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  • 12/20/2021
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LAPACKE_zheev 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_zheev Example. ====================== Program computes all eigenvalues and eigenvectors of a complex Hermitian matrix A: ( 9.14, 0.00) ( -4.37, -9.22) ( -1.98, -1.72) ( -8.96, -9.50) ( -4.37, 9.22) ( -3.35, 0.00) ( 2.25, -9.51) ( 2.57, 2.40) ( -1.98, 1.72) ( 2.25, 9.51) ( -4.82, 0.00) ( -3.24, 2.04) ( -8.96, 9.50) ( 2.57, -2.40) ( -3.24, -2.04) ( 8.44, 0.00) Description. ============ The routine computes all eigenvalues and, optionally, eigenvectors of an n-by-n complex Hermitian 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. Example Program Results. ======================== LAPACKE_zheev (row-major, high-level) Example Program Results Eigenvalues -16.00 -6.76 6.67 25.51 Eigenvectors (stored columnwise) ( 0.34, 0.00) ( -0.55, 0.00) ( 0.31, 0.00) ( -0.70, 0.00) ( 0.44, -0.54) ( 0.26, 0.18) ( 0.45, 0.29) ( 0.22, -0.28) ( -0.48, -0.37) ( -0.52, -0.02) ( -0.05, 0.57) ( 0.15, 0.08) ( 0.10, -0.12) ( -0.50, 0.28) ( -0.23, -0.48) ( 0.34, -0.49) */ #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, MKL_Complex16* a, MKL_INT lda ); extern void print_rmatrix( char* desc, MKL_INT m, MKL_INT n, double* a, MKL_INT lda ); /* Parameters */ #define N 4 #define LDA N /* Main program */ int main() { /* Locals */ MKL_INT n = N, lda = LDA, info; /* Local arrays */ double w[N]; MKL_Complex16 a[LDA*N] = { { 9.14, 0.00}, { 0.00, 0.00}, { 0.00, 0.00}, { 0.00, 0.00}, {-4.37, 9.22}, {-3.35, 0.00}, { 0.00, 0.00}, { 0.00, 0.00}, {-1.98, 1.72}, { 2.25, 9.51}, {-4.82, 0.00}, { 0.00, 0.00}, {-8.96, 9.50}, { 2.57, -2.40}, {-3.24, -2.04}, { 8.44, 0.00} }; /* Executable statements */ printf( "LAPACKE_zheev (row-major, high-level) Example Program Results\n" ); /* Solve eigenproblem */ info = LAPACKE_zheev( LAPACK_ROW_MAJOR, 'V', 'L', n, a, lda, w ); /* Check for convergence */ if( info > 0 ) { printf( "The algorithm failed to compute eigenvalues.\n" ); exit( 1 ); } /* Print eigenvalues */ print_rmatrix( "Eigenvalues", 1, n, w, 1 ); /* Print eigenvectors */ print_matrix( "Eigenvectors (stored columnwise)", n, n, a, lda ); exit( 0 ); } /* End of LAPACKE_zheev Example */ /* Auxiliary routine: printing a matrix */ void print_matrix( char* desc, MKL_INT m, MKL_INT n, MKL_Complex16* 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,%6.2f)", a[i*lda+j].real, a[i*lda+j].imag ); printf( "\n" ); } } /* Auxiliary routine: printing a real matrix */ void print_rmatrix( 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*lda+j] ); printf( "\n" ); } }

Product and Performance Information

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