Developer Reference

Intel® oneAPI Math Kernel Library LAPACK Examples

ID 766877
Date 10/31/2024
Public
Document Table of Contents

SSYEVD 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. * ******************************************************************************** */ /* 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. ======================== SSYEVD 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> /* SSYEVD prototype */ extern void ssyevd( char* jobz, char* uplo, int* n, float* a, int* lda, float* w, float* work, int* lwork, int* iwork, int* liwork, int* info ); /* Auxiliary routines prototypes */ extern void print_matrix( char* desc, int m, int n, float* a, int lda ); /* Parameters */ #define N 5 #define LDA N /* Main program */ int main() { /* Locals */ int n = N, lda = LDA, info, lwork, liwork; int iwkopt; int* iwork; float wkopt; float* work; /* Local arrays */ float w[N]; float a[LDA*N] = { 6.39f, 0.00f, 0.00f, 0.00f, 0.00f, 0.13f, 8.37f, 0.00f, 0.00f, 0.00f, -8.23f, -4.46f, -9.58f, 0.00f, 0.00f, 5.71f, -6.10f, -9.25f, 3.72f, 0.00f, -3.18f, 7.21f, -7.42f, 8.54f, 2.51f }; /* Executable statements */ printf( " SSYEVD Example Program Results\n" ); /* Query and allocate the optimal workspace */ lwork = -1; liwork = -1; ssyevd( "Vectors", "Upper", &n, a, &lda, w, &wkopt, &lwork, &iwkopt, &liwork, &info ); lwork = (int)wkopt; work = (float*)malloc( lwork*sizeof(float) ); liwork = iwkopt; iwork = (int*)malloc( liwork*sizeof(int) ); /* Solve eigenproblem */ ssyevd( "Vectors", "Upper", &n, a, &lda, w, work, &lwork, iwork, &liwork, &info ); /* 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 ); /* Free workspace */ free( (void*)iwork ); free( (void*)work ); exit( 0 ); } /* End of SSYEVD Example */ /* Auxiliary routine: printing a matrix */ void print_matrix( char* desc, int m, int n, float* 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" ); } }