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CGESVD Example Program in Fortran
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* =============================================================================
*
* CGESVD Example.
* ==============
*
* Program computes the singular value decomposition of a general
* rectangular complex matrix A:
*
* ( 5.91, -5.69) ( 7.09, 2.72) ( 7.78, -4.06) ( -0.79, -7.21)
* ( -3.15, -4.08) ( -1.89, 3.27) ( 4.57, -2.07) ( -3.88, -3.30)
* ( -4.89, 4.20) ( 4.10, -6.70) ( 3.28, -3.84) ( 3.84, 1.19)
*
* Description.
* ============
*
* The routine computes the singular value decomposition (SVD) of a complex
* m-by-n matrix A, optionally computing the left and/or right singular
* vectors. The SVD is written as
*
* A = U*SIGMA*VH
*
* where SIGMA is an m-by-n matrix which is zero except for its min(m,n)
* diagonal elements, U is an m-by-m unitary matrix and VH (V conjugate
* transposed) is an n-by-n unitary matrix. The diagonal elements of SIGMA
* are the singular values of A; they are real and non-negative, and are
* returned in descending order. The first min(m, n) columns of U and V are
* the left and right singular vectors of A.
*
* Note that the routine returns VH, not V.
*
* Example Program Results.
* ========================
*
* CGESVD Example Program Results
*
* Singular values
* 17.63 11.61 6.78
*
* Left singular vectors (stored columnwise)
* ( -0.86, 0.00) ( 0.40, 0.00) ( 0.32, 0.00)
* ( -0.35, 0.13) ( -0.24, -0.21) ( -0.63, 0.60)
* ( 0.15, 0.32) ( 0.61, 0.61) ( -0.36, 0.10)
*
* Right singular vectors (stored rowwise)
* ( -0.22, 0.51) ( -0.37, -0.32) ( -0.53, 0.11) ( 0.15, 0.38)
* ( 0.31, 0.31) ( 0.09, -0.57) ( 0.18, -0.39) ( 0.38, -0.39)
* ( 0.53, 0.24) ( 0.49, 0.28) ( -0.47, -0.25) ( -0.15, 0.19)
* =============================================================================
*
* .. Parameters ..
INTEGER M, N
PARAMETER ( M = 3, N = 4 )
INTEGER LDA, LDU, LDVT
PARAMETER ( LDA = M, LDU = M, LDVT = N )
INTEGER LWMAX
PARAMETER ( LWMAX = 1000 )
*
* .. Local Scalars ..
INTEGER INFO, LWORK
*
* .. Local Arrays ..
* RWORK dimension should be at least MAX( 1, 5*MIN(M,N) )
REAL S( M ), RWORK( 5*M )
COMPLEX A( LDA, N ), U( LDU, M ), VT( LDVT, N ),
$ WORK( LWMAX )
DATA A/
$ ( 5.91,-5.69),(-3.15,-4.08),(-4.89, 4.20),
$ ( 7.09, 2.72),(-1.89, 3.27),( 4.10,-6.70),
$ ( 7.78,-4.06),( 4.57,-2.07),( 3.28,-3.84),
$ (-0.79,-7.21),(-3.88,-3.30),( 3.84, 1.19)
$ /
*
* .. External Subroutines ..
EXTERNAL CGESVD
EXTERNAL PRINT_MATRIX, PRINT_RMATRIX
*
* .. Intrinsic Functions ..
INTRINSIC INT, MIN
*
* .. Executable Statements ..
WRITE(*,*)'CGESVD Example Program Results'
*
* Query the optimal workspace.
*
LWORK = -1
CALL CGESVD( 'All', 'All', M, N, A, LDA, S, U, LDU, VT, LDVT,
$ WORK, LWORK, RWORK, INFO )
LWORK = MIN( LWMAX, INT( WORK( 1 ) ) )
*
* Compute SVD.
*
CALL CGESVD( 'All', 'All', M, N, A, LDA, S, U, LDU, VT, LDVT,
$ WORK, LWORK, RWORK, INFO )
*
* Check for convergence.
*
IF( INFO.GT.0 ) THEN
WRITE(*,*)'The algorithm computing SVD failed to converge.'
STOP
END IF
*
* Print singular values.
*
CALL PRINT_RMATRIX( 'Singular values', 1, M, S, 1 )
*
* Print left singular vectors.
*
CALL PRINT_MATRIX( 'Left singular vectors (stored columnwise)',
$ M, M, U, LDU )
*
* Print right singular vectors.
*
CALL PRINT_MATRIX( 'Right singular vectors (stored rowwise)',
$ M, N, VT, LDVT )
STOP
END
*
* End of CGESVD Example.
*
* =============================================================================
*
* Auxiliary routine: printing a matrix.
*
SUBROUTINE PRINT_MATRIX( DESC, M, N, A, LDA )
CHARACTER*(*) DESC
INTEGER M, N, LDA
COMPLEX A( LDA, * )
*
INTEGER I, J
*
WRITE(*,*)
WRITE(*,*) DESC
DO I = 1, M
WRITE(*,9998) ( A( I, J ), J = 1, N )
END DO
*
9998 FORMAT( 11(:,1X,'(',F6.2,',',F6.2,')') )
RETURN
END
*
* Auxiliary routine: printing a real matrix.
*
SUBROUTINE PRINT_RMATRIX( DESC, M, N, A, LDA )
CHARACTER*(*) DESC
INTEGER M, N, LDA
REAL A( LDA, * )
*
INTEGER I, J
*
WRITE(*,*)
WRITE(*,*) DESC
DO I = 1, M
WRITE(*,9998) ( A( I, J ), J = 1, N )
END DO
*
9998 FORMAT( 11(:,1X,F6.2) )
RETURN
END
Parent topic: CGESVD Example