SelfAdjointEigenSolver_MKL.h
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27  ********************************************************************************
28  * Content : Eigen bindings to Intel(R) MKL
29  * Self-adjoint eigenvalues/eigenvectors.
30  ********************************************************************************
31 */
32 
33 #ifndef EIGEN_SAEIGENSOLVER_MKL_H
34 #define EIGEN_SAEIGENSOLVER_MKL_H
35 
37 
38 namespace Eigen {
39 
42 #define EIGEN_MKL_EIG_SELFADJ(EIGTYPE, MKLTYPE, MKLRTYPE, MKLNAME, EIGCOLROW, MKLCOLROW ) \
43 template<> inline \
44 SelfAdjointEigenSolver<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >& \
45 SelfAdjointEigenSolver<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >::compute(const Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW>& matrix, int options) \
46 { \
47  eigen_assert(matrix.cols() == matrix.rows()); \
48  eigen_assert((options&~(EigVecMask|GenEigMask))==0 \
49  && (options&EigVecMask)!=EigVecMask \
50  && "invalid option parameter"); \
51  bool computeEigenvectors = (options&ComputeEigenvectors)==ComputeEigenvectors; \
52  lapack_int n = matrix.cols(), lda, matrix_order, info; \
53  m_eivalues.resize(n,1); \
54  m_subdiag.resize(n-1); \
55  m_eivec = matrix; \
56 \
57  if(n==1) \
58  { \
59  m_eivalues.coeffRef(0,0) = numext::real(matrix.coeff(0,0)); \
60  if(computeEigenvectors) m_eivec.setOnes(n,n); \
61  m_info = Success; \
62  m_isInitialized = true; \
63  m_eigenvectorsOk = computeEigenvectors; \
64  return *this; \
65  } \
66 \
67  lda = matrix.outerStride(); \
68  matrix_order=MKLCOLROW; \
69  char jobz, uplo='L'/*, range='A'*/; \
70  jobz = computeEigenvectors ? 'V' : 'N'; \
71 \
72  info = LAPACKE_##MKLNAME( matrix_order, jobz, uplo, n, (MKLTYPE*)m_eivec.data(), lda, (MKLRTYPE*)m_eivalues.data() ); \
73  m_info = (info==0) ? Success : NoConvergence; \
74  m_isInitialized = true; \
75  m_eigenvectorsOk = computeEigenvectors; \
76  return *this; \
77 }
78 
79 
80 EIGEN_MKL_EIG_SELFADJ(double, double, double, dsyev, ColMajor, LAPACK_COL_MAJOR)
81 EIGEN_MKL_EIG_SELFADJ(float, float, float, ssyev, ColMajor, LAPACK_COL_MAJOR)
82 EIGEN_MKL_EIG_SELFADJ(dcomplex, MKL_Complex16, double, zheev, ColMajor, LAPACK_COL_MAJOR)
83 EIGEN_MKL_EIG_SELFADJ(scomplex, MKL_Complex8, float, cheev, ColMajor, LAPACK_COL_MAJOR)
84 
85 EIGEN_MKL_EIG_SELFADJ(double, double, double, dsyev, RowMajor, LAPACK_ROW_MAJOR)
86 EIGEN_MKL_EIG_SELFADJ(float, float, float, ssyev, RowMajor, LAPACK_ROW_MAJOR)
87 EIGEN_MKL_EIG_SELFADJ(dcomplex, MKL_Complex16, double, zheev, RowMajor, LAPACK_ROW_MAJOR)
88 EIGEN_MKL_EIG_SELFADJ(scomplex, MKL_Complex8, float, cheev, RowMajor, LAPACK_ROW_MAJOR)
89 
90 } // end namespace Eigen
91 
92 #endif // EIGEN_SAEIGENSOLVER_H
#define EIGEN_MKL_EIG_SELFADJ(EIGTYPE, MKLTYPE, MKLRTYPE, MKLNAME, EIGCOLROW, MKLCOLROW)
iterative scaling algorithm to equilibrate rows and column norms in matrices
Definition: matrix.hpp:471


acado
Author(s): Milan Vukov, Rien Quirynen
autogenerated on Mon Jun 10 2019 12:35:04