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porting arapck code to octave
|
From: |
David Bateman |
|
Subject: |
porting arapck code to octave |
|
Date: |
Sat, 20 Dec 2008 00:27:26 +0100 |
|
User-agent: |
Mozilla-Thunderbird 2.0.0.17 (X11/20081018) |
I've been looking at porting the eigs function to octave and basically
have it finished.. However I have a bizarre issue in that
eigs (diag(1:100),4,'LM')
doesn't work due to an internal error in an ARPACK function.. I'd
remember something like this and that I'd patch arpack to avoid it on my
machine in the past. However, I thought I'd investigate the issue
further and modified an ARPACK example function "dssimp" to treat the
exact same case as above and no bug manifested..
I then tried reimplementing dssimp as an oct-file, trying to keep it
close to the same functionality as the fortran code (ie arrays
initialized to zero, etc), and the result was I saw the same bug in the
DSAUPD function as previously. I've been staring at this code for hours
and don't see what the issue is. I attach my modified dssimp.f and the
code that duplicates it in octave with "dssimp(diag(1:100))"... Anyone
want to confirm that
gfortran -o dssimp dssimp.f -larpack -lblas -llapack
./dssimp
works and gives the 4 largest eigenvalues [100, 99, 98, 97] as expected.
whereas
mkoctfile dssimp.cc -larpack
octave --eval "dssimp(diag(1:100))"
doesn't... Anyone want to look at the code and see if they can see an
issue that I missed?
Regards
David
--
David Bateman address@hidden
35 rue Gambetta +33 1 46 04 02 18 (Home)
92100 Boulogne-Billancourt FRANCE +33 6 72 01 06 33 (Mob)
#include <octave/oct.h>
#include "f77-fcn.h"
extern "C"
{
F77_RET_T
F77_FUNC (dsaupd, DSAUPD) (octave_idx_type&, F77_CONST_CHAR_ARG_DECL,
const octave_idx_type&, F77_CONST_CHAR_ARG_DECL,
const octave_idx_type&, const double&,
double*, const octave_idx_type&, double*,
const octave_idx_type&, octave_idx_type*,
octave_idx_type*, double*, double*,
const octave_idx_type&, octave_idx_type&
F77_CHAR_ARG_LEN_DECL F77_CHAR_ARG_LEN_DECL);
F77_RET_T
F77_FUNC (dseupd, DSEUPD) (const int&, F77_CONST_CHAR_ARG_DECL,
int*, double*, double*,
const octave_idx_type&, const double&,
F77_CONST_CHAR_ARG_DECL, const octave_idx_type&,
F77_CONST_CHAR_ARG_DECL, const octave_idx_type&,
const double&, double*, const octave_idx_type&,
double*, const octave_idx_type&, octave_idx_type*,
octave_idx_type*, double*, double*,
const octave_idx_type&, octave_idx_type&
F77_CHAR_ARG_LEN_DECL F77_CHAR_ARG_LEN_DECL
F77_CHAR_ARG_LEN_DECL);
F77_RET_T
F77_FUNC (dgemv, DGEMV) (F77_CONST_CHAR_ARG_DECL,
const octave_idx_type&, const octave_idx_type&,
const double&,
const double*, const octave_idx_type&, const double*,
const octave_idx_type&, const double&, double*,
const octave_idx_type&
F77_CHAR_ARG_LEN_DECL);
}
#define maxn 256
#define maxnev 10
#define maxncv 25
#define ldv maxn
static bool
vector_product (const Matrix& m, const double *x, double *y)
{
octave_idx_type nr = m.rows ();
octave_idx_type nc = m.cols ();
