Program f08kmfe
! F08KMF Example Program Text
! Mark 30.2 Release. NAG Copyright 2024.
! .. Use Statements ..
Use nag_library, Only: dgesvdx, nag_wp
! .. Implicit None Statement ..
Implicit None
! .. Parameters ..
Integer, Parameter :: nin = 5, nout = 6
! .. Local Scalars ..
Real (Kind=nag_wp) :: vl, vu
Integer :: i, il, info, iu, lda, ldu, ldvt, &
lwork, m, n, ns
Character (1) :: range
! .. Local Arrays ..
Real (Kind=nag_wp), Allocatable :: a(:,:), a_copy(:,:), b(:), s(:), &
u(:,:), vt(:,:), work(:)
Real (Kind=nag_wp) :: dummy(1,1)
Integer, Allocatable :: iwork(:)
! .. Intrinsic Procedures ..
Intrinsic :: nint
! .. Executable Statements ..
Write (nout,*) 'F08KMF Example Program Results'
Write (nout,*)
! Skip heading in data file
Read (nin,*)
Read (nin,*) m, n
lda = m
ldu = m
ldvt = n
Allocate (a(lda,n),a_copy(m,n),s(n),vt(ldvt,n),u(ldu,m),b(m), &
iwork(12*m))
! Read the m by n matrix A from data file
Read (nin,*)(a(i,1:n),i=1,m)
! Read the right hand side of the linear system
Read (nin,*) b(1:m)
! Read range for selected singular values
Read (nin,*) range
If (range=='I' .Or. range=='i') Then
Read (nin,*) il, iu
Else If (range=='V' .Or. range=='v') Then
Read (nin,*) vl, vu
End If
a_copy(1:m,1:n) = a(1:m,1:n)
! Use routine workspace query to get optimal workspace.
lwork = -1
! The NAG name equivalent of dgesvd is f08kmf
Call dgesvdx('V','V',range,m,n,a,lda,vl,vu,il,iu,ns,s,u,ldu,vt,ldvt, &
dummy,lwork,iwork,info)
! Make sure that there is enough workspace for block size nb.
lwork = nint(dummy(1,1))
Allocate (work(lwork))
! Compute the singular values and left and right singular vectors
! of A.
! The NAG name equivalent of dgesvd is f08kmf
Call dgesvdx('V','V',range,m,n,a,lda,vl,vu,il,iu,ns,s,u,ldu,vt,ldvt, &
work,lwork,iwork,info)
If (info/=0) Then
Write (nout,99999) 'Failure in DGESVDX. INFO =', info
99999 Format (1X,A,I4)
Go To 100
End If
! Print the selected singular values of A
Write (nout,*) 'Singular values of A:'
Write (nout,99998) s(1:ns)
99998 Format (1X,4(3X,F11.4))
Call compute_error_bounds(m,ns,s)
If (m>n .And. ns==n) Then
! Compute V*Inv(S)*U^T * b to get least squares solution.
Call compute_least_squares(m,n,a_copy,m,u,ldu,vt,ldvt,s,b)
End If
100 Continue
Contains
Subroutine compute_least_squares(m,n,a,lda,u,ldu,vt,ldvt,s,b)
! .. Use Statements ..
Use nag_library, Only: dgemv, dnrm2
! .. Implicit None Statement ..
Implicit None
! .. Scalar Arguments ..
Integer, Intent (In) :: lda, ldu, ldvt, m, n
! .. Array Arguments ..
Real (Kind=nag_wp), Intent (In) :: a(lda,n), s(n), u(ldu,m), &
vt(ldvt,n)
Real (Kind=nag_wp), Intent (Inout) :: b(m)
! .. Local Scalars ..
Real (Kind=nag_wp) :: alpha, beta, norm
! .. Local Arrays ..
Real (Kind=nag_wp), Allocatable :: x(:), y(:)
! .. Intrinsic Procedures ..
Intrinsic :: allocated
! .. Executable Statements ..
Allocate (x(n),y(n))
! Compute V*Inv(S)*U^T * b to get least squares solution.
! y = U^T b
! The NAG name equivalent of dgemv is f06paf
alpha = 1._nag_wp
beta = 0._nag_wp
Call dgemv('T',m,n,alpha,u,ldu,b,1,beta,y,1)
y(1:n) = y(1:n)/s(1:n)
! x = V y
Call dgemv('T',n,n,alpha,vt,ldvt,y,1,beta,x,1)
Write (nout,*)
Write (nout,*) 'Least squares solution:'
Write (nout,99999) x(1:n)
! Find norm of residual ||b-Ax||.
alpha = -1._nag_wp
beta = 1._nag_wp
Call dgemv('N',m,n,alpha,a,lda,x,1,beta,b,1)
norm = dnrm2(m,b,1)
Write (nout,*)
Write (nout,*) 'Norm of Residual:'
Write (nout,99999) norm
If (allocated(x)) Then
Deallocate (x)
End If
If (allocated(y)) Then
Deallocate (y)
End If
99999 Format (1X,4(3X,F11.4))
End Subroutine compute_least_squares
Subroutine compute_error_bounds(m,n,s)
! Error estimates for singular values and vectors is computed
! and printed here.
! .. Use Statements ..
Use nag_library, Only: ddisna, nag_wp, x02ajf
! .. Implicit None Statement ..
Implicit None
! .. Scalar Arguments ..
Integer, Intent (In) :: m, n
! .. Array Arguments ..
Real (Kind=nag_wp), Intent (In) :: s(n)
! .. Local Scalars ..
Real (Kind=nag_wp) :: eps, serrbd
Integer :: i, info
! .. Local Arrays ..
Real (Kind=nag_wp), Allocatable :: rcondu(:), rcondv(:), uerrbd(:), &
verrbd(:)
! .. Executable Statements ..
Allocate (rcondu(n),rcondv(n),uerrbd(n),verrbd(n))
! Get the machine precision, EPS and compute the approximate
! error bound for the computed singular values. Note that for
! the 2-norm, S(1) = norm(A)
eps = x02ajf()
serrbd = eps*s(1)
! Call DDISNA (F08FLF) to estimate reciprocal condition
! numbers for the singular vectors
Call ddisna('Left',m,n,s,rcondu,info)
Call ddisna('Right',m,n,s,rcondv,info)
! Compute the error estimates for the singular vectors
Do i = 1, n
uerrbd(i) = serrbd/rcondu(i)
verrbd(i) = serrbd/rcondv(i)
End Do
! Print the approximate error bounds for the singular values
! and vectors
Write (nout,*)
Write (nout,*) 'Estimates given as multiples of machine precision'
Write (nout,*) 'Error estimate for the singular values'
Write (nout,99999) nint(serrbd/x02ajf())
Write (nout,*)
Write (nout,*) 'Error estimates for the left singular vectors'
Write (nout,99999) nint(uerrbd(1:n)/x02ajf())
Write (nout,*)
Write (nout,*) 'Error estimates for the right singular vectors'
Write (nout,99999) nint(verrbd(1:n)/x02ajf())
99999 Format (4X,6I11)
End Subroutine compute_error_bounds
End Program f08kmfe