nag_sparseig_real_iter (f12ab) is an iterative solver used to find some of the eigenvalues (and optionally the corresponding eigenvectors) of a standard or generalized eigenvalue problem defined by real nonsymmetric matrices. This is part of a suite of functions that also includes nag_sparseig_real_init (f12aa), nag_sparseig_real_proc (f12ac), nag_sparseig_real_option (f12ad) and nag_sparseig_real_monit (f12ae). It is

Syntax

[irevcm, resid, v, x, mx, nshift, comm, icomm, ifail] = f12ab(irevcm, resid, v, x, mx, comm, icomm)

[irevcm, resid, v, x, mx, nshift, comm, icomm, ifail] = nag_sparseig_real_iter(irevcm, resid, v, x, mx, comm, icomm)

Description

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

nag_sparseig_real_iter (f12ab) is a reverse communication function, based on the ARPACK routine dnaupd, using 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 functions offers the same functionality as the ARPACK software for real nonsymmetric problems, but the interface design is quite different in order to make the option setting clearer and to simplify the interface of nag_sparseig_real_iter (f12ab).

The setup function nag_sparseig_real_init (f12aa) must be called before nag_sparseig_real_iter (f12ab), the reverse communication iterative solver. Options may be set for nag_sparseig_real_iter (f12ab) by prior calls to the option setting function nag_sparseig_real_option (f12ad) and a post-processing function nag_sparseig_real_proc (f12ac) must be called following a successful final exit from nag_sparseig_real_iter (f12ab). nag_sparseig_real_monit (f12ae), may be called following certain flagged, intermediate exits from nag_sparseig_real_iter (f12ab) to provide additional monitoring information about the computation.

nag_sparseig_real_iter (f12ab) uses reverse communication, i.e., it returns repeatedly to the calling program with the argument irevcm (see Arguments) set to specified values which require the calling program to carry out one of the following tasks:

– compute the matrix-vector product $y = OP x$ , where $OP$ is defined by the computational mode;
– compute the matrix-vector product $y = B x$ ;
– notify the completion of the computation;
– allow the calling program to monitor the solution.

The problem type to be solved (standard or generalized), the spectrum of eigenvalues of interest, the mode used (regular, regular inverse, shifted inverse, shifted real or shifted imaginary) and other options can all be set using the option setting function nag_sparseig_real_option (f12ad) (see Description of the s in nag_sparseig_real_option (f12ad) for details on setting options and of the default settings).

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

Parameters

Note: this function uses reverse communication. Its use involves an initial entry, intermediate exits and re-entries, and a final exit, as indicated by the argument irevcm. Between intermediate exits and re-entries, all arguments other than x, mx and comm must remain unchanged.

Compulsory Input Parameters

1: $irevcm$ – int64int32nag_int scalar: On initial entry: $irevcm = 0$ , otherwise an error condition will be raised.

On intermediate re-entry: must be unchanged from its previous exit value. Changing irevcm to any other value between calls will result in an error.

Constraint: on initial entry, $irevcm = 0$ ; on re-entry irevcm must remain unchanged.
2: $resid (:)$ – double array: The dimension of the array resid must be at least $n$ (see nag_sparseig_real_init (f12aa))

On initial entry: need not be set unless the option Initial Residual has been set in a prior call to nag_sparseig_real_option (f12ad) in which case resid should contain an initial residual vector, possibly from a previous run.

On intermediate re-entry: must be unchanged from its previous exit. Changing resid to any other value between calls may result in an error exit.
3: $v (ldv :)$ – double array: The first dimension of the array v must be at least $n$ .
The second dimension of the array v must be at least $\max (1, ncv)$ .

On initial entry: need not be set.

On intermediate re-entry: must be unchanged from its previous exit.
4: $x (:)$ – double array: The dimension of the array x must be at least $n$ (see nag_sparseig_real_init (f12aa))

On initial entry: need not be set, it is used as a convenient mechanism for accessing elements of comm.

On intermediate re-entry: if $Pointers = YES$ , x need not be set.
If $Pointers = NO$ , x must contain the result of $y = OP x$ when irevcm returns the value $- 1$ or $+ 1$ . It must return the real parts of the computed shifts when irevcm returns the value $3$ .
5: $mx (:)$ – double array: The dimension of the array mx must be at least $n$ (see nag_sparseig_real_init (f12aa))

On initial entry: need not be set, it is used as a convenient mechanism for accessing elements of comm.

