Developer Reference

Intel® oneAPI Math Kernel Library LAPACK Examples

ID 766877
Date 3/31/2023
Public

A newer version of this document is available.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

Document Table of Contents

LAPACKE_zgesdd Example Program in C for Row Major Data Layout

/*******************************************************************************
*  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.
*
********************************************************************************
*/
/*
   LAPACKE_zgesdd Example.
   =======================

   Program computes the singular value decomposition of a general
   rectangular complex matrix A using a divide and conquer method, where A is:

   ( -5.40,  7.40) (  6.00,  6.38) (  9.91,  0.16) ( -5.28, -4.16)
   (  1.09,  1.55) (  2.60,  0.07) (  3.98, -5.26) (  2.03,  1.11)
   (  9.88,  1.91) (  4.92,  6.31) ( -2.11,  7.39) ( -9.81, -8.98)

   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. If singular vectors are desired, it uses a divide and conquer
   algorithm. 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.
   ========================

 LAPACKE_zgesdd (row-major, high-level) Example Program Results

 Singular values
  21.76  16.60   3.97

 Left singular vectors (stored columnwise)
 (  0.55,  0.00) (  0.76,  0.00) ( -0.34,  0.00)
 ( -0.04, -0.15) (  0.27, -0.23) (  0.55, -0.74)
 (  0.81,  0.12) ( -0.52, -0.14) (  0.13, -0.11)

 Right singular vectors (stored rowwise)
 (  0.23,  0.21) (  0.37,  0.39) (  0.24,  0.33) ( -0.56, -0.37)
 ( -0.58,  0.40) (  0.11,  0.17) (  0.60, -0.27) (  0.16,  0.06)
 (  0.60,  0.12) ( -0.19,  0.30) (  0.39,  0.20) (  0.45,  0.31)
*/
#include <stdlib.h>
#include <stdio.h>
#include "mkl_lapacke.h"

/* Auxiliary routines prototypes */
extern void print_matrix( char* desc, MKL_INT m, MKL_INT n, MKL_Complex16* a, MKL_INT lda );
extern void print_rmatrix( char* desc, MKL_INT m, MKL_INT n, double* a, MKL_INT lda );

/* Parameters */
#define M 3
#define N 4
#define LDA N
#define LDU M
#define LDVT N

/* Main program */
int main() {
        /* Locals */
        MKL_INT m = M, n = N, lda = LDA, ldu = LDU, ldvt = LDVT, info;
        /* Local arrays */
        double s[M];
        MKL_Complex16 u[LDU*M], vt[LDVT*N];
        MKL_Complex16 a[LDA*M] = {
           {-5.40,  7.40}, { 6.00,  6.38}, { 9.91,  0.16}, {-5.28, -4.16},
           { 1.09,  1.55}, { 2.60,  0.07}, { 3.98, -5.26}, { 2.03,  1.11},
           { 9.88,  1.91}, { 4.92,  6.31}, {-2.11,  7.39}, {-9.81, -8.98}
        };
        /* Executable statements */
        printf( "LAPACKE_zgesdd (row-major, high-level) Example Program Results\n" );
        /* Compute SVD */
        info = LAPACKE_zgesdd( LAPACK_ROW_MAJOR, 'S', m, n, a, lda, s, u,
         ldu, vt, ldvt );
        /* Check for convergence */
        if( info > 0 ) {
                printf( "The algorithm computing SVD failed to converge.\n" );
                exit( 1 );
        }
        /* Print singular values */
        print_rmatrix( "Singular values", 1, m, s, 1 );
        /* Print left singular vectors */
        print_matrix( "Left singular vectors (stored columnwise)", m, m, u, ldu );
        /* Print right singular vectors */
        print_matrix( "Right singular vectors (stored rowwise)", m, n, vt, ldvt );
        exit( 0 );
} /* End of LAPACKE_zgesdd Example */

/* Auxiliary routine: printing a matrix */
void print_matrix( char* desc, MKL_INT m, MKL_INT n, MKL_Complex16* a, MKL_INT lda ) {
        MKL_INT i, j;
        printf( "\n %s\n", desc );
        for( i = 0; i < m; i++ ) {
                for( j = 0; j < n; j++ )
                        printf( " (%6.2f,%6.2f)", a[i*lda+j].real, a[i*lda+j].imag );
                printf( "\n" );
        }
}

/* Auxiliary routine: printing a real matrix */
void print_rmatrix( char* desc, MKL_INT m, MKL_INT n, double* a, MKL_INT lda ) {
        MKL_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*lda+j] );
                printf( "\n" );
        }
}