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|
program dsdrv4
c
c Program to illustrate the idea of reverse communication
c in shift and invert mode for a generalized symmetric eigenvalue
c problem. The following program uses the two LAPACK subroutines
c dgttrf.f and dgttrs to factor and solve a tridiagonal system of
c equations.
c
c We implement example four of ex-sym.doc in DOCUMENTS directory
c
c\Example-4
c ... Suppose we want to solve A*x = lambda*M*x in inverse mode,
c where A and M are obtained from the finite element discretrization
c of the 1-dimensional discrete Laplacian
c d^2u / dx^2
c on the interval [0,1] with zero Dirichlet boundary condition
c using piecewise linear elements.
c
c ... OP = (inv[A - sigma*M])*M and B = M.
c
c ... Use mode 3 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 dgttrf LAPACK tridiagonal factorization routine.
c dgttrs LAPACK tridiagonal solve routine.
c daxpy Level 1 BLAS that computes y <- alpha*x+y.
c dcopy Level 1 BLAS that copies one vector to another.
c dscal Level 1 BLAS that scales a vector by a scalar.
c dnrm2 Level 1 BLAS that computes the norm of a vector.
c av Matrix vector multiplication routine that computes A*x.
c mv Matrix vector multiplication routine that computes M*x.
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: sdrv4.F SID: 2.5 DATE OF SID: 10/17/00 RELEASE: 2
c
c\Remarks
c 1. None
c
c\EndLib
c----------------------------------------------------------------------
c
c %-----------------------------%
c | Define leading dimensions |
c | for all arrays. |
c | MAXN: Maximum dimension |
c | 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),
& ad(maxn), adl(maxn), adu(maxn), adu2(maxn)
logical select(maxncv)
integer iparam(11), ipntr(11), ipiv(maxn)
c
c %---------------%
c | Local Scalars |
c %---------------%
c
character bmat*1, which*2
integer ido, n, nev, ncv, lworkl, info, j, ierr,
& nconv, maxitr, ishfts, mode
logical rvec
Double precision
& sigma, r1, r2, tol, h
c
c %------------%
c | Parameters |
c %------------%
c
Double precision
& zero, one, two, four, six
parameter (zero = 0.0D+0, one = 1.0D+0,
& four = 4.0D+0, six = 6.0D+0,
& two = 2.0D+0 )
c
c %-----------------------------%
c | BLAS & LAPACK routines used |
c %-----------------------------%
c
Double precision
& dnrm2
external daxpy, dcopy, dscal, dnrm2, dgttrf, dgttrs
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 (BMAT = |
c | 'G'.) NEV is the number of eigenvalues (closest to |
c | the shift SIGMA) to be approximated. Since the |
c | shift-invert mode is used, WHICH is set to 'LM'. |
c | The user can modify NEV, NCV, SIGMA to solve |
c | problems of different sizes, and to get different |
c | parts of the spectrum. However, The following |
c | conditions must be 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 _SDRV4: N is greater than MAXN '
go to 9000
else if ( nev .gt. maxnev ) then
print *, ' ERROR with _SDRV4: NEV is greater than MAXNEV '
go to 9000
else if ( ncv .gt. maxncv ) then
print *, ' ERROR with _SDRV4: NCV is greater than MAXNCV '
go to 9000
end if
bmat = 'G'
which = 'LM'
sigma = zero
c
c %--------------------------------------------------%
c | The work array WORKL is used in DSAUPD 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 DSAUPD to start the Arnoldi |
c | iteration. |
c %--------------------------------------------------%
c
lworkl = ncv*(ncv+8)
tol = zero
ido = 0
info = 0
c
c %---------------------------------------------------%
c | This program uses exact shifts with respect to |
c | the current Hessenberg matrix (IPARAM(1) = 1). |
c | IPARAM(3) specifies the maximum number of Arnoldi |
c | iterations allowed. Mode 3 specified in the |
c | documentation of DSAUPD is used (IPARAM(7) = 3). |
c | All these options may be changed by the user. |
c | For details, see the documentation in DSAUPD. |
c %---------------------------------------------------%
c
ishfts = 1
maxitr = 300
mode = 3
c
iparam(1) = ishfts
iparam(3) = maxitr
iparam(7) = mode
c
c %-------------------------------------------------------%
c | Call LAPACK routine to factor the tridiagonal matrix |
c | (A-SIGMA*M). The matrix A is the 1-d discrete |
c | Laplacian. The matrix M is the associated mass matrix |
c | arising from using piecewise linear finite elements |
c | on the interval [0, 1]. |
c %-------------------------------------------------------%
c
h = one / dble(n+1)
r1 = (four / six) * h
r2 = (one / six) * h
do 20 j=1,n
ad(j) = two/h - sigma * r1
adl(j) = -one/h - sigma * r2
20 continue
call dcopy (n, adl, 1, adu, 1)
call dgttrf (n, adl, ad, adu, adu2, ipiv, ierr)
if (ierr .ne. 0) then
print *, ' Error with _gttrf in _SDRV4.'
