Visible to Intel only — GUID: GUID-AC75F42C-F55F-41BB-963D-1205A04AC5F5
LAPACKE_ssyevd 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_ssyevd Example.
=======================
Program computes all eigenvalues and eigenvectors of a real symmetric
matrix A using divide and conquer algorithm, where A is:
6.39 0.13 -8.23 5.71 -3.18
0.13 8.37 -4.46 -6.10 7.21
-8.23 -4.46 -9.58 -9.25 -7.42
5.71 -6.10 -9.25 3.72 8.54
-3.18 7.21 -7.42 8.54 2.51
Description.
============
The routine computes all eigenvalues and, optionally, eigenvectors of an
n-by-n real symmetric matrix A. The eigenvector v(j) of A satisfies
A*v(j) = lambda(j)*v(j)
where lambda(j) is its eigenvalue. The computed eigenvectors are
orthonormal.
If the eigenvectors are requested, then this routine uses a divide and
conquer algorithm to compute eigenvalues and eigenvectors.
Example Program Results.
========================
LAPACKE_ssyevd (row-major, high-level) Example Program Results
Eigenvalues
-17.44 -11.96 6.72 14.25 19.84
Eigenvectors (stored columnwise)
-0.26 0.31 -0.74 0.33 0.42
-0.17 -0.39 -0.38 -0.80 0.16
-0.89 0.04 0.09 0.03 -0.45
-0.29 -0.59 0.34 0.31 0.60
-0.19 0.63 0.44 -0.38 0.48
*/
#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, float* a, MKL_INT lda );
/* Parameters */
#define N 5
#define LDA N
/* Main program */
int main() {
/* Locals */
MKL_INT n = N, lda = LDA, info;
/* Local arrays */
float w[N];
float a[LDA*N] = {
6.39f, 0.13f, -8.23f, 5.71f, -3.18f,
0.00f, 8.37f, -4.46f, -6.10f, 7.21f,
0.00f, 0.00f, -9.58f, -9.25f, -7.42f,
0.00f, 0.00f, 0.00f, 3.72f, 8.54f,
0.00f, 0.00f, 0.00f, 0.00f, 2.51f
};
/* Executable statements */
printf( "LAPACKE_ssyevd (row-major, high-level) Example Program Results\n" );
/* Solve eigenproblem */
info = LAPACKE_ssyevd( LAPACK_ROW_MAJOR, 'V', 'U', n, a, lda, w );
/* Check for convergence */
if( info > 0 ) {
printf( "The algorithm failed to compute eigenvalues.\n" );
exit( 1 );
}
/* Print eigenvalues */
print_matrix( "Eigenvalues", 1, n, w, 1 );
/* Print eigenvectors */
print_matrix( "Eigenvectors (stored columnwise)", n, n, a, lda );
exit( 0 );
} /* End of LAPACKE_ssyevd Example */
/* Auxiliary routine: printing a matrix */
void print_matrix( char* desc, MKL_INT m, MKL_INT n, float* 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" );
}
}
Parent topic: SSYEVD Example