F77_XFCN (dgemv, DGEMV, (F77_CONST_CHAR_ARG2 ("N", 1),
nr, nc, 1.0, m.data (), nr,
x, 1, 0.0, y, 1
F77_CHAR_ARG_LEN (1)));
if (f77_exception_encountered)
{
(*current_liboctave_error_handler)
("dssimp: unrecoverable error in dgemv");
return false;
}
else
return true;
}
DEFUN_DLD (dssimp, args, nargout, "no help")
{
int nargin = args.length ();
octave_value_list retval;
if (nargin != 1)
print_usage ();
else
{
Matrix m = args(0).matrix_value ();
if (!error_state)
{
#define MY_LOCAL_BUFFER(T, V, N) \
OCTAVE_LOCAL_BUFFER (T, V, N); \
for (octave_idx_type iii = 0; iii < N; iii++) \
V[iii] = 0.;
MY_LOCAL_BUFFER (double, v, ldv*maxncv);
MY_LOCAL_BUFFER (double, workl, maxncv * (maxncv+8));
MY_LOCAL_BUFFER (double, workd, 3 * maxn);
MY_LOCAL_BUFFER (double, d, maxncv*2);
MY_LOCAL_BUFFER (double, resid, maxn);
MY_LOCAL_BUFFER (double, ax, maxn);
MY_LOCAL_BUFFER (int, select, maxncv);
MY_LOCAL_BUFFER (octave_idx_type, iparam, 11);
MY_LOCAL_BUFFER (octave_idx_type, ipntr, 11);
for (int i = 0; i < 11; i++)
{
iparam[i] = 0;
ipntr[i] = 0;
}
iparam[0] = 1;
iparam[2] = 300;
iparam[6] = 1;
octave_idx_type nev = 4;
octave_idx_type ncv = 20;
octave_idx_type ido = 0;
octave_idx_type lworkl = ncv * ( ncv + 8);
char bmat = 'I';
std::string which = "LM";
double tol = 0.;
octave_idx_type info = 0;
octave_idx_type n = m.rows();
octave_idx_type lldv = ldv;
while (true)
{
F77_FUNC (dsaupd, DSAUPD)
(ido, F77_CONST_CHAR_ARG2 (&bmat, 1), n,
F77_CONST_CHAR_ARG2 ((which.c_str ()), 2),
nev, tol, resid, ncv, v, lldv, iparam,
ipntr, workd, workl, lworkl, info
F77_CHAR_ARG_LEN(1) F77_CHAR_ARG_LEN(2));
if (f77_exception_encountered)
{
(*current_liboctave_error_handler)
("dssimp: unrecoverable exception encountered in dsaupd");
return retval;
}
if (ido == -1 || ido == 1)
{
#if 0
if (!vector_product (m, workd + ipntr[0] - 1,
workd + ipntr[1] - 1))
break;
#else
// v = diag(1:100) * w
for (octave_idx_type i = 0; i < n; i++)
workd[i+ipntr[1]-1] = static_cast<double>(i) *
workd[i+ipntr[0]-1];
#endif
}
else
break;
}
if (info < 0)
error ("dssimp: error %d in dsaupd", info);
else
{
int rvec = 1;
Matrix eig_vec (n, nev);
double *z = eig_vec.fortran_vec ();
ColumnVector eig_val (nev);
double *d = eig_val.fortran_vec ();
Array<int> s (ncv);
int *sel = s.fortran_vec ();
double sigma;
F77_FUNC (dseupd, DSEUPD)
(rvec, F77_CONST_CHAR_ARG2 ("A", 1), sel, d, z, n, sigma,
F77_CONST_CHAR_ARG2 (&bmat, 1), n,
F77_CONST_CHAR_ARG2 ((which.c_str()), 2),
nev, tol, resid, ncv, v, n, iparam, ipntr, workd, workl,
lworkl, info
F77_CHAR_ARG_LEN(1) F77_CHAR_ARG_LEN(1) F77_CHAR_ARG_LEN(2));
if (f77_exception_encountered)
{
(*current_liboctave_error_handler)
("dssimp: unrecoverable exception encountered in dseupd");
return retval;
}
retval(1) = eig_val;
retval(2) = eig_vec;
}
}
}
return retval;
}
program dssimp
c
c This example program is intended to illustrate the
c simplest case of using ARPACK in considerable detail.
c This code may be used to understand basic usage of ARPACK
c and as a template for creating an interface to ARPACK.
