JacobiSVD_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  *    Singular Value Decomposition - SVD.
00030  ********************************************************************************
00031 */
00032 
00033 #ifndef EIGEN_JACOBISVD_MKL_H
00034 #define EIGEN_JACOBISVD_MKL_H
00035 
00036 #include "Eigen/src/Core/util/MKL_support.h"
00037 
00038 namespace Eigen { 
00039 
00042 #define EIGEN_MKL_SVD(EIGTYPE, MKLTYPE, MKLRTYPE, MKLPREFIX, EIGCOLROW, MKLCOLROW) \
00043 template<> inline \
00044 JacobiSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, ColPivHouseholderQRPreconditioner>& \
00045 JacobiSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, ColPivHouseholderQRPreconditioner>::compute(const Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>& matrix, unsigned int computationOptions) \
00046 { \
00047   typedef Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic> MatrixType; \
00048   typedef MatrixType::Scalar Scalar; \
00049   typedef MatrixType::RealScalar RealScalar; \
00050   allocate(matrix.rows(), matrix.cols(), computationOptions); \
00051 \
00052   /*const RealScalar precision = RealScalar(2) * NumTraits<Scalar>::epsilon();*/ \
00053   m_nonzeroSingularValues = m_diagSize; \
00054 \
00055   lapack_int lda = matrix.outerStride(), ldu, ldvt; \
00056   lapack_int matrix_order = MKLCOLROW; \
00057   char jobu, jobvt; \
00058   MKLTYPE *u, *vt, dummy; \
00059   jobu  = (m_computeFullU) ? 'A' : (m_computeThinU) ? 'S' : 'N'; \
00060   jobvt = (m_computeFullV) ? 'A' : (m_computeThinV) ? 'S' : 'N'; \
00061   if (computeU()) { \
00062     ldu  = m_matrixU.outerStride(); \
00063     u    = (MKLTYPE*)m_matrixU.data(); \
00064   } else { ldu=1; u=&dummy; }\
00065   MatrixType localV; \
00066   ldvt = (m_computeFullV) ? m_cols : (m_computeThinV) ? m_diagSize : 1; \
00067   if (computeV()) { \
00068     localV.resize(ldvt, m_cols); \
00069     vt   = (MKLTYPE*)localV.data(); \
00070   } else { ldvt=1; vt=&dummy; }\
00071   Matrix<MKLRTYPE, Dynamic, Dynamic> superb; superb.resize(m_diagSize, 1); \
00072   MatrixType m_temp; m_temp = matrix; \
00073   LAPACKE_##MKLPREFIX##gesvd( matrix_order, jobu, jobvt, m_rows, m_cols, (MKLTYPE*)m_temp.data(), lda, (MKLRTYPE*)m_singularValues.data(), u, ldu, vt, ldvt, superb.data()); \
00074   if (computeV()) m_matrixV = localV.adjoint(); \
00075  /* for(int i=0;i<m_diagSize;i++) if (m_singularValues.coeffRef(i) < precision) { m_nonzeroSingularValues--; m_singularValues.coeffRef(i)=RealScalar(0);}*/ \
00076   m_isInitialized = true; \
00077   return *this; \
00078 }
00079 
00080 EIGEN_MKL_SVD(double,   double,        double, d, ColMajor, LAPACK_COL_MAJOR)
00081 EIGEN_MKL_SVD(float,    float,         float , s, ColMajor, LAPACK_COL_MAJOR)
00082 EIGEN_MKL_SVD(dcomplex, MKL_Complex16, double, z, ColMajor, LAPACK_COL_MAJOR)
00083 EIGEN_MKL_SVD(scomplex, MKL_Complex8,  float , c, ColMajor, LAPACK_COL_MAJOR)
00084 
00085 EIGEN_MKL_SVD(double,   double,        double, d, RowMajor, LAPACK_ROW_MAJOR)
00086 EIGEN_MKL_SVD(float,    float,         float , s, RowMajor, LAPACK_ROW_MAJOR)
00087 EIGEN_MKL_SVD(dcomplex, MKL_Complex16, double, z, RowMajor, LAPACK_ROW_MAJOR)
00088 EIGEN_MKL_SVD(scomplex, MKL_Complex8,  float , c, RowMajor, LAPACK_ROW_MAJOR)
00089 
00090 } // end namespace Eigen
00091 
00092 #endif // EIGEN_JACOBISVD_MKL_H


win_eigen
Author(s): Daniel Stonier
autogenerated on Wed Sep 16 2015 07:11:02