RealSchur_MKL.h
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00001 /*
00002  Copyright (c) 2011, Intel Corporation. All rights reserved.
00003 
00004  Redistribution and use in source and binary forms, with or without modification,
00005  are permitted provided that the following conditions are met:
00006 
00007  * Redistributions of source code must retain the above copyright notice, this
00008    list of conditions and the following disclaimer.
00009  * Redistributions in binary form must reproduce the above copyright notice,
00010    this list of conditions and the following disclaimer in the documentation
00011    and/or other materials provided with the distribution.
00012  * Neither the name of Intel Corporation nor the names of its contributors may
00013    be used to endorse or promote products derived from this software without
00014    specific prior written permission.
00015 
00016  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
00017  ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
00018  WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
00019  DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
00020  ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
00021  (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
00022  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
00023  ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
00024  (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
00025  SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
00026 
00027  ********************************************************************************
00028  *   Content : Eigen bindings to Intel(R) MKL
00029  *    Real Schur needed to real unsymmetrical eigenvalues/eigenvectors.
00030  ********************************************************************************
00031 */
00032 
00033 #ifndef EIGEN_REAL_SCHUR_MKL_H
00034 #define EIGEN_REAL_SCHUR_MKL_H
00035 
00036 #include "Eigen/src/Core/util/MKL_support.h"
00037 
00038 namespace Eigen { 
00039 
00042 #define EIGEN_MKL_SCHUR_REAL(EIGTYPE, MKLTYPE, MKLPREFIX, MKLPREFIX_U, EIGCOLROW, MKLCOLROW) \
00043 template<> inline \
00044 RealSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >& \
00045 RealSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >::compute(const Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW>& matrix, bool computeU) \
00046 { \
00047   typedef Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> MatrixType; \
00048   typedef MatrixType::Scalar Scalar; \
00049   typedef MatrixType::RealScalar RealScalar; \
00050 \
00051   eigen_assert(matrix.cols() == matrix.rows()); \
00052 \
00053   lapack_int n = matrix.cols(), sdim, info; \
00054   lapack_int lda = matrix.outerStride(); \
00055   lapack_int matrix_order = MKLCOLROW; \
00056   char jobvs, sort='N'; \
00057   LAPACK_##MKLPREFIX_U##_SELECT2 select = 0; \
00058   jobvs = (computeU) ? 'V' : 'N'; \
00059   m_matU.resize(n, n); \
00060   lapack_int ldvs  = m_matU.outerStride(); \
00061   m_matT = matrix; \
00062   Matrix<EIGTYPE, Dynamic, Dynamic> wr, wi; \
00063   wr.resize(n, 1); wi.resize(n, 1); \
00064   info = LAPACKE_##MKLPREFIX##gees( matrix_order, jobvs, sort, select, n, (MKLTYPE*)m_matT.data(), lda, &sdim, (MKLTYPE*)wr.data(), (MKLTYPE*)wi.data(), (MKLTYPE*)m_matU.data(), ldvs ); \
00065   if(info == 0) \
00066     m_info = Success; \
00067   else \
00068     m_info = NoConvergence; \
00069 \
00070   m_isInitialized = true; \
00071   m_matUisUptodate = computeU; \
00072   return *this; \
00073 \
00074 }
00075 
00076 EIGEN_MKL_SCHUR_REAL(double,   double, d, D, ColMajor, LAPACK_COL_MAJOR)
00077 EIGEN_MKL_SCHUR_REAL(float,    float,  s, S, ColMajor, LAPACK_COL_MAJOR)
00078 EIGEN_MKL_SCHUR_REAL(double,   double, d, D, RowMajor, LAPACK_ROW_MAJOR)
00079 EIGEN_MKL_SCHUR_REAL(float,    float,  s, S, RowMajor, LAPACK_ROW_MAJOR)
00080 
00081 } // end namespace Eigen
00082 
00083 #endif // EIGEN_REAL_SCHUR_MKL_H


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autogenerated on Thu Jun 6 2019 20:59:32