292 lines
9.6 KiB
FortranFixed
292 lines
9.6 KiB
FortranFixed
program ssbdr3
|
|
c
|
|
c ... Construct the matrix A in LAPACK-style band form.
|
|
c The matrix A is the 1-dimensional discrete Laplacian on [0,1]
|
|
c with zero Dirichlet boundary condition, M is the mass
|
|
c formed by using piecewise linear elements on [0,1].
|
|
c
|
|
c ... Call SSBAND with regular mode to find eigenvalues LAMBDA
|
|
c such that
|
|
c A*x = LAMBDA*M*x.
|
|
c
|
|
c ... Use mode 2 of SSAUPD.
|
|
c
|
|
c\BeginLib
|
|
c
|
|
c\Routines called:
|
|
c ssband ARPACK banded eigenproblem solver.
|
|
c slapy2 LAPACK routine to compute sqrt(x**2+y**2) carefully.
|
|
c slaset LAPACK routine to initialize a matrix to zero.
|
|
c saxpy Level 1 BLAS that computes y <- alpha*x+y.
|
|
c snrm2 Level 1 BLAS that computes the norm of a vector.
|
|
c sgbmv Level 2 BLAS that computes the band matrix vector product
|
|
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: sbdr3.F SID: 2.5 DATE OF SID: 08/26/96 RELEASE: 2
|
|
c
|
|
c\Remarks
|
|
c 1. None
|
|
c
|
|
c\EndLib
|
|
c
|
|
c----------------------------------------------------------------------
|
|
c
|
|
c %-------------------------------------%
|
|
c | Define leading dimensions for all |
|
|
c | arrays. |
|
|
c | MAXN - Maximum size of the matrix |
|
|
c | MAXNEV - Maximum number of |
|
|
c | eigenvalues to be computed |
|
|
c | MAXNCV - Maximum number of Arnoldi |
|
|
c | vectors stored |
|
|
c | MAXBDW - Maximum bandwidth |
|
|
c %-------------------------------------%
|
|
c
|
|
integer maxn, maxnev, maxncv, maxbdw, lda,
|
|
& lworkl, ldv
|
|
parameter ( maxn = 1000, maxnev = 25, maxncv=50,
|
|
& maxbdw=50, lda = maxbdw, ldv = maxn )
|
|
c
|
|
c %--------------%
|
|
c | Local Arrays |
|
|
c %--------------%
|
|
c
|
|
integer iparam(11), iwork(maxn)
|
|
logical select(maxncv)
|
|
Real
|
|
& a(lda,maxn), m(lda,maxn), rfac(lda,maxn),
|
|
& workl(maxncv*maxncv+8*maxncv), workd(3*maxn),
|
|
& v(ldv, maxncv), resid(maxn), d(maxncv, 2),
|
|
& ax(maxn), mx(maxn)
|
|
c
|
|
c %---------------%
|
|
c | Local Scalars |
|
|
c %---------------%
|
|
c
|
|
character which*2, bmat
|
|
integer nev, ncv, ku, kl, info, j, ido,
|
|
& n, isub, isup, idiag, maxitr, mode, nconv
|
|
Real
|
|
& tol, h, sigma, r1, r2
|
|
logical rvec
|
|
c
|
|
c %------------%
|
|
c | Parameters |
|
|
c %------------%
|
|
c
|
|
Real
|
|
& one, zero, two, four, six
|
|
parameter (one = 1.0E+0 , zero = 0.0E+0 , two = 2.0E+0 ,
|
|
& four = 4.0E+0 , six = 6.0E+0 )
|
|
c
|
|
c %-----------------------------%
|
|
c | BLAS & LAPACK routines used |
|
|
c %-----------------------------%
|
|
c
|
|
Real
|
|
& slapy2, snrm2
|
|
external slapy2, snrm2, saxpy, sgbmv
|
|
c
|
|
c %--------------------%
|
|
c | Intrinsic function |
|
|
c %--------------------%
|
|
c
|
|
intrinsic abs
|
|
c
|
|
c %-----------------------%
|
|
c | Executable Statements |
|
|
c %-----------------------%
|
|
c
|
|
c %-------------------------------------------------%
|
|
c | The number N is the dimension of the matrix. A |
|
|
c | generalized eigenvalue problem is solved |
|
|
c | (BMAT = 'G'). NEV is the number of eigenvalues |
|
|
c | to be approximated. The user can modify N, NEV, |
|
|
c | NCV and WHICH to solve problems of different |
|
|
c | sizes, and to get different parts the spectrum. |
|
|
c | However, the following conditions must be |
|
|