go to 9000
end if
c
c %-------------------------------------------%
c | M A I N L O O P (Reverse communication) |
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) then
c
c %--------------------------------------------%
c | Perform y <--- OP*x = inv[A-SIGMA*M]*M*x |
c | to force the starting vector into the |
c | range of OP. The user should supply |
c | his/her own matrix vector multiplication |
c | routine and a linear system solver here. |
c | The matrix vector multiplication routine |
c | takes workd(ipntr(1)) as the input vector. |
c | The final result is returned to |
c | workd(ipntr(2)). |
c %--------------------------------------------%
c
call mv (n, workd(ipntr(1)), workd(ipntr(2)))
c
call dgttrs ('Notranspose', n, 1, adl, ad, adu, adu2, ipiv,
& workd(ipntr(2)), n, ierr)
if (ierr .ne. 0) then
print *, ' '
print *, ' Error with _gttrs in _SDRV4. '
print *, ' '
go to 9000
end if
c
c %-----------------------------------------%
c | L O O P B A C K to call DSAUPD again. |
c %-----------------------------------------%
c
go to 10
c
else if (ido .eq. 1) then
c
c %-----------------------------------------%
c | Perform y <-- OP*x = inv[A-sigma*M]*M*x |
c | M*x has been saved in workd(ipntr(3)). |
c | the user only needs the linear system |
c | solver here that takes workd(ipntr(3) |
c | as input, and returns the result to |
c | workd(ipntr(2)). |
c %-----------------------------------------%
c
call dcopy ( n, workd(ipntr(3)), 1, workd(ipntr(2)), 1)
call dgttrs ('Notranspose', n, 1, adl, ad, adu, adu2, ipiv,
& workd(ipntr(2)), n, ierr)
if (ierr .ne. 0) then
print *, ' '
print *, ' Error with _gttrs in _SDRV4.'
print *, ' '
go to 9000
end if
c
c %-----------------------------------------%
c | L O O P B A C K to call DSAUPD again. |
c %-----------------------------------------%
c
go to 10
c
else if (ido .eq. 2) then
c
c %-----------------------------------------%
c | Perform y <--- M*x |
c | Need the matrix vector multiplication |
c | routine here that takes workd(ipntr(1)) |
c | as the input and returns the result to |
c | workd(ipntr(2)). |
c %-----------------------------------------%
c
call mv (n, 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 the documentation of _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 also be computed now if |
c | desired. (indicated by rvec = .true.) |
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 NEV columns of the two dimensional |
c | array V if requested. Otherwise, an |
c | orthogonal basis for the invariant subspace |
c | corresponding to the eigenvalues in D is |
c | 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 30 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 av(n, v(1,j), workd)
call mv(n, v(1,j), workd(n+1))
call daxpy (n, -d(j,1), workd(n+1), 1, workd, 1)
d(j,2) = dnrm2(n, workd, 1)
d(j,2) = d(j,2) / abs(d(j,1))
c
30 continue
c
call dmout(6, nconv, 2, d, maxncv, -6,
& 'Ritz values and relative residuals')
c
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 *, ' _SDRV4 '
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 dsdrv4. |
c %---------------------------%
c
9000 continue
c
end
c
c------------------------------------------------------------------------
c matrix vector subroutine
c The matrix used is the 1 dimensional mass matrix
c on the interval [0,1].
c
subroutine mv (n, v, w)
integer n, j
Double precision
& v(n),w(n), one, four, six, h
parameter (one = 1.0D+0, four = 4.0D+0,
& six = 6.0D+0)
c
w(1) = four*v(1) + v(2)
do 100 j = 2,n-1
w(j) = v(j-1) + four*v(j) + v(j+1)
100 continue
j = n
w(j) = v(j-1) + four*v(j)
c
c Scale the vector w by h.
c
h = one / ( six*dble(n+1))
call dscal(n, h, w, 1)
return
end
c------------------------------------------------------------------------
c matrix vector subroutine
c where the matrix is the finite element discretization of the
c 1 dimensional discrete Laplacian on [0,1] with zero Dirichlet
c boundary condition using piecewise linear elements.
c
subroutine av (n, v, w)
integer n, j
Double precision
& v(n), w(n), two, one, h
parameter (one = 1.0D+0, two = 2.0D+0)
c
w(1) = two*v(1) - v(2)
do 100 j = 2,n-1
w(j) = - v(j-1) + two*v(j) - v(j+1)
100 continue
j = n
w(j) = - v(j-1) + two*v(j)
c
c Scale the vector w by (1/h)
c
h = one / dble(n+1)
call dscal(n, one/h, w, 1)
return
end
|