On intermediate re-entry: if $Pointers = YES$ , mx need not be set.
If $Pointers = NO$ , mx must contain the result of $y = B x$ when irevcm returns the value $2$ . It must return the imaginary parts of the computed shifts when irevcm returns the value $3$ .
6: $comm (:)$ – double array: The dimension of the array comm must be at least $\max (1, lcomm)$ (see nag_sparseig_real_init (f12aa))

On initial entry: must remain unchanged following a call to the setup function nag_sparseig_real_init (f12aa).
7: $icomm (:)$ – int64int32nag_int array: The dimension of the array icomm must be at least $\max (1, licomm)$ (see nag_sparseig_real_init (f12aa))

On initial entry: must remain unchanged following a call to the setup function nag_sparseig_real_init (f12aa).

Optional Input Parameters

None.

Output Parameters

1: $irevcm$ – int64int32nag_int scalar

On intermediate exit: has the following meanings.

$irevcm = - 1$: The calling program must compute the matrix-vector product $y = OP x$ , where $x$ is stored in x (by default) or in the array comm (starting from the location given by the first element of icomm) when the option $Pointers = YES$ is set in a prior call to nag_sparseig_real_option (f12ad). The result $y$ is returned in x (by default) or in the array comm (starting from the location given by the second element of icomm) when the option $Pointers = YES$ is set. If $B$ is not symmetric semidefinite then the precomputed values in mx should not be used (see the explanation under $irevcm = 2$ ).
$irevcm = 1$: The calling program must compute the matrix-vector product $y = OP x$ . This is similar to the case $irevcm = - 1$ except that the result of the matrix-vector product $B x$ (as required in some computational modes) has already been computed and is available in mx (by default) or in the array comm (starting from the location given by the third element of icomm) when the option $Pointers = YES$ is set.
$irevcm = 2$: The calling program must compute the matrix-vector product $y = B x$ , where $x$ is stored as described in the case $irevcm = - 1$ and $y$ is returned in the location described by the case $irevcm = 1$ . This computation is requested when solving the Generalized problem using either Shifted Inverse Imaginary or Shifted Inverse Real; in these cases $B$ is used as an inner-product space and requires that $B$ be symmetric semidefinite. If neither $A$ nor $B$ is symmetric semidefinite then the problem should be reformulated in a Standard form.
$irevcm = 3$: Compute the nshift real and imaginary parts of the shifts where the real parts are to be returned in the first nshift locations of the array x and the imaginary parts are to be returned in the first nshift locations of the array mx. Only complex conjugate pairs of shifts may be applied and the pairs must be placed in consecutive locations. This value of irevcm will only arise if the optional parameter Supplied Shifts is set in a prior call to nag_sparseig_real_option (f12ad) which is intended for experienced users only; the default and recommended option is to use exact shifts (see Lehoucq et al. (1998) for details).
$irevcm = 4$: Monitoring step: a call to nag_sparseig_real_monit (f12ae) can now be made to return the number of Arnoldi iterations, the number of converged Ritz values, their real and imaginary parts, and the corresponding Ritz estimates.

On final exit:

irevcm = 5

: nag_sparseig_real_iter (f12ab) has completed its tasks. The value of ifail determines whether the iteration has been successfully completed, or whether errors have been detected. On successful completion nag_sparseig_real_proc (f12ac) must be called to return the requested eigenvalues and eigenvectors (and/or Schur vectors).

2: $resid (:)$ – double array

The dimension of the array resid will be

n

(see nag_sparseig_real_init (f12aa))

On intermediate exit: contains the current residual vector.

On final exit: contains the final residual vector.

3: $v (ldv :)$ – double array

The first dimension of the array v will be

n

The second dimension of the array v will be

\max (1, ncv)

On intermediate exit: contains the current set of Arnoldi basis vectors.

On final exit: contains the final set of Arnoldi basis vectors.

4: $x (:)$ – double array

The dimension of the array x will be

n

(see nag_sparseig_real_init (f12aa))

On intermediate exit: if

Pointers = YES

, x is not referenced.

Pointers = NO

, x contains the vector

x

when irevcm returns the value

- 1

+ 1

On final exit: does not contain useful data.

5: $mx (:)$ – double array

The dimension of the array mx will be

n

(see nag_sparseig_real_init (f12aa))

On intermediate exit: if

Pointers = YES

, mx is not referenced.

Pointers = NO

, mx contains the vector

B x

when irevcm returns the value

+ 1

On final exit: does not contain any useful data.

6: $nshift$ – int64int32nag_int scalar

On intermediate exit: if the option Supplied Shifts is set and irevcm returns a value of

3

, nshift returns the number of complex shifts required.

7: $comm (:)$ – double array

The dimension of the array comm will be

\max (1, lcomm)

(see nag_sparseig_real_init (f12aa))

Contains data defining the current state of the iterative process.

8: $icomm (:)$ – int64int32nag_int array

The dimension of the array icomm will be

\max (1, licomm)

(see nag_sparseig_real_init (f12aa))

Contains data defining the current state of the iterative process.

9: $ifail$ – int64int32nag_int scalar

On final exit:

ifail = 0

unless the function detects an error (see Error Indicators and Warnings).

