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SGESDD Example Program in Fortran
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* =============================================================================
*
* SGESDD Example.
* ==============
*
* Program computes the singular value decomposition of a general
* rectangular matrix A using a divide and conquer method, where A is:
*
* 7.52 -1.10 -7.95 1.08
* -0.76 0.62 9.34 -7.10
* 5.13 6.62 -5.66 0.87
* -4.75 8.52 5.75 5.30
* 1.33 4.91 -5.49 -3.52
* -2.40 -6.77 2.34 3.95
*
* Description.
* ============
*
* The routine computes the singular value decomposition (SVD) of a real
* m-by-n matrix A, optionally computing the left and/or right singular
* vectors. If singular vectors are desired, it uses a divide and conquer
* algorithm. The SVD is written as
*
* A = U*SIGMA*VT
*
* 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 orthogonal matrix and VT (V transposed)
* is an n-by-n orthogonal 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 VT, not V.
*
* Example Program Results.
* ========================
*
* SGESDD Example Program Results
*
* Singular values
* 18.37 13.63 10.85 4.49
*
* Left singular vectors (stored columnwise)
* -0.57 0.18 0.01 0.53
* 0.46 -0.11 -0.72 0.42
* -0.45 -0.41 0.00 0.36
* 0.33 -0.69 0.49 0.19
* -0.32 -0.31 -0.28 -0.61
* 0.21 0.46 0.39 0.09
*
* Right singular vectors (stored rowwise)
* -0.52 -0.12 0.85 -0.03
* 0.08 -0.99 -0.09 -0.01
* -0.28 -0.02 -0.14 0.95
* 0.81 0.01 0.50 0.31
* =============================================================================
*
* .. Parameters ..
INTEGER M, N
PARAMETER ( M = 6, 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 ..
* IWORK dimension should be at least 8*MIN(M,N)
INTEGER IWORK( 8*N )
REAL A( LDA, N ), U( LDU, M ), VT( LDVT, N ), S( N ),
$ WORK( LWMAX )
DATA A/
$ 7.52,-0.76, 5.13,-4.75, 1.33,-2.40,
$ -1.10, 0.62, 6.62, 8.52, 4.91,-6.77,
$ -7.95, 9.34,-5.66, 5.75,-5.49, 2.34,
$ 1.08,-7.10, 0.87, 5.30,-3.52, 3.95
$ /
*
* .. External Subroutines ..
EXTERNAL SGESDD
EXTERNAL PRINT_MATRIX
*
* .. Intrinsic Functions ..
INTRINSIC INT, MIN
*
* .. Executable Statements ..
WRITE(*,*)'SGESDD Example Program Results'
*
* Query the optimal workspace.
*
LWORK = -1
CALL SGESDD( 'Singular vectors', M, N, A, LDA, S, U, LDU, VT,
$ LDVT, WORK, LWORK, IWORK, INFO )
LWORK = MIN( LWMAX, INT( WORK( 1 ) ) )
*
* Compute SVD.
*
CALL SGESDD( 'Singular vectors', M, N, A, LDA, S, U, LDU, VT,
$ LDVT, WORK, LWORK, IWORK, 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_MATRIX( 'Singular values', 1, N, S, 1 )
*
* Print left singular vectors.
*
CALL PRINT_MATRIX( 'Left singular vectors (stored columnwise)',
$ M, N, U, LDU )
*
* Print right singular vectors.
*
CALL PRINT_MATRIX( 'Right singular vectors (stored rowwise)',
$ N, N, VT, LDVT )
STOP
END
*
* End of SGESDD Example.
*
* =============================================================================
*
* Auxiliary routine: printing a matrix.
*
SUBROUTINE PRINT_MATRIX( 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: SGESDD Example