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  • 12/20/2021
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LAPACKE_dsyevx 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_dsyevx Example. ======================= Program computes the smallest eigenvalues and the corresponding eigenvectors of a real symmetric matrix A: 6.29 -0.39 0.61 1.18 -0.08 -0.39 7.19 0.81 1.19 -0.08 0.61 0.81 5.48 -3.13 0.22 1.18 1.19 -3.13 3.79 -0.26 -0.08 -0.08 0.22 -0.26 0.83 Description. ============ The routine computes selected 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. Eigenvalues and eigenvectors can be selected by specifying either a range of values or a range of indices for the desired eigenvalues. Example Program Results. ======================== LAPACKE_dsyevx (column-major, high-level) Example Program Results The total number of eigenvalues found: 3 Selected eigenvalues 0.71 0.82 6.58 Selected eigenvectors (stored columnwise) 0.22 0.09 -0.95 0.21 0.08 -0.04 -0.52 -0.22 -0.29 -0.73 -0.21 -0.09 -0.32 0.94 0.01 */ #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 NSELECT 3 #define LDA N #define LDZ N /* Main program */ int main() { /* Locals */ MKL_INT n = N, il, iu, m, lda = LDA, ldz = LDZ, info; double abstol, vl, vu; /* Local arrays */ MKL_INT ifail[N]; double w[N], z[LDZ*NSELECT]; double a[LDA*N] = { 6.29, 0.00, 0.00, 0.00, 0.00, -0.39, 7.19, 0.00, 0.00, 0.00, 0.61, 0.81, 5.48, 0.00, 0.00, 1.18, 1.19, -3.13, 3.79, 0.00, -0.08, -0.08, 0.22, -0.26, 0.83 }; /* Executable statements */ printf( "LAPACKE_dsyevx (column-major, high-level) Example Program Results\n" ); /* Negative abstol means using the default value */ abstol = -1.0; /* Set il, iu to compute NSELECT smallest eigenvalues */ il = 1; iu = NSELECT; /* Solve eigenproblem */ info = LAPACKE_dsyevx( LAPACK_COL_MAJOR, 'V', 'I', 'U', n, a, lda, vl, vu, il, iu, abstol, &m, w, z, ldz, ifail ); /* Check for convergence */ if( info > 0 ) { printf( "The algorithm failed to compute eigenvalues.\n" ); exit( 1 ); } /* Print the number of eigenvalues found */ printf( "\n The total number of eigenvalues found:%2i\n", m ); /* Print eigenvalues */ print_matrix( "Selected eigenvalues", 1, m, w, 1 ); /* Print eigenvectors */ print_matrix( "Selected eigenvectors (stored columnwise)", n, m, z, ldz ); exit( 0 ); } /* End of LAPACKE_dsyevx 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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