Error Indicators and Warnings

Errors or warnings detected by the function:

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

$ifail = 1$: On initial entry, the maximum number of iterations $\leq 0$ , the option Iteration Limit has been set to a non-positive value.

$ifail = 2$: The options Generalized and Regular are incompatible.

$ifail = 3$: The option Initial Residual was selected but the starting vector held in resid is zero.

W $ifail = 4$: The maximum number of iterations has been reached. Some Ritz values may have converged; a subsequent call to nag_sparseig_real_proc (f12ac) will return the number of converged values and the converged values.

$ifail = 5$: No shifts could be applied during a cycle of the implicitly restarted Arnoldi iteration. One possibility is to increase the size of ncv relative to nev (see Arguments in nag_sparseig_real_init (f12aa) for details of these arguments).

$ifail = 6$: Could not build an Arnoldi factorization. Consider changing ncv or nev in the initialization function (see Arguments in nag_sparseig_real_init (f12aa) for details of these arguments).

$ifail = 7$: Unexpected error in internal call to compute eigenvalues and corresponding error bounds of the current upper Hessenberg matrix. Please contact NAG.

$ifail = 8$: Either the initialization function nag_sparseig_real_init (f12aa) has not been called prior to the first call of this function or a communication array has become corrupted.

$ifail = 9$: An unexpected error has occurred. Please contact NAG.

$ifail = - 99$: An unexpected error has been triggered by this routine. Please contact NAG.

$ifail = - 399$: Your licence key may have expired or may not have been installed correctly.

$ifail = - 999$: Dynamic memory allocation failed.

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 nag_machine_precision (x02aj).

Further Comments

None.

Example

This example solves

A x = λ x

in shift-invert mode, where

A

is obtained from the standard central difference discretization of the convection-diffusion operator

\frac{\partial^{2} u}{\partial x^{2}} + \frac{\partial^{2} u}{\partial y^{2}} + ρ \frac{\partial u}{\partial x}

on the unit square, with zero Dirichlet boundary conditions. The shift used is a real number.

Open in the MATLAB editor: f12ab_example

function f12ab_example


fprintf('f12ab example results\n\n');

% Use existing sparse matrix to generate symmetric one.
load('west0479.mat')
S = west0479;

% Extract details of sparse matrix S and form shifted inverse.
[irow,icol,a] = find(S);
n   = int64(size(S,1));
sigma = 100;
C = S - sigma*eye(n);
C = inv(C);

% Find 10 eigenvalues closest to sigma
nev = int64(10);
ncv = int64(30);
irevcm = int64(0);
resid = zeros(n,1);
v = zeros(n, ncv);
x = zeros(n, 1);
mx = zeros(n);

% Setup reverse communication solver using shifted inverse mode
[icomm, comm, ifail] = f12aa( ...
                              n, nev, ncv);
[icomm, comm, ifail] = f12ad( ...
                              'Shifted Inverse Real', icomm, comm);

while (irevcm ~= 5)
  [irevcm, resid, v, x, mx, nshift, comm, icomm, ifail] = ...
    f12ab( ...
           irevcm, resid, v, x, mx, comm, icomm);
  if (irevcm == -1 || irevcm == 1)
    x = C*x;
  elseif (irevcm == 4)
    [niter, nconv, ritzr, ritzi, rzest] = f12ae(icomm, comm);
    if (niter == 1)
      fprintf('\n');
    end
    fprintf('Iteration %2d No. converged = %d Norm of estimates = %10.2e\n', ...
            niter, nconv, norm(rzest));
  end
end

[nconv, dr, di, z, v, comm, icomm, ifail] = ...
  f12ac( ...
         sigma, 0, resid, v, comm, icomm);

fprintf('\nThe %4d Ritz values closest to %8.2f are:\n\n', size(dr,1), sigma);
fprintf('%9.3f %+9.3fi\n', [dr di]');

f12ab example results


Iteration  1 No. converged = 1 Norm of estimates =   3.31e-05
Iteration  2 No. converged = 5 Norm of estimates =   3.04e-05
Iteration  3 No. converged = 7 Norm of estimates =   2.07e-04
Iteration  4 No. converged = 7 Norm of estimates =   2.22e-04
Iteration  5 No. converged = 8 Norm of estimates =   2.43e-04

The   10 Ritz values closest to   100.00 are:

   74.635    +0.000i
  108.125   -54.066i
  108.125   +54.066i
   59.789   -43.689i
   59.789   +43.689i
   43.062   -39.164i
   43.062   +39.164i
   33.707   -17.557i
   33.707   +17.557i
   35.662    +0.000i

PDF version (NAG web site, 64-bit version, 64-bit version)

Chapter Contents

Chapter Introduction

NAG Toolbox