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  • 12/20/2021
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LAPACKE_dsyevr 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_dsyevr Example. ======================= Program computes the smallest eigenvalues and the corresponding eigenvectors of a real symmetric matrix A using the Relatively Robust Representations, where A is: 0.67 -0.20 0.19 -1.06 0.46 -0.20 3.82 -0.13 1.06 -0.48 0.19 -0.13 3.27 0.11 1.10 -1.06 1.06 0.11 5.86 -0.98 0.46 -0.48 1.10 -0.98 3.54 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_dsyevr (column-major, high-level) Example Program Results The total number of eigenvalues found: 3 Selected eigenvalues 0.43 2.14 3.37 Selected eigenvectors (stored columnwise) -0.98 -0.01 -0.08 0.01 0.02 -0.93 0.04 -0.69 -0.07 -0.18 0.19 0.31 0.07 0.69 -0.13 */ #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 isuppz[N]; double w[N], z[LDZ*NSELECT]; double a[LDA*N] = { 0.67, 0.00, 0.00, 0.00, 0.00, -0.20, 3.82, 0.00, 0.00, 0.00, 0.19, -0.13, 3.27, 0.00, 0.00, -1.06, 1.06, 0.11, 5.86, 0.00, 0.46, -0.48, 1.10, -0.98, 3.54 }; /* Executable statements */ printf( "LAPACKE_dsyevr (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_dsyevr( LAPACK_COL_MAJOR, 'V', 'I', 'U', n, a, lda, vl, vu, il, iu, abstol, &m, w, z, ldz, isuppz ); /* 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_dsyevr 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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