static void apply( const tensor_type & tensor ,
const MatrixValue * const a ,
const VectorValue * const x ,
VectorValue * const y )
{
// const int max_size = 10;
// MatrixValue ax[max_size][max_size];
const size_type nBlock = tensor.num_coord();
// Loop over coordinate blocks
size_type value_entry = 0;
for ( size_type block = 0; block < nBlock; ++block) {
const size_type i_begin = tensor.get_i_begin(block);
const size_type j_begin = tensor.get_j_begin(block);
const size_type k_begin = tensor.get_k_begin(block);
const size_type i_size = tensor.get_i_size(block);
const size_type j_size = tensor.get_j_size(block);
const size_type k_size = tensor.get_k_size(block);
VectorValue * const y_block = y + i_begin;
const MatrixValue * const a_block = a + j_begin;
const VectorValue * const x_block = x + k_begin;
// // Precompute a*x outer product
// for (size_type j=0; j<j_size; ++j) {
// for (size_type k=0; k<k_size; ++k) {
// ax[j][k] = a_block[j]*x_block[k];
// }
// }
/*
// Compute y_i = \sum_{j,k} c_{ijk} * a_j * x_k
for (size_type i=0; i<i_size; ++i) {
VectorValue ytmp = 0;
for (size_type j=0; j<j_size; ++j) {
const size_type imj = i-j;
const size_type ipj = i+j+1;
const size_type k_beg = 0 <= imj ? imj : -imj;
const size_type k_end = k_size <= ipj ? k_size : ipj;
const size_type k0 = k_beg % 2 == (i+j) % 2 ? k_beg : k_beg+1;
for (size_type k=k0; k<k_end; ++k) {
//ytmp += tensor.value(value_entry++) * ax[j][k];
ytmp += tensor.value(value_entry++) * ( a_block[j] * x_block[k] );
}
}
y_block[i] += ytmp ;
}
*/
// Compute y_i = \sum_{j,k} c_{ijk} * a_j * x_k
for (size_type i=0; i<i_size; ++i) {
VectorValue ytmp = 0;
for (size_type j=0; j<j_size; ++j) {
for (size_type k=((i+j)%2); k<k_size; k+=2) {
ytmp += tensor.value(value_entry++) * ( a_block[j] * x_block[k] );
}
}
y_block[i] += ytmp ;
}
}
}
