typename _matmul_rvalue<A,B>::type
operator * (A const & a, B const & b) {
if (second_dim(a) != first_dim(b)) TRIQS_RUNTIME_ERROR<< "Matrix product : dimension mismatch in A*B "<< a<<" "<< b;
auto R = typename _matmul_rvalue<A,B>::type( first_dim(a), second_dim(b));
blas::gemm(1.0,a, b, 0.0, R);
return R;
}
typename _mat_vec_mul_rvalue<M,V>::type
operator * (M const & m, V const & v) {
if (second_dim(m) != v.size()) TRIQS_RUNTIME_ERROR<< "Matrix product : dimension mismatch in Matrix*Vector "<< m<<" "<< v;
auto R = typename _mat_vec_mul_rvalue<M,V>::type(first_dim(m));
blas::gemv(1.0,m,v,0.0,R);
return R;
}
typename std::enable_if<is_blas_lapack_type<typename VTX::value_type>::value && have_same_value_type<VTX, VTY, MT>::value>::type
ger(typename VTX::value_type alpha, VTX const &X, VTY const &Y, MT &A) {
static_assert(is_amv_value_or_view_class<MT>::value, "ger : A must be a matrix or a matrix_view");
if ((first_dim(A) != Y.size()) || (second_dim(A) != X.size()))
TRIQS_RUNTIME_ERROR << "Dimension mismatch in ger : A : " << get_shape(A()) << " while X : " << get_shape(X()) << " and Y : " << get_shape(Y());
const_qcache<VTX> Cx(X); // mettre la condition a la main
const_qcache<VTY> Cy(Y); // mettre la condition a la main
reflexive_qcache<MT> Ca(A);
if (Ca().memory_layout_is_c()) // tA += alpha y tx
f77::ger(get_n_rows(Ca()), get_n_cols(Ca()), alpha, Cy().data_start(), Cy().stride(), Cx().data_start(), Cx().stride(), Ca().data_start(),
get_ld(Ca()));
else
f77::ger(get_n_rows(Ca()), get_n_cols(Ca()), alpha, Cx().data_start(), Cx().stride(), Cy().data_start(), Cy().stride(), Ca().data_start(),
get_ld(Ca()));
/* std::cerr << " Meme labout C"<< Ca().memory_layout_is_c() << " "<<A.memory_layout_is_c()<<std::endl ;
std::cerr<< " has_contiguous_data(A) : "<< has_contiguous_data(A) << std::endl;
std::cerr<< Ca()<< std::endl;
std::cerr<< Ca()(0,0) << " "<< Ca()(1,0) << " "<< Ca()(0,1) << " "<< Ca()(1,1) << " "<< std::endl;
std::cerr<< Ca().data_start()[0]<< " "<< Ca().data_start()[1]<< " "<< Ca().data_start()[2]<< " " << Ca().data_start()[3]<< " "<<std::endl;
std::cerr<< A<< std::endl;
std::cerr<< A(0,0) << " "<< A(1,0) << " "<< A(0,1) << " "<< A(1,1) << " "<< std::endl;
std::cerr<< A.data_start()[0]<< " "<< A.data_start()[1]<< " "<< A.data_start()[2]<< " " << A.data_start()[3]<< " "<<std::endl;
*/
}
// returns the # of cols of the matrix *seen* as fortran matrix
template <typename MatrixType> int get_n_cols (MatrixType const & A) {
return (A.memory_layout_is_fortran() ? second_dim(A) : first_dim(A));
}
det_and_inverse_worker (ViewType const & a): V(a), dim(first_dim(a)), ipiv(dim), step(0) {
if (first_dim(a)!=second_dim(a))
TRIQS_RUNTIME_ERROR<<"Inverse/Det error : non-square matrix. Dimensions are : ("<<first_dim(a)<<","<<second_dim(a)<<")"<<"\n ";
if (!(has_contiguous_data(a))) TRIQS_RUNTIME_ERROR<<"det_and_inverse_worker only takes a contiguous view";
}
template <typename AA> inverse_lazy(AA &&a_) : a(std::forward<AA>(a_)), M{}, computed{false} {
if (first_dim(a) != second_dim(a))
TRIQS_RUNTIME_ERROR << "Inverse : matrix is not square but of size " << first_dim(a) << " x " << second_dim(a);
}