Program dsygvd_example ! DSYGVD Example Program Text ! Copyright 2017, Numerical Algorithms Group Ltd. http://www.nag.com ! .. Use Statements .. Use lapack_example_aux, Only: nagf_blas_damax_val, & nagf_file_print_matrix_real_gen Use lapack_interfaces, Only: ddisna, dlansy, dsygvd, dtrcon Use lapack_precision, Only: dp ! .. Implicit None Statement .. Implicit None ! .. Parameters .. Real (Kind=dp), Parameter :: one = 1.0E+0_dp Real (Kind=dp), Parameter :: zero = 0.0_dp Integer, Parameter :: nb = 64, nin = 5, nout = 6 ! .. Local Scalars .. Real (Kind=dp) :: anorm, bnorm, eps, r, rcond, rcondb, t1, t2, t3 Integer :: i, ifail, info, k, lda, ldb, liwork, lwork, n ! .. Local Arrays .. Real (Kind=dp), Allocatable :: a(:, :), b(:, :), eerbnd(:), rcondz(:), & w(:), work(:), zerbnd(:) Real (Kind=dp) :: dummy(1) Integer :: idum(1) Integer, Allocatable :: iwork(:) ! .. Intrinsic Procedures .. Intrinsic :: abs, epsilon, max, nint ! .. Executable Statements .. Write (nout, *) 'DSYGVD Example Program Results' Write (nout, *) ! Skip heading in data file Read (nin, *) Read (nin, *) n lda = n ldb = n Allocate (a(lda,n), b(ldb,n), eerbnd(n), rcondz(n), w(n), zerbnd(n)) ! Use routine workspace query to get optimal workspace. lwork = -1 liwork = -1 Call dsygvd(2, 'Vectors', 'Upper', n, a, lda, b, ldb, w, dummy, lwork, & idum, liwork, info) ! Make sure that there is enough workspace for block size nb. lwork = max(1+(nb+6+2*n)*n, nint(dummy(1))) liwork = max(3+5*n, idum(1)) Allocate (work(lwork), iwork(liwork)) ! Read the upper triangular parts of the matrices A and B Read (nin, *)(a(i,i:n), i=1, n) Read (nin, *)(b(i,i:n), i=1, n) ! Compute the one-norms of the symmetric matrices A and B anorm = dlansy('One norm', 'Upper', n, a, lda, work) bnorm = dlansy('One norm', 'Upper', n, b, ldb, work) ! Solve the generalized symmetric eigenvalue problem ! A*B*x = lambda*x (ITYPE = 2) Call dsygvd(2, 'Vectors', 'Upper', n, a, lda, b, ldb, w, work, lwork, & iwork, liwork, info) If (info==0) Then ! Print solution Write (nout, *) 'Eigenvalues' Write (nout, 100) w(1:n) Flush (nout) ! Normalize the eigenvectors, largest positive Do i = 1, n Call nagf_blas_damax_val(n, a(1,i), 1, k, r) If (a(k,i)<zero) Then a(1:n, i) = -a(1:n, i) End If End Do ! ifail: behaviour on error exit ! =0 for hard exit, =1 for quiet-soft, =-1 for noisy-soft ifail = 0 Call nagf_file_print_matrix_real_gen('General', ' ', n, n, a, lda, & 'Eigenvectors', ifail) ! Call DTRCON to estimate the reciprocal condition ! number of the Cholesky factor of B. Note that: ! cond(B) = 1/RCOND**2 Call dtrcon('One norm', 'Upper', 'Non-unit', n, b, ldb, rcond, work, & iwork, info) ! Print the reciprocal condition number of B rcondb = rcond**2 Write (nout, *) Write (nout, *) 'Estimate of reciprocal condition number for B' Write (nout, 110) rcondb Flush (nout) ! Get the machine precision, EPS, and if RCONDB is not less ! than EPS**2, compute error estimates for the eigenvalues and ! eigenvectors eps = epsilon(1.0E0_dp) If (rcond>=eps) Then ! Call DDISNA to estimate reciprocal condition ! numbers for the eigenvectors of (A*B - lambda*I) Call ddisna('Eigenvectors', n, n, w, rcondz, info) ! Compute the error estimates for the eigenvalues and ! eigenvectors t1 = one/rcond t2 = eps*t1 t3 = anorm*bnorm Do i = 1, n eerbnd(i) = eps*(t3+abs(w(i))/rcondb) zerbnd(i) = t2*(t3/rcondz(i)+t1) End Do ! Print the approximate error bounds for the eigenvalues ! and vectors Write (nout, *) Write (nout, *) 'Error estimates for the eigenvalues' Write (nout, 110) eerbnd(1:n) Write (nout, *) Write (nout, *) 'Error estimates for the eigenvectors' Write (nout, 110) zerbnd(1:n) Else Write (nout, *) Write (nout, *) 'B is very ill-conditioned, error ', & 'estimates have not been computed' End If Else If (info>n .And. info<=2*n) Then i = info - n Write (nout, 120) 'The leading minor of order ', i, & ' of B is not positive definite' Else Write (nout, 130) 'Failure in DSYGVD. INFO =', info End If 100 Format (3X, (6F11.4)) 110 Format (4X, 1P, 6E11.1) 120 Format (1X, A, I4, A) 130 Format (1X, A, I4) End Program