void AdaptiveSparseGridInterface<Scalar,UserVector>::eval_cubature(
UserVector & output,
CubatureTensorSorted<Scalar> & cubRule) {
//int dimf = 0; // Dimension of the integrand
Scalar weight = 0.0;
std::vector<Scalar> point(dimension_,(Scalar)0.0);
//std::vector<Scalar> f(1,0.0);
Teuchos::RCP<UserVector> f = output.Create(); output.Update(-1.0,output);
typename std::map<std::vector<Scalar>,int>::iterator it;
for (it=cubRule.begin(); it!=cubRule.end(); it++) {
// Evaluate Function
point.assign((it->first).begin(),(it->first).end()); // Extract point
f->Update(-1.0,*f);
eval_integrand(*f,point); // Evaluate Integrand at point
// Update integral
weight = cubRule.getWeight(it->second);
output.Update(weight,*f);
}
}
void AdaptiveSparseGridInterface<Scalar,UserVector>::init(UserVector & output) {
std::vector<int> index(dimension_,1);
CubatureTensorSorted<Scalar> cubRule(
dimension_,index,rule1D_,growth1D_,isNormalized_);
// Evaluate the initial contribution to the integral
initialDiff_ = 1.0;
output.Update(-1.0,output);
eval_cubature(output,cubRule);
// Compute the initial error indicator
initialDiff_ = error_indicator(output);
if (fabs(initialDiff_)<INTREPID_TOL)
initialDiff_ = 1.0;
}
Scalar AdaptiveSparseGrid<Scalar,UserVector>::refine_grid(
typename std::multimap<Scalar,std::vector<int> > & activeIndex,
std::set<std::vector<int> > & oldIndex,
UserVector & integralValue,
CubatureTensorSorted<Scalar> & cubRule,
Scalar globalErrorIndicator,
AdaptiveSparseGridInterface<Scalar,UserVector> & problem_data) {
TEUCHOS_TEST_FOR_EXCEPTION((activeIndex.empty()),std::out_of_range,
">>> ERROR (AdaptiveSparseGrid): Active Index set is empty.");
int dimension = problem_data.getDimension();
std::vector<EIntrepidBurkardt> rule1D; problem_data.getRule(rule1D);
std::vector<EIntrepidGrowth> growth1D; problem_data.getGrowth(growth1D);
// Initialize Flags
bool maxLevelFlag = true;
bool isAdmissibleFlag = true;
// Initialize Cubature Rule
Teuchos::RCP<UserVector> s = integralValue.Create();
// Initialize iterator at end of inOldIndex
std::set<std::vector<int> >::iterator it1(oldIndex.end());
// Initialize iterator at end of inActiveIndex
typename std::multimap<Scalar,std::vector<int> >::iterator it;
// Obtain Global Error Indicator as sum of key values of inActiveIndex
Scalