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  • 2022.1
  • 12/20/2021
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LAPACKE_dsyevd Example Program in C for Column 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_dsyevd 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_dsyevd (column-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, double* 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 */ double w[N]; double a[LDA*N] = { 6.39, 0.00, 0.00, 0.00, 0.00, 0.13, 8.37, 0.00, 0.00, 0.00, -8.23, -4.46, -9.58, 0.00, 0.00, 5.71, -6.10, -9.25, 3.72, 0.00, -3.18, 7.21, -7.42, 8.54, 2.51 }; /* Executable statements */ printf( "LAPACKE_dsyevd (column-major, high-level) Example Program Results\n" ); /* Solve eigenproblem */ info = LAPACKE_dsyevd( LAPACK_COL_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_dsyevd Example */ /* Auxiliary routine: printing a matrix */ void print_matrix( 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+j*lda] ); printf( "\n" ); } }

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