1，源码

        SUBROUTINE INIT_MATRIX(A, m, n, lda)DOUBLE PRECISION A(*)CALL SRAND(2024)DO i=1, mDO j=1, nA(i + lda*(j-1)) = RAND() + RAND()
C                                WRITE(*, '(F8.4)') A(i)END DOEND DOENDSUBROUTINE PRINT_MATRIX(A, m, n, lda)DOUBLE PRECISION A(*)DO i=1, mDO j=1, n
C                                WRITE(*, '(F8.4 , $)') A(i + lda*(j-1))WRITE(*, 20) A(i + lda*(j-1))
20                              FORMAT (1X,F8.4, $)END DOPRINT *,''END DOENDPROGRAM MATRIX_EXINTEGER m, n, ldaDOUBLE PRECISION A(20)lda = 4m = 4n = 5CALL INIT_MATRIX(A, m, n, lda)PRINT *, "A ="CALL PRINT_MATRIX(A, m, n, lda)PRINT *, "A(11) ="PRINT *, A(11)END

 Makefile

matrix_ex: matrix_ex.fgfortran -g $< -o $@.PHONY: clean
clean:-rm matrix_ex

2，执行
 

make./matrix_ex

效果:

3, dgesvd example

*  Copyright (C) 2009-2015 Intel Corporation. All Rights Reserved.
*  The information and material ("Material") provided below is owned by Intel
*  Corporation or its suppliers or licensors, and title to such Material remains
*  with Intel Corporation or its suppliers or licensors. The Material contains
*  proprietary information of Intel or its suppliers and licensors. The Material
*  is protected by worldwide copyright laws and treaty provisions. No part of
*  the Material may be copied, reproduced, published, uploaded, posted,
*  transmitted, or distributed in any way without Intel's prior express written
*  permission. No license under any patent, copyright or other intellectual
*  property rights in the Material is granted to or conferred upon you, either
*  expressly, by implication, inducement, estoppel or otherwise. Any license
*  under such intellectual property rights must be express and approved by Intel
*  in writing.
*  =============================================================================
*
*  DGESVD Example.
*  ==============
*
*  Program computes the singular value decomposition of a general
*  rectangular matrix A:
*
*    8.79   9.93   9.83   5.45   3.16
*    6.11   6.91   5.04  -0.27   7.98
*   -9.15  -7.93   4.86   4.85   3.01
*    9.57   1.64   8.83   0.74   5.80
*   -3.49   4.02   9.80  10.00   4.27
*    9.84   0.15  -8.99  -6.02  -5.31
*
*  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. 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.
*  ========================
*
* DGESVD Example Program Results
*
* Singular values
*  27.47  22.64   8.56   5.99   2.01
*
* Left singular vectors (stored columnwise)
*  -0.59   0.26   0.36   0.31   0.23
*  -0.40   0.24  -0.22  -0.75  -0.36
*  -0.03  -0.60  -0.45   0.23  -0.31
*  -0.43   0.24  -0.69   0.33   0.16
*  -0.47  -0.35   0.39   0.16  -0.52
*   0.29   0.58  -0.02   0.38  -0.65
*
* Right singular vectors (stored rowwise)
*  -0.25  -0.40  -0.69  -0.37  -0.41
*   0.81   0.36  -0.25  -0.37  -0.10
*  -0.26   0.70  -0.22   0.39  -0.49
*   0.40  -0.45   0.25   0.43  -0.62
*  -0.22   0.14   0.59  -0.63  -0.44
*  =============================================================================
*
*     .. Parameters ..INTEGER          M, NPARAMETER        ( M = 6, N = 5 )INTEGER          LDA, LDU, LDVTPARAMETER        ( LDA = M, LDU = M, LDVT = N )INTEGER          LWMAXPARAMETER        ( LWMAX = 1000 )
*
*     .. Local Scalars ..INTEGER          INFO, LWORK
*
*     .. Local Arrays ..DOUBLE PRECISION A( LDA, N ), U( LDU, M ), VT( LDVT, N ), S( N ),$                 WORK( LWMAX )DATA             A/$  8.79, 6.11,-9.15, 9.57,-3.49, 9.84,$  9.93, 6.91,-7.93, 1.64, 4.02, 0.15,$  9.83, 5.04, 4.86, 8.83, 9.80,-8.99,$  5.45,-0.27, 4.85, 0.74,10.00,-6.02,$  3.16, 7.98, 3.01, 5.80, 4.27,-5.31$                  /
*
*     .. External Subroutines ..EXTERNAL         DGESVDEXTERNAL         PRINT_MATRIX
*
*     .. Intrinsic Functions ..INTRINSIC        INT, MIN
*
*     .. Executable Statements ..WRITE(*,*)'DGESVD Example Program Results'
*
*     Query the optimal workspace.
*LWORK = -1CALL DGESVD( 'All', 'All', M, N, A, LDA, S, U, LDU, VT, LDVT,$             WORK, LWORK, INFO )LWORK = MIN( LWMAX, INT( WORK( 1 ) ) )
*
*     Compute SVD.
*CALL DGESVD( 'All', 'All', M, N, A, LDA, S, U, LDU, VT, LDVT,$             WORK, LWORK, INFO )
*
*     Check for convergence.
*IF( INFO.GT.0 ) THENWRITE(*,*)'The algorithm computing SVD failed to converge.'STOPEND 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 )STOPEND
*
*     End of DGESVD Example.
*
*  =============================================================================
*
*     Auxiliary routine: printing a matrix.
*SUBROUTINE PRINT_MATRIX( DESC, M, N, A, LDA )CHARACTER*(*)    DESCINTEGER          M, N, LDADOUBLE PRECISION A( LDA, * )
*INTEGER          I, J
*WRITE(*,*)WRITE(*,*) DESCDO I = 1, MWRITE(*,9998) ( A( I, J ), J = 1, N )END DO
*9998 FORMAT( 11(:,1X,F6.2) )RETURNEND

Makefile

svd_dgesve_ex: svd_dgesve_ex.fgfortran -g $< ../lapack-3.11/liblapack.a  ../lapack-3.11/librefblas.a -o $@

运行