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  • 2022.1
  • 12/20/2021
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SSYEVD Example Program in Fortran

* 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 * ============================================================================= * * .. Parameters .. INTEGER N PARAMETER ( N = 5 ) INTEGER LDA PARAMETER ( LDA = N ) INTEGER LWMAX PARAMETER ( LWMAX = 1000 ) * * .. Local Scalars .. INTEGER INFO, LWORK, LIWORK * * .. Local Arrays .. INTEGER IWORK( LWMAX ) REAL A( LDA, N ), W( N ), WORK( LWMAX ) DATA A/ $ 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 $ / * * .. External Subroutines .. EXTERNAL SSYEVD EXTERNAL PRINT_MATRIX * * .. Intrinsic Functions .. INTRINSIC INT, MIN * * .. Executable Statements .. WRITE(*,*)'SSYEVD Example Program Results' * * Query the optimal workspace. * LWORK = -1 LIWORK = -1 CALL SSYEVD( 'Vectors', 'Upper', N, A, LDA, W, WORK, LWORK, $ IWORK, LIWORK, INFO ) LWORK = MIN( LWMAX, INT( WORK( 1 ) ) ) LIWORK = MIN( LWMAX, IWORK( 1 ) ) * * Solve eigenproblem. * CALL SSYEVD( 'Vectors', 'Upper', N, A, LDA, W, WORK, LWORK, $ IWORK, LIWORK, INFO ) * * Check for convergence. * IF( INFO.GT.0 ) THEN WRITE(*,*)'The algorithm failed to compute eigenvalues.' STOP END IF * * Print eigenvalues. * CALL PRINT_MATRIX( 'Eigenvalues', 1, N, W, 1 ) * * Print eigenvectors. * CALL PRINT_MATRIX( 'Eigenvectors (stored columnwise)', N, N, A, $ LDA ) STOP END * * End of SSYEVD Example. * * ============================================================================= * * Auxiliary routine: printing a matrix. * SUBROUTINE PRINT_MATRIX( DESC, M, N, A, LDA ) CHARACTER*(*) DESC INTEGER M, N, LDA REAL A( LDA, * ) * INTEGER I, J * WRITE(*,*) WRITE(*,*) DESC DO I = 1, M WRITE(*,9998) ( A( I, J ), J = 1, N ) END DO * 9998 FORMAT( 11(:,1X,F6.2) ) RETURN END

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