source: branches/HeuristicLab.Problems.GaussianProcessTuning/HeuristicLab.Eigen/Eigen/src/SVD/JacobiSVD_MKL.h @ 9562

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 ********************************************************************************
 *   Content : Eigen bindings to Intel(R) MKL
 *    Singular Value Decomposition - SVD.
 ********************************************************************************
*/

#ifndef EIGEN_JACOBISVD_MKL_H
#define EIGEN_JACOBISVD_MKL_H

#include "Eigen/src/Core/util/MKL_support.h"

namespace Eigen { 

/** \internal Specialization for the data types supported by MKL */

#define EIGEN_MKL_SVD(EIGTYPE, MKLTYPE, MKLRTYPE, MKLPREFIX, EIGCOLROW, MKLCOLROW) \
template<> inline \
JacobiSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, ColPivHouseholderQRPreconditioner>& \
JacobiSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, ColPivHouseholderQRPreconditioner>::compute(const Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>& matrix, unsigned int computationOptions) \
{ \
  typedef Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic> MatrixType; \
  typedef MatrixType::Scalar Scalar; \
  typedef MatrixType::RealScalar RealScalar; \
  allocate(matrix.rows(), matrix.cols(), computationOptions); \
\
  /*const RealScalar precision = RealScalar(2) * NumTraits<Scalar>::epsilon();*/ \
  m_nonzeroSingularValues = m_diagSize; \
\
  lapack_int lda = matrix.outerStride(), ldu, ldvt; \
  lapack_int matrix_order = MKLCOLROW; \
  char jobu, jobvt; \
  MKLTYPE *u, *vt, dummy; \
  jobu  = (m_computeFullU) ? 'A' : (m_computeThinU) ? 'S' : 'N'; \
  jobvt = (m_computeFullV) ? 'A' : (m_computeThinV) ? 'S' : 'N'; \
  if (computeU()) { \
    ldu  = m_matrixU.outerStride(); \
    u    = (MKLTYPE*)m_matrixU.data(); \
  } else { ldu=1; u=&dummy; }\
  MatrixType localV; \
  ldvt = (m_computeFullV) ? m_cols : (m_computeThinV) ? m_diagSize : 1; \
  if (computeV()) { \
    localV.resize(ldvt, m_cols); \
    vt   = (MKLTYPE*)localV.data(); \
  } else { ldvt=1; vt=&dummy; }\
  Matrix<MKLRTYPE, Dynamic, Dynamic> superb; superb.resize(m_diagSize, 1); \
  MatrixType m_temp; m_temp = matrix; \
  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()); \
  if (computeV()) m_matrixV = localV.adjoint(); \
 /* for(int i=0;i<m_diagSize;i++) if (m_singularValues.coeffRef(i) < precision) { m_nonzeroSingularValues--; m_singularValues.coeffRef(i)=RealScalar(0);}*/ \
  m_isInitialized = true; \
  return *this; \
}

EIGEN_MKL_SVD(double,   double,        double, d, ColMajor, LAPACK_COL_MAJOR)
EIGEN_MKL_SVD(float,    float,         float , s, ColMajor, LAPACK_COL_MAJOR)
EIGEN_MKL_SVD(dcomplex, MKL_Complex16, double, z, ColMajor, LAPACK_COL_MAJOR)
EIGEN_MKL_SVD(scomplex, MKL_Complex8,  float , c, ColMajor, LAPACK_COL_MAJOR)

EIGEN_MKL_SVD(double,   double,        double, d, RowMajor, LAPACK_ROW_MAJOR)
EIGEN_MKL_SVD(float,    float,         float , s, RowMajor, LAPACK_ROW_MAJOR)
EIGEN_MKL_SVD(dcomplex, MKL_Complex16, double, z, RowMajor, LAPACK_ROW_MAJOR)
EIGEN_MKL_SVD(scomplex, MKL_Complex8,  float , c, RowMajor, LAPACK_ROW_MAJOR)

} // end namespace Eigen

#endif // EIGEN_JACOBISVD_MKL_H