c
c This code shows how to use ARPACK to find a few eigenvalues
c (lambda) and corresponding eigenvectors (x) for the standard
c eigenvalue problem:
c
c A*x = lambda*x
c
c where A is an n by n real symmetric matrix.
c
c The main points illustrated here are
c
c 1) How to declare sufficient memory to find NEV
c eigenvalues of largest magnitude. Other options
c are available.
c
c 2) Illustration of the reverse communication interface
c needed to utilize the top level ARPACK routine DSAUPD
c that computes the quantities needed to construct
c the desired eigenvalues and eigenvectors(if requested).
c
c 3) How to extract the desired eigenvalues and eigenvectors
c using the ARPACK routine DSEUPD.
c
c The only thing that must be supplied in order to use this
c routine on your problem is to change the array dimensions
c appropriately, to specify WHICH eigenvalues you want to compute
c and to supply a matrix-vector product
c
c w <- Av
c
c in place of the call to AV( ) below.
c
c Once usage of this routine is understood, you may wish to explore
c the other available options to improve convergence, to solve generalized
c problems, etc. Look at the file ex-sym.doc in DOCUMENTS directory.
c This codes implements
c
c\Example-1
c ... Suppose we want to solve A*x = lambda*x in regular mode,
c where A is derived from the central difference discretization
c of the 2-dimensional Laplacian on the unit square with
c zero Dirichlet boundary condition.
c ... OP = A and B = I.
c ... Assume "call av (n,x,y)" computes y = A*x
c ... Use mode 1 of DSAUPD.
c
c\BeginLib
c
c\Routines called:
c dsaupd ARPACK reverse communication interface routine.
c dseupd ARPACK routine that returns Ritz values and (optionally)
c Ritz vectors.
c dnrm2 Level 1 BLAS that computes the norm of a vector.
c daxpy Level 1 BLAS that computes y <- alpha*x+y.
c
c\Author
c Richard Lehoucq
c Danny Sorensen
c Chao Yang
c Dept. of Computational &
c Applied Mathematics
c Rice University
c Houston, Texas
c
c\SCCS Information: @(#)
c FILE: ssimp.F SID: 2.6 DATE OF SID: 10/17/00 RELEASE: 2
c
c\Remarks
c 1. None
c
c\EndLib
c
c-----------------------------------------------------------------------
c
c %------------------------------------------------------%
c | Storage Declarations: |
c | |
c | The maximum dimensions for all arrays are |
c | set here to accommodate a problem size of |
c | N .le. MAXN |
c | |
c | NEV is the number of eigenvalues requested. |
c | See specifications for ARPACK usage below. |
c | |
c | NCV is the largest number of basis vectors that will |
c | be used in the Implicitly Restarted Arnoldi |
c | Process. Work per major iteration is |
c | proportional to N*NCV*NCV. |
c | |
c | You must set: |
c | |
c | MAXN: Maximum dimension of the A allowed. |
c | MAXNEV: Maximum NEV allowed. |
c | MAXNCV: Maximum NCV allowed. |
c %------------------------------------------------------%
c
integer maxn, maxnev, maxncv, ldv
parameter (maxn=256, maxnev=10, maxncv=25,
$ ldv=maxn )
c
c %--------------%
c | Local Arrays |
c %--------------%
c
Double precision
& v(ldv,maxncv), workl(maxncv*(maxncv+8)),
& workd(3*maxn), d(maxncv,2), resid(maxn),
& ax(maxn)
logical select(maxncv)
integer iparam(11), ipntr(11)
c
c %---------------%
c | Local Scalars |
c %---------------%
c
character bmat*1, which*2
integer ido, n, nev, ncv, lworkl, info, ierr,
& j, nx, ishfts, maxitr, mode1, nconv
logical rvec
Double precision
& tol, sigma
c
c %------------%
c | Parameters |
c %------------%
c
Double precision
& zero
parameter (zero = 0.0D+0)
c
c %-----------------------------%
c | BLAS & LAPACK routines used |
c %-----------------------------%
c
Double precision
& dnrm2
external dnrm2, daxpy
c
c %--------------------%
c | Intrinsic function |
c %--------------------%
c
intrinsic abs
c
c %-----------------------%
