Yoji KURODA / Eigen

Eigne Matrix Class Library

Dependents:   Eigen_test Odometry_test AttitudeEstimation_usingTicker MPU9250_Quaternion_Binary_Serial ... more

Eigen Matrix Class Library for mbed.

Finally, you can use Eigen on your mbed!!!

src/Eigenvalues/SelfAdjointEigenSolver_MKL.h@0:13a5d365ba16, 2016-10-13 (annotated)

Committer:
ykuroda
Date:
Thu Oct 13 04:07:23 2016 +0000
Revision:
0:13a5d365ba16
First commint, Eigne Matrix Class Library

Who changed what in which revision?

UserRevisionLine numberNew contents of line
ykuroda 0:13a5d365ba16 1 /*
ykuroda 0:13a5d365ba16 2 Copyright (c) 2011, Intel Corporation. All rights reserved.
ykuroda 0:13a5d365ba16 3
ykuroda 0:13a5d365ba16 4 Redistribution and use in source and binary forms, with or without modification,
ykuroda 0:13a5d365ba16 5 are permitted provided that the following conditions are met:
ykuroda 0:13a5d365ba16 6
ykuroda 0:13a5d365ba16 7 * Redistributions of source code must retain the above copyright notice, this
ykuroda 0:13a5d365ba16 8 list of conditions and the following disclaimer.
ykuroda 0:13a5d365ba16 9 * Redistributions in binary form must reproduce the above copyright notice,
ykuroda 0:13a5d365ba16 10 this list of conditions and the following disclaimer in the documentation
ykuroda 0:13a5d365ba16 11 and/or other materials provided with the distribution.
ykuroda 0:13a5d365ba16 12 * Neither the name of Intel Corporation nor the names of its contributors may
ykuroda 0:13a5d365ba16 13 be used to endorse or promote products derived from this software without
ykuroda 0:13a5d365ba16 14 specific prior written permission.
ykuroda 0:13a5d365ba16 15
ykuroda 0:13a5d365ba16 16 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ykuroda 0:13a5d365ba16 17 ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
ykuroda 0:13a5d365ba16 18 WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
ykuroda 0:13a5d365ba16 19 DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
ykuroda 0:13a5d365ba16 20 ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
ykuroda 0:13a5d365ba16 21 (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
ykuroda 0:13a5d365ba16 22 LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
ykuroda 0:13a5d365ba16 23 ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
ykuroda 0:13a5d365ba16 24 (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
ykuroda 0:13a5d365ba16 25 SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
ykuroda 0:13a5d365ba16 26
ykuroda 0:13a5d365ba16 27 ********************************************************************************
ykuroda 0:13a5d365ba16 28 * Content : Eigen bindings to Intel(R) MKL
ykuroda 0:13a5d365ba16 29 * Self-adjoint eigenvalues/eigenvectors.
ykuroda 0:13a5d365ba16 30 ********************************************************************************
ykuroda 0:13a5d365ba16 31 */
ykuroda 0:13a5d365ba16 32
ykuroda 0:13a5d365ba16 33 #ifndef EIGEN_SAEIGENSOLVER_MKL_H
ykuroda 0:13a5d365ba16 34 #define EIGEN_SAEIGENSOLVER_MKL_H
ykuroda 0:13a5d365ba16 35
ykuroda 0:13a5d365ba16 36 #include "Eigen/src/Core/util/MKL_support.h"
ykuroda 0:13a5d365ba16 37
ykuroda 0:13a5d365ba16 38 namespace Eigen {
ykuroda 0:13a5d365ba16 39
ykuroda 0:13a5d365ba16 40 /** \internal Specialization for the data types supported by MKL */
ykuroda 0:13a5d365ba16 41
ykuroda 0:13a5d365ba16 42 #define EIGEN_MKL_EIG_SELFADJ(EIGTYPE, MKLTYPE, MKLRTYPE, MKLNAME, EIGCOLROW, MKLCOLROW ) \
