Developer Reference

Intel® oneAPI Math Kernel Library LAPACK Examples

ID 766877
Date 3/22/2024
Public

A newer version of this document is available. Customers should click here to go to the newest version.

Document Table of Contents

DGESVD Example Program in C

/*******************************************************************************
*  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
*/
#include <stdlib.h>
#include <stdio.h>

/* DGESVD prototype */
extern void dgesvd( char* jobu, char* jobvt, int* m, int* n, double* a,
                int* lda, double* s, double* u, int* ldu, double* vt, int* ldvt,
                double* work, int* lwork, int* info );
/* Auxiliary routines prototypes */
extern void print_matrix( char* desc, int m, int n, double* a, int lda );

/* Parameters */
#define M 6
#define N 5
#define LDA M
#define LDU M
#define LDVT N

/* Main program */
int main() {
        /* Locals */
        int m = M, n = N, lda = LDA, ldu = LDU, ldvt = LDVT, info, lwork;
        double wkopt;
        double* work;
        /* Local arrays */
        double s[N], u[LDU*M], vt[LDVT*N];
        double a[LDA*N] = {
            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
        };
        /* Executable statements */
        printf( " DGESVD Example Program Results\n" );
        /* Query and allocate the optimal workspace */
        lwork = -1;
        dgesvd( "All", "All", &m, &n, a, &lda, s, u, &ldu, vt, &ldvt, &wkopt, &lwork,
         &info );
        lwork = (int)wkopt;
        work = (double*)malloc( lwork*sizeof(double) );
        /* Compute SVD */
        dgesvd( "All", "All", &m, &n, a, &lda, s, u, &ldu, vt, &ldvt, work, &lwork,
         &info );
        /* Check for convergence */
        if( info > 0 ) {
                printf( "The algorithm computing SVD failed to converge.\n" );
                exit( 1 );
        }
        /* Print singular values */
        print_matrix( "Singular values", 1, n, s, 1 );
        /* Print left singular vectors */
        print_matrix( "Left singular vectors (stored columnwise)", m, n, u, ldu );
        /* Print right singular vectors */
        print_matrix( "Right singular vectors (stored rowwise)", n, n, vt, ldvt );
        /* Free workspace */
        free( (void*)work );
        exit( 0 );
} /* End of DGESVD Example */

/* Auxiliary routine: printing a matrix */
void print_matrix( char* desc, int m, int n, double* a, int lda ) {
        int i, j;
        printf( "\n %s\n", desc );
        for( i = 0; i < m; i++ ) {
                for( j = 0; j < n; j++ ) printf( " %6.2f", a[i+j*lda] );
                printf( "\n" );
        }
}