used is the machine precision given by x02ajf.

8

Parallelism and Performance

f12aqf 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.

9

Further Comments

None.

10

Example

This example solves

A x = λ B x

in regular-invert mode, where

A

and

B

are derived from the standard central difference discretization of the one-dimensional convection-diffusion operator

\frac{d^{2} u}{d x^{2}} + ρ \frac{d u}{d x}

[0, 1]

, with zero Dirichlet boundary conditions.

NAG Library Routine Document

f12aqf (complex_proc)

▸▿ 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

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