c | Executable Statements |
c %-----------------------%
c
c %-------------------------------------------------%
c | The following include statement and assignments |
c | initiate trace output from the internal |
c | actions of ARPACK. See debug.doc in the |
c | DOCUMENTS directory for usage. Initially, the |
c | most useful information will be a breakdown of |
c | time spent in the various stages of computation |
c | given by setting msaupd = 1. |
c %-------------------------------------------------%
c
include 'debug.h'
ndigit = -3
logfil = 6
msgets = 0
msaitr = 0
msapps = 0
msaupd = 1
msaup2 = 0
mseigt = 0
mseupd = 0
c
c %-------------------------------------------------%
c | The following sets dimensions for this problem. |
c %-------------------------------------------------%
c
nx = 10
n = nx*nx
c
c %-----------------------------------------------%
c | |
c | Specifications for ARPACK usage are set |
c | below: |
c | |
c | 1) NEV = 4 asks for 4 eigenvalues to be |
c | computed. |
c | |
c | 2) NCV = 20 sets the length of the Arnoldi |
c | factorization |
c | |
c | 3) This is a standard problem |
c | (indicated by bmat = 'I') |
c | |
c | 4) Ask for the NEV eigenvalues of |
c | largest magnitude |
c | (indicated by which = 'LM') |
c | See documentation in DSAUPD for the |
c | other options SM, LA, SA, LI, SI. |
c | |
c | Note: NEV and NCV must satisfy the following |
c | conditions: |
c | NEV <= MAXNEV |
c | NEV + 1 <= NCV <= MAXNCV |
c %-----------------------------------------------%
c
nev = 4
ncv = 20
bmat = 'I'
which = 'LM'
c
if ( n .gt. maxn ) then
print *, ' ERROR with _SSIMP: N is greater than MAXN '
go to 9000
else if ( nev .gt. maxnev ) then
print *, ' ERROR with _SSIMP: NEV is greater than MAXNEV '
go to 9000
else if ( ncv .gt. maxncv ) then
print *, ' ERROR with _SSIMP: NCV is greater than MAXNCV '
go to 9000
end if
c
c %-----------------------------------------------------%
c | |
c | Specification of stopping rules and initial |
c | conditions before calling DSAUPD |
c | |
c | TOL determines the stopping criterion. |
c | |
c | Expect |
c | abs(lambdaC - lambdaT) < TOL*abs(lambdaC) |
c | computed true |
c | |
c | If TOL .le. 0, then TOL <- macheps |
c | (machine precision) is used. |
c | |
c | IDO is the REVERSE COMMUNICATION parameter |
c | used to specify actions to be taken on return |
c | from DSAUPD. (See usage below.) |
c | |
c | It MUST initially be set to 0 before the first |
c | call to DSAUPD. |
c | |
c | INFO on entry specifies starting vector information |
c | and on return indicates error codes |
c | |
c | Initially, setting INFO=0 indicates that a |
c | random starting vector is requested to |
c | start the ARNOLDI iteration. Setting INFO to |
c | a nonzero value on the initial call is used |
c | if you want to specify your own starting |
c | vector (This vector must be placed in RESID.) |
c | |
c | The work array WORKL is used in DSAUPD as |
c | workspace. Its dimension LWORKL is set as |
c | illustrated below. |
c | |
c %-----------------------------------------------------%
c
lworkl = ncv*(ncv+8)
tol = zero
info = 0
ido = 0
c
c %---------------------------------------------------%
c | Specification of Algorithm Mode: |
c | |
c | This program uses the exact shift strategy |
c | (indicated by setting PARAM(1) = 1). |
c | IPARAM(3) specifies the maximum number of Arnoldi |
c | iterations allowed. Mode 1 of DSAUPD is used |
c | (IPARAM(7) = 1). All these options can be changed |
c | by the user. For details see the documentation in |
c | DSAUPD. |
c %---------------------------------------------------%
c
ishfts = 1
maxitr = 300
mode1 = 1
c
iparam(1) = ishfts
c
iparam(3) = maxitr
c
iparam(7) = mode1
c
c %------------------------------------------------%
c | M A I N L O O P (Reverse communication loop) |
c %------------------------------------------------%
c
10 continue
c
c %---------------------------------------------%
c | Repeatedly call the routine DSAUPD and take |
c | actions indicated by parameter IDO until |
c | either convergence is indicated or maxitr |
c | has been exceeded. |
c %---------------------------------------------%
c
call dsaupd ( ido, bmat, n, which, nev, tol, resid,
& ncv, v, ldv, iparam, ipntr, workd, workl,
& lworkl, info )
c
if (ido .eq. -1 .or. ido .eq. 1) then
c
c %--------------------------------------%
c | Perform matrix vector multiplication |
c | y <--- OP*x |
c | The user should supply his/her own |
c | matrix vector multiplication routine |
c | here that takes workd(ipntr(1)) as |
c | the input, and return the result to |
c | workd(ipntr(2)). |
c %--------------------------------------%
c
call av2 (nx, workd(ipntr(1)), workd(ipntr(2)))
c
c %-----------------------------------------%
c | L O O P B A C K to call DSAUPD again. |
c %-----------------------------------------%
c
go to 10
c
end if
c
c %----------------------------------------%
c | Either we have convergence or there is |
c | an error. |
c %----------------------------------------%
c
if ( info .lt. 0 ) then
c
c %--------------------------%
c | Error message. Check the |
c | documentation in DSAUPD. |
c %--------------------------%
c
print *, ' '
print *, ' Error with _saupd, info = ', info
print *, ' Check documentation in _saupd '
print *, ' '
c
else
c
c %-------------------------------------------%
c | No fatal errors occurred. |
c | Post-Process using DSEUPD. |
c | |
c | Computed eigenvalues may be extracted. |
c | |
c | Eigenvectors may be also computed now if |
c | desired. (indicated by rvec = .true.) |
c | |
c | The routine DSEUPD now called to do this |
c | post processing (Other modes may require |
c | more complicated post processing than |
c | mode1.) |
c | |
c %-------------------------------------------%
c
rvec = .true.
c
call dseupd ( rvec, 'All', select, d, v, ldv, sigma,
& bmat, n, which, nev, tol, resid, ncv, v, ldv,
& iparam, ipntr, workd, workl, lworkl, ierr )
c
c %----------------------------------------------%
c | Eigenvalues are returned in the first column |
c | of the two dimensional array D and the |
c | corresponding eigenvectors are returned in |
c | the first NCONV (=IPARAM(5)) columns of the |
c | two dimensional array V if requested. |
c | Otherwise, an orthogonal basis for the |
c | invariant subspace corresponding to the |
c | eigenvalues in D is returned in V. |
c %----------------------------------------------%
c
if ( ierr .ne. 0) then
c
c %------------------------------------%
c | Error condition: |
c | Check the documentation of DSEUPD. |
c %------------------------------------%
c
print *, ' '
print *, ' Error with _seupd, info = ', ierr
print *, ' Check the documentation of _seupd. '
print *, ' '
c
else
c
nconv = iparam(5)
do 20 j=1, nconv
c
c %---------------------------%
c | Compute the residual norm |
c | |
c | || A*x - lambda*x || |
c | |
c | for the NCONV accurately |
c | computed eigenvalues and |
c | eigenvectors. (iparam(5) |
c | indicates how many are |
c | accurate to the requested |
c | tolerance) |
c %---------------------------%
c
call av2(nx, v(1,j), ax)
call daxpy(n, -d(j,1), v(1,j), 1, ax, 1)
d(j,2) = dnrm2(n, ax, 1)
d(j,2) = d(j,2) / abs(d(j,1))
c
20 continue
c
c %-----------------------------%
c | Display computed residuals. |
c %-----------------------------%
c
call dmout(6, nconv, 2, d, maxncv, -6,
& 'Ritz values and relative residuals')
end if
c
c %-------------------------------------------%
c | Print additional convergence information. |
c %-------------------------------------------%
c
if ( info .eq. 1) then
print *, ' '
print *, ' Maximum number of iterations reached.'
print *, ' '
else if ( info .eq. 3) then
print *, ' '
print *, ' No shifts could be applied during implicit',
& ' Arnoldi update, try increasing NCV.'
print *, ' '
end if
c
print *, ' '
print *, ' _SSIMP '
print *, ' ====== '
print *, ' '
print *, ' Size of the matrix is ', n
print *, ' The number of Ritz values requested is ', nev
print *, ' The number of Arnoldi vectors generated',
& ' (NCV) is ', ncv
print *, ' What portion of the spectrum: ', which
print *, ' The number of converged Ritz values is ',
& nconv
print *, ' The number of Implicit Arnoldi update',
& ' iterations taken is ', iparam(3)
print *, ' The number of OP*x is ', iparam(9)
print *, ' The convergence criterion is ', tol
print *, ' '
c
end if
c
c %---------------------------%
c | Done with program dssimp. |
c %---------------------------%
c
9000 continue
c
end
c
subroutine av2 (nx, v, w)
integer nx, j
double precision v(nx*nx), w(nx*nx)
do 10 j = 1, nx * nx
w(j) = dble(j) * v(j)
10 continue
end
c
c ------------------------------------------------------------------
c matrix vector subroutine
c
c The matrix used is the 2 dimensional discrete Laplacian on unit
c square with zero Dirichlet boundary condition.
c
c Computes w <--- OP*v, where OP is the nx*nx by nx*nx block
c tridiagonal matrix
c
c | T -I |
c |-I T -I |
c OP = | -I T |
c | ... -I|
c | -I T|
c
c The subroutine TV is called to computed y<---T*x.
c
subroutine av (nx, v, w)
integer nx, j, lo, n2
Double precision
& v(nx*nx), w(nx*nx), one, h2
parameter ( one = 1.0D+0 )
c
call tv(nx,v(1),w(1))
call daxpy(nx, -one, v(nx+1), 1, w(1), 1)
c
do 10 j = 2, nx-1
lo = (j-1)*nx
call tv(nx, v(lo+1), w(lo+1))
call daxpy(nx, -one, v(lo-nx+1), 1, w(lo+1), 1)
call daxpy(nx, -one, v(lo+nx+1), 1, w(lo+1), 1)
10 continue
c
lo = (nx-1)*nx
call tv(nx, v(lo+1), w(lo+1))
call daxpy(nx, -one, v(lo-nx+1), 1, w(lo+1), 1)
c
c Scale the vector w by (1/h^2), where h is the mesh size
c
n2 = nx*nx
h2 = one / dble((nx+1)*(nx+1))
call dscal(n2, one/h2, w, 1)
return
end
c
c-------------------------------------------------------------------
subroutine tv (nx, x, y)
c
integer nx, j
Double precision
& x(nx), y(nx), dd, dl, du
c
Double precision
& one, four
parameter (one = 1.0D+0, four = 4.0D+0)
c
c Compute the matrix vector multiplication y<---T*x
c where T is a nx by nx tridiagonal matrix with DD on the
c diagonal, DL on the subdiagonal, and DU on the superdiagonal.
c
c
dd = four
dl = -one
du = -one
c
y(1) = dd*x(1) + du*x(2)
do 10 j = 2,nx-1
y(j) = dl*x(j-1) + dd*x(j) + du*x(j+1)
10 continue
y(nx) = dl*x(nx-1) + dd*x(nx)
return
end
- porting arapck code to octave,
David Bateman <=
- Re: porting arapck code to octave, Jaroslav Hajek, 2008/12/20
- Re: porting arapck code to octave, David Bateman, 2008/12/20
- Re: porting arapck code to octave, David Bateman, 2008/12/22
- Re: porting arapck code to octave, David Bateman, 2008/12/22
- Re: porting arapck code to octave, David Bateman, 2008/12/23
- Re: porting arapck code to octave, Thomas Weber, 2008/12/23