program dnsimp c 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 a n by n real nonsymmetric 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 DNAUPD 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 DNEUPD. 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-nonsym.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 obtained from the standard central difference c discretization of the convection-diffusion operator c (Laplacian u) + rho*(du / dx) c on the unit square, with zero Dirichlet boundary condition. c c ... OP = A and B = I. c ... Assume "call av (nx,x,y)" computes y = A*x c ... Use mode 1 of DNAUPD. c c\BeginLib c c\Routines called: c dnaupd ARPACK reverse communication interface routine. c dneupd ARPACK routine that returns Ritz values and (optionally) c Ritz vectors. c dlapy2 LAPACK routine to compute sqrt(x**2+y**2) carefully. c daxpy Level 1 BLAS that computes y <- alpha*x+y. c dnrm2 Level 1 BLAS that computes the norm of a vector. c av Matrix vector multiplication routine that computes A*x. c tv Matrix vector multiplication routine that computes T*x, c where T is a tridiagonal matrix. It is used in routine c av. 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: nsimp.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 | 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=2500, maxnev=12, maxncv=30, ldv=maxn) c c %--------------% c | Local Arrays | c %--------------% c integer iparam(11), ipntr(14) logical select(maxncv) Double precision & ax(maxn), d(maxncv,3), resid(maxn), & v(ldv,maxncv), workd(3*maxn), & workev(3*maxncv), & workl(3*maxncv*maxncv+6*maxncv) c c %---------------% c | Local Scalars | c %---------------% c character bmat*1, which*2 integer ido, n, nx, nev, ncv, lworkl, info, ierr, & j, ishfts, maxitr, mode1, nconv Double precision & tol, sigmar, sigmai logical first, rvec 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 & dlapy2, dnrm2 external dlapy2, dnrm2, daxpy c c %--------------------% c | Intrinsic function | c %--------------------% c intrinsic abs c c Storage and variables for getting matrix from disk c integer dimA, sizeA parameter (dimA = 50) parameter (sizeA = dimA*dimA) character rep*10 character field*7 character symm*19 double precision A(sizeA) 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 mnaupd = 1. | c %-------------------------------------------------% c include 'debug.h' ndigit = -3 logfil = 6 mnaitr = 0 mnapps = 0 mnaupd = 1 mnaup2 = 0 mneigh = 0 mneupd = 0 c c Read in A matrix from disk, Matrix Market format c open(10, FILE='testA.mtx',STATUS='OLD') call mmread(10,rep,field,symm,nrows,ncols,nnz,sizeA, * temp,temp,temp,A,temp) close(10) c c %-------------------------------------------------% c | The following sets dimensions for this problem. | c %-------------------------------------------------% c nx = dimA 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 DNAUPD for the | c | other options SM, LR, SR, LI, SI. | c | | c | Note: NEV and NCV must satisfy the following | c | conditions: | c | NEV <= MAXNEV | c | NEV + 2 <= NCV <= MAXNCV | c | | c %-----------------------------------------------% c nev = 10 ncv = 20 bmat = 'I' which = 'SR' c if ( n .gt. maxn ) then print *, ' ERROR with _NSIMP: N is greater than MAXN ' go to 9000 else if ( nev .gt. maxnev ) then print *, ' ERROR with _NSIMP: NEV is greater than MAXNEV ' go to 9000 else if ( ncv .gt. maxncv ) then print *, ' ERROR with _NSIMP: NCV is greater than MAXNCV ' go to 9000 end if c c %-----------------------------------------------------% c | | c | Specification of stopping rules and initial | c | conditions before calling DNAUPD | 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 DNAUPD. (see usage below) | c | | c | It MUST initially be set to 0 before the first | c | call to DNAUPD. | 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 DNAUPD as | c | workspace. Its dimension LWORKL is set as | c | illustrated below. | c | | c %-----------------------------------------------------% c lworkl = 3*ncv**2+6*ncv tol = zero ido = 0 info = 0 c c %---------------------------------------------------% c | Specification of Algorithm Mode: | c | | c | This program uses the exact shift strategy | c | (indicated by setting IPARAM(1) = 1). | c | IPARAM(3) specifies the maximum number of Arnoldi | c | iterations allowed. Mode 1 of DNAUPD is used | c | (IPARAM(7) = 1). All these options can be changed | c | by the user. For details see the documentation in | c | DNAUPD. | c %---------------------------------------------------% c ishfts = 1 maxitr = 30 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) | c %-------------------------------------------% c 10 continue c c %---------------------------------------------% c | Repeatedly call the routine DNAUPD and take | c | actions indicated by parameter IDO until | c | either convergence is indicated or maxitr | c | has been exceeded. | c %---------------------------------------------% c call dnaupd ( 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 here | c | that takes workd(ipntr(1)) as the input | c | vector, and return the matrix vector | c | product to workd(ipntr(2)). | c %-------------------------------------------% c call av (nx, A, workd(ipntr(1)), workd(ipntr(2))) c c %-----------------------------------------% c | L O O P B A C K to call DNAUPD again. | c %-----------------------------------------% c go to 10 c endif 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 DNAUPD. | c %--------------------------% c print *, ' ' print *, ' Error with _naupd, info = ',info print *, ' Check the documentation of _naupd' print *, ' ' c else c c %-------------------------------------------% c | No fatal errors occurred. | c | Post-Process using DNEUPD. | 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 DNEUPD now called to do this | c | post processing (Other modes may require | c | more complicated post processing than | c | mode1,) | c | | c %-------------------------------------------% c c change to .true. to invoke bug with latest ARPACK rvec = .true. c call dneupd ( rvec, 'A', select, d, d(1,2), v, ldv, & sigmar, sigmai, workev, bmat, n, which, nev, tol, & resid, ncv, v, ldv, iparam, ipntr, workd, workl, & lworkl, ierr ) c c %------------------------------------------------% c | The real parts of the eigenvalues are returned | c | in the first column of the two dimensional | c | array D, and the IMAGINARY part are returned | c | in the second column of D. The corresponding | c | eigenvectors are returned in the first | c | NCONV (= IPARAM(5)) columns of the two | c | dimensional array V if requested. Otherwise, | c | an 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 DNEUPD. | c %------------------------------------% c print *, ' ' print *, ' Error with _neupd, info = ', ierr print *, ' Check the documentation of _neupd. ' print *, ' ' c else c first = .true. 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 if (d(j,2) .eq. zero) then c c %--------------------% c | Ritz value is real | c %--------------------% c call av(nx, A, v(1,j), ax) call daxpy(n, -d(j,1), v(1,j), 1, ax, 1) d(j,3) = dnrm2(n, ax, 1) d(j,3) = d(j,3) / abs(d(j,1)) c else if (first) then c c %------------------------% c | Ritz value is complex. | c | Residual of one Ritz | c | value of the conjugate | c | pair is computed. | c %------------------------% c call av(nx, A, v(1,j), ax) call daxpy(n, -d(j,1), v(1,j), 1, ax, 1) call daxpy(n, d(j,2), v(1,j+1), 1, ax, 1) d(j,3) = dnrm2(n, ax, 1) call av(nx, A, v(1,j+1), ax) call daxpy(n, -d(j,2), v(1,j), 1, ax, 1) call daxpy(n, -d(j,1), v(1,j+1), 1, ax, 1) d(j,3) = dlapy2( d(j,3), dnrm2(n, ax, 1) ) d(j,3) = d(j,3) / dlapy2(d(j,1),d(j,2)) d(j+1,3) = d(j,3) first = .false. else first = .true. end if c 20 continue c c %-----------------------------% c | Display computed residuals. | c %-----------------------------% c call dmout(6, nconv, 3, d, maxncv, -6, & 'Ritz values (Real, Imag) and residual 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 *, ' _NSIMP ' 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 dnsimp. | c %---------------------------% c 9000 continue c end c c========================================================================== c c matrix vector subroutine for Matrix Market format c subroutine av (nx, A, v, w) integer nx double precision A(nx*nx) double precision v(nx) double precision w(nx) CALL DGEMV('N', nx, nx, 1.D0, A, nx, v, 1, 0.D0, w, 1) return end c==========================================================================