ykuroda 0:13a5d365ba16 43 template<> inline \
ykuroda 0:13a5d365ba16 44 SelfAdjointEigenSolver<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >& \
ykuroda 0:13a5d365ba16 45 SelfAdjointEigenSolver<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >::compute(const Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW>& matrix, int options) \
ykuroda 0:13a5d365ba16 46 { \
ykuroda 0:13a5d365ba16 47 eigen_assert(matrix.cols() == matrix.rows()); \
ykuroda 0:13a5d365ba16 48 eigen_assert((options&~(EigVecMask|GenEigMask))==0 \
ykuroda 0:13a5d365ba16 49 && (options&EigVecMask)!=EigVecMask \
ykuroda 0:13a5d365ba16 50 && "invalid option parameter"); \
ykuroda 0:13a5d365ba16 51 bool computeEigenvectors = (options&ComputeEigenvectors)==ComputeEigenvectors; \
ykuroda 0:13a5d365ba16 52 lapack_int n = matrix.cols(), lda, matrix_order, info; \
ykuroda 0:13a5d365ba16 53 m_eivalues.resize(n,1); \
ykuroda 0:13a5d365ba16 54 m_subdiag.resize(n-1); \
ykuroda 0:13a5d365ba16 55 m_eivec = matrix; \
ykuroda 0:13a5d365ba16 56 \
ykuroda 0:13a5d365ba16 57 if(n==1) \
ykuroda 0:13a5d365ba16 58 { \
ykuroda 0:13a5d365ba16 59 m_eivalues.coeffRef(0,0) = numext::real(matrix.coeff(0,0)); \
ykuroda 0:13a5d365ba16 60 if(computeEigenvectors) m_eivec.setOnes(n,n); \
ykuroda 0:13a5d365ba16 61 m_info = Success; \
ykuroda 0:13a5d365ba16 62 m_isInitialized = true; \
ykuroda 0:13a5d365ba16 63 m_eigenvectorsOk = computeEigenvectors; \
ykuroda 0:13a5d365ba16 64 return *this; \
ykuroda 0:13a5d365ba16 65 } \
ykuroda 0:13a5d365ba16 66 \
ykuroda 0:13a5d365ba16 67 lda = matrix.outerStride(); \
ykuroda 0:13a5d365ba16 68 matrix_order=MKLCOLROW; \
ykuroda 0:13a5d365ba16 69 char jobz, uplo='L'/*, range='A'*/; \
ykuroda 0:13a5d365ba16 70 jobz = computeEigenvectors ? 'V' : 'N'; \
ykuroda 0:13a5d365ba16 71 \
ykuroda 0:13a5d365ba16 72 info = LAPACKE_##MKLNAME( matrix_order, jobz, uplo, n, (MKLTYPE*)m_eivec.data(), lda, (MKLRTYPE*)m_eivalues.data() ); \
ykuroda 0:13a5d365ba16 73 m_info = (info==0) ? Success : NoConvergence; \
ykuroda 0:13a5d365ba16 74 m_isInitialized = true; \
ykuroda 0:13a5d365ba16 75 m_eigenvectorsOk = computeEigenvectors; \
ykuroda 0:13a5d365ba16 76 return *this; \
ykuroda 0:13a5d365ba16 77 }
ykuroda 0:13a5d365ba16 78
ykuroda 0:13a5d365ba16 79
ykuroda 0:13a5d365ba16 80 EIGEN_MKL_EIG_SELFADJ(double, double, double, dsyev, ColMajor, LAPACK_COL_MAJOR)
ykuroda 0:13a5d365ba16 81 EIGEN_MKL_EIG_SELFADJ(float, float, float, ssyev, ColMajor, LAPACK_COL_MAJOR)
ykuroda 0:13a5d365ba16 82 EIGEN_MKL_EIG_SELFADJ(dcomplex, MKL_Complex16, double, zheev, ColMajor, LAPACK_COL_MAJOR)
ykuroda 0:13a5d365ba16 83 EIGEN_MKL_EIG_SELFADJ(scomplex, MKL_Complex8, float, cheev, ColMajor, LAPACK_COL_MAJOR)
ykuroda 0:13a5d365ba16 84
ykuroda 0:13a5d365ba16 85 EIGEN_MKL_EIG_SELFADJ(double, double, double, dsyev, RowMajor, LAPACK_ROW_MAJOR)
ykuroda 0:13a5d365ba16 86 EIGEN_MKL_EIG_SELFADJ(float, float, float, ssyev, RowMajor, LAPACK_ROW_MAJOR)
ykuroda 0:13a5d365ba16 87 EIGEN_MKL_EIG_SELFADJ(dcomplex, MKL_Complex16, double, zheev, RowMajor, LAPACK_ROW_MAJOR)
ykuroda 0:13a5d365ba16 88 EIGEN_MKL_EIG_SELFADJ(scomplex, MKL_Complex8, float, cheev, RowMajor, LAPACK_ROW_MAJOR)
ykuroda 0:13a5d365ba16 89
ykuroda 0:13a5d365ba16 90 } // end namespace Eigen
ykuroda 0:13a5d365ba16 91
ykuroda 0:13a5d365ba16 92 #endif // EIGEN_SAEIGENSOLVER_H