void RBEvaluation::clear()
{
START_LOG("clear()", "RBEvaluation");
// Clear the basis functions
for(unsigned int i=0; i<basis_functions.size(); i++)
{
if (basis_functions[i])
{
basis_functions[i]->clear();
delete basis_functions[i];
basis_functions[i] = NULL;
}
}
set_n_basis_functions(0);
clear_riesz_representors();
// Clear the Greedy param list
for(unsigned int i=0; i<greedy_param_list.size(); i++)
greedy_param_list[i].clear();
greedy_param_list.clear();
STOP_LOG("clear()", "RBEvaluation");
}
void RBEvaluation::resize_data_structures(const unsigned int Nmax,
bool resize_error_bound_data)
{
START_LOG("resize_data_structures()", "RBEvaluation");
if(Nmax < this->get_n_basis_functions())
libmesh_error_msg("Error: Cannot set Nmax to be less than the current number of basis functions.");
// Resize/clear inner product matrix
if(compute_RB_inner_product)
RB_inner_product_matrix.resize(Nmax,Nmax);
// Allocate dense matrices for RB solves
RB_Aq_vector.resize(rb_theta_expansion->get_n_A_terms());
for(unsigned int q=0; q<rb_theta_expansion->get_n_A_terms(); q++)
{
// Initialize the memory for the RB matrices
RB_Aq_vector[q].resize(Nmax,Nmax);
}
RB_Fq_vector.resize(rb_theta_expansion->get_n_F_terms());
for(unsigned int q=0; q<rb_theta_expansion->get_n_F_terms(); q++)
{
// Initialize the memory for the RB vectors
RB_Fq_vector[q].resize(Nmax);
}
// Initialize the RB output vectors
RB_output_vectors.resize(rb_theta_expansion->get_n_outputs());
for(unsigned int n=0; n<rb_theta_expansion->get_n_outputs(); n++)
{
RB_output_vectors[n].resize(rb_theta_expansion->get_n_output_terms(n));
for(unsigned int q_l=0; q_l<rb_theta_expansion->get_n_output_terms(n); q_l++)
{
RB_output_vectors[n][q_l].resize(Nmax);
}
}
// Initialize vectors storing output data
RB_outputs.resize(rb_theta_expansion->get_n_outputs(), 0.);
if(resize_error_bound_data)
{
// Initialize vectors for the norms of the Fq representors
unsigned int Q_f_hat = rb_theta_expansion->get_n_F_terms()*(rb_theta_expansion->get_n_F_terms()+1)/2;
Fq_representor_innerprods.resize(Q_f_hat);
// Initialize vectors for the norms of the representors
Fq_Aq_representor_innerprods.resize(rb_theta_expansion->get_n_F_terms());
for(unsigned int i=0; i<rb_theta_expansion->get_n_F_terms(); i++)
{
Fq_Aq_representor_innerprods[i].resize(rb_theta_expansion->get_n_A_terms());
for(unsigned int j=0; j<rb_theta_expansion->get_n_A_terms(); j++)
{
Fq_Aq_representor_innerprods[i][j].resize(Nmax, 0.);
}
}
unsigned int Q_a_hat = rb_theta_expansion->get_n_A_terms()*(rb_theta_expansion->get_n_A_terms()+1)/2;
Aq_Aq_representor_innerprods.resize(Q_a_hat);
for(unsigned int i=0; i<Q_a_hat; i++)
{
Aq_Aq_representor_innerprods[i].resize(Nmax);
for(unsigned int j=0; j<Nmax; j++)
{
Aq_Aq_representor_innerprods[i][j].resize(Nmax, 0.);
}
}
RB_output_error_bounds.resize(rb_theta_expansion->get_n_outputs(), 0.);
// Resize the output dual norm vectors
output_dual_innerprods.resize(rb_theta_expansion->get_n_outputs());
for(unsigned int n=0; n<rb_theta_expansion->get_n_outputs(); n++)
{
unsigned int Q_l_hat = rb_theta_expansion->get_n_output_terms(n)*(rb_theta_expansion->get_n_output_terms(n)+1)/2;
output_dual_innerprods[n].resize(Q_l_hat);
}
// Clear and resize the vector of Aq_representors
clear_riesz_representors();
Aq_representor.resize(rb_theta_expansion->get_n_A_terms());
for(unsigned int q_a=0; q_a<rb_theta_expansion->get_n_A_terms(); q_a++)
{
Aq_representor[q_a].resize(Nmax);
}
}
STOP_LOG("resize_data_structures()", "RBEvaluation");
}
