27   #define BOOST_UBLAS_NDEBUG 
   36 #define VIENNACL_WITH_UBLAS 
   50 #include <boost/numeric/ublas/matrix.hpp> 
   51 #include <boost/numeric/ublas/matrix_proxy.hpp> 
   52 #include <boost/numeric/ublas/matrix_expression.hpp> 
   53 #include <boost/numeric/ublas/matrix_sparse.hpp> 
   54 #include <boost/numeric/ublas/vector.hpp> 
   55 #include <boost/numeric/ublas/operation.hpp> 
   56 #include <boost/numeric/ublas/vector_expression.hpp> 
   70   boost::numeric::ublas::compressed_matrix<ScalarType> ublas_A;
 
   74     std::cout << 
"Error reading Matrix file" << std::endl;
 
   90   std::cout << 
"Starting computation of eigenvalue with largest modulus (might take about a minute)..." << std::endl;
 
   91   std::cout << 
"Result of power iteration with ublas matrix (single-threaded): " << 
viennacl::linalg::eig(ublas_A, ptag) << std::endl;
 
   92   std::cout << 
"Result of power iteration with ViennaCL (OpenCL accelerated): " << 
viennacl::linalg::eig(vcl_A, ptag) << std::endl;
 
  100   std::cout << 
"First three entries in eigenvector: " << eigenvector[0] << 
" " << eigenvector[1] << 
" " << eigenvector[2] << std::endl;
 
  102   std::cout << 
"First three entries in A*eigenvector: " << Ax[0] << 
" " << Ax[1] << 
" " << Ax[2] << std::endl;
 
A reader and writer for the matrix market format is implemented here. 
std::vector< typename viennacl::result_of::cpu_value_type< typename MatrixT::value_type >::type > eig(MatrixT const &matrix, DenseMatrixT &eigenvectors_A, lanczos_tag const &tag, bool compute_eigenvectors=true)
Implementation of the calculation of eigenvalues using lanczos (with and without reorthogonalization)...
A tag for the power iteration algorithm. 
VectorT prod(std::vector< std::vector< T, A1 >, A2 > const &matrix, VectorT const &vector)
Implementation of the compressed_matrix class. 
Defines a tag for the configuration of the power iteration method. 
The vector type with operator-overloads and proxy classes is defined here. Linear algebra operations ...
void copy(std::vector< NumericT > &cpu_vec, circulant_matrix< NumericT, AlignmentV > &gpu_mat)
Copies a circulant matrix from the std::vector to the OpenCL device (either GPU or multi-core CPU) ...
A sparse square matrix in compressed sparse rows format. 
long read_matrix_market_file(MatrixT &mat, const char *file, long index_base=1)
Reads a sparse matrix from a file (MatrixMarket format) 
Implementation of the ViennaCL scalar class.