c | satisfied: |
|
|
c | N <= MAXN |
|
|
c | NEV <= MAXNEV |
|
|
c | NEV + 1 <= NCV <= MAXNCV |
|
|
c %-------------------------------------------------%
|
|
c
|
|
n = 100
|
|
nev = 4
|
|
ncv = 10
|
|
if ( n .gt. maxn ) then
|
|
print *, ' ERROR with _SBDR3: N is greater than MAXN '
|
|
go to 9000
|
|
else if ( nev .gt. maxnev ) then
|
|
print *, ' ERROR with _SBDR3: NEV is greater than MAXNEV '
|
|
go to 9000
|
|
else if ( ncv .gt. maxncv ) then
|
|
print *, ' ERROR with _SBDR3: NCV is greater than MAXNCV '
|
|
go to 9000
|
|
end if
|
|
bmat = 'G'
|
|
which = 'LM'
|
|
c
|
|
c %-----------------------------------------------------%
|
|
c | The work array WORKL is used in SSAUPD as |
|
|
c | workspace. Its dimension LWORKL is set as |
|
|
c | illustrated below. The parameter TOL determines |
|
|
c | the stopping criterion. If TOL<=0, machine |
|
|
c | precision is used. The variable IDO is used for |
|
|
c | reverse communication, and is initially set to 0. |
|
|
c | Setting INFO=0 indicates that a random vector is |
|
|
c | generated in SSAUPD to start the Arnoldi iteration. |
|
|
c %-----------------------------------------------------%
|
|
c
|
|
lworkl = ncv**2+8*ncv
|
|
tol = zero
|
|
ido = 0
|
|
info = 0
|
|
c
|
|
c %---------------------------------------------------%
|
|
c | IPARAM(3) specifies the maximum number of Arnoldi |
|
|
c | iterations allowed. Mode 2 of SSAUPD is used |
|
|
c | (IPARAM(7) = 2). All these options can be changed |
|
|
c | by the user. For details see the documentation in |
|
|
c | SSBAND. |
|
|
c %---------------------------------------------------%
|
|
c
|
|
maxitr = 300
|
|
mode = 2
|
|
c
|
|
iparam(3) = maxitr
|
|
iparam(7) = mode
|
|
c
|
|
c %----------------------------------------%
|
|
c | Construct the matrix A in LAPACK-style |
|
|
c | banded form. |
|
|
c %----------------------------------------%
|
|
c
|
|
c %---------------------------------------------%
|
|
c | Zero out the workspace for banded matrices. |
|
|
c %---------------------------------------------%
|
|
c
|
|
call slaset('A', lda, n, zero, zero, a, lda)
|
|
call slaset('A', lda, n, zero, zero, m, lda)
|
|
call slaset('A', lda, n, zero, zero, rfac, lda)
|
|
c
|
|
c %-------------------------------------%
|
|
c | KU, KL are number of superdiagonals |
|
|
c | and subdiagonals within the band of |
|
|
c | matrices A and M. |
|
|
c %-------------------------------------%
|
|
c
|
|
kl = 1
|
|
ku = 1
|
|
c
|
|
c %---------------%
|
|
c | Main diagonal |
|
|
c %---------------%
|
|
c
|
|
h = one / real (n+1)
|
|
r1 = four / six
|
|
idiag = kl+ku+1
|
|
do 30 j = 1, n
|
|
a(idiag,j) = two / h
|
|
m(idiag,j) = r1 * h
|
|
30 continue
|
|
c
|
|
c %-------------------------------------%
|
|
c | First subdiagonal and superdiagonal |
|
|
c %-------------------------------------%
|
|
c
|
|
r2 = one / six
|
|
isup = kl+ku
|
|
isub = kl+ku+2
|
|
do 60 j = 1, n-1
|
|
a(isup,j+1) = -one / h
|
|
a(isub,j) = -one / h
|
|
m(isup,j+1) = r2 * h
|
|
m(isub,j) = r2 * h
|
|
60 continue
|
|
c
|
|
c %-------------------------------------%
|
|
c | Call SSBAND to find eigenvalues and |
|
|
c | eigenvectors. Eigenvalues are |
|
|
c | returned in the first column of D. |
|
|
c | Eigenvectors are returned in the |
|
|
c | first NCONV (=IPARAM(5)) columns of |
|
|
c | V. |
|
|
c %-------------------------------------%
|
|
c
|
|
rvec = .true.
