/**
* Performs a transposed mass evaluation
*
* @param storage GridStorage object that contains the grid's points information
* @param basis a reference to a class that implements a specific basis
* @param source the coefficients of the grid points
* @param x the d-dimensional vector with data points (row-wise)
* @param result the result vector of the matrix vector multiplication
*/
void mult_transpose(GridStorage* storage, BASIS& basis, DataVector& source,
DataMatrix& x, DataVector& result) {
result.setAll(0.0);
size_t source_size = source.getSize();
#pragma omp parallel
{
DataVector privateResult(result.getSize());
privateResult.setAll(0.0);
DataVector line(x.getNcols());
AlgorithmEvaluationTransposed<BASIS> AlgoEvalTrans(storage);
privateResult.setAll(0.0);
#pragma omp for schedule(static)
for (size_t i = 0; i < source_size; i++) {
x.getRow(i, line);
AlgoEvalTrans(basis, line, source[i], privateResult);
}
#pragma omp critical
{
result.add(privateResult);
}
}
}
/**
* Performs a mass evaluation
*
* @param storage GridStorage object that contains the grid's points information
* @param basis a reference to a class that implements a specific basis
* @param source the coefficients of the grid points
* @param x the d-dimensional vector with data points (row-wise)
* @param result the result vector of the matrix vector multiplication
*/
void mult(GridStorage* storage, BASIS& basis, DataVector& source, DataMatrix& x,
DataVector& result) {
result.setAll(0.0);
size_t result_size = result.getSize();
#pragma omp parallel
{
DataVector line(x.getNcols());
AlgorithmEvaluation<BASIS> AlgoEval(storage);
#pragma omp for schedule(static)
for (size_t i = 0; i < result_size; i++) {
x.getRow(i, line);
result[i] = AlgoEval(basis, line, source);
}
}
}