|
|
call ssband( rvec, 'A', select, d, v, ldv, sigma, n, a, m, lda,
|
|
& rfac, kl, ku, which, bmat, nev, tol,
|
|
& resid, ncv, v, ldv, iparam, workd, workl, lworkl,
|
|
& iwork, info)
|
|
c
|
|
if ( info .eq. 0) then
|
|
c
|
|
nconv = iparam(5)
|
|
c
|
|
c %-----------------------------------%
|
|
c | Print out convergence information |
|
|
c %-----------------------------------%
|
|
c
|
|
print *, ' '
|
|
print *, ' _SBDR3 '
|
|
print *, ' ====== '
|
|
print *, ' '
|
|
print *, ' The size of the matrix is ', n
|
|
print *, ' Number of eigenvalue requested is ', nev
|
|
print *, ' The number of Lanczos vectors generated',
|
|
& ' (NCV) is ', ncv
|
|
print *, ' The number of converged Ritz values is ',
|
|
& nconv
|
|
print *, ' What portion of the spectrum ', which
|
|
print *, ' The number of Implicit Arnoldi',
|
|
& ' update taken is ', iparam(3)
|
|
print *, ' The number of OP*x is ', iparam(9)
|
|
print *, ' The convergence tolerance is ', tol
|
|
print *, ' '
|
|
c
|
|
c %----------------------------%
|
|
c | Compute the residual norm. |
|
|
c | || A*x - lambda*x || |
|
|
c %----------------------------%
|
|
c
|
|
do 90 j = 1, nconv
|
|
call sgbmv('Notranspose', n, n, kl, ku, one,
|
|
& a(kl+1,1), lda, v(1,j), 1, zero,
|
|
& ax, 1)
|
|
call sgbmv('Notranspose', n, n, kl, ku, one,
|
|
& m(kl+1,1), lda, v(1,j), 1, zero,
|
|
& mx, 1)
|
|
call saxpy(n, -d(j,1), mx, 1, ax, 1)
|
|
d(j,2) = snrm2(n, ax, 1)
|
|
d(j,2) = d(j,2) / abs(d(j,1))
|
|
c
|
|
90 continue
|
|
|
|
call smout(6, nconv, 2, d, maxncv, -6,
|
|
& 'Ritz values and relative residuals')
|
|
else
|
|
c
|
|
c %-------------------------------------%
|
|
c | Either convergence failed, or there |
|
|
c | is error. Check the documentation |
|
|
c | for SSBAND. |
|
|
c %-------------------------------------%
|
|
c
|
|
print *, ' '
|
|
print *, ' Error with _sband, info= ', info
|
|
print *, ' Check the documentation of _sband '
|
|
print *, ' '
|
|
c
|
|
end if
|
|
c
|
|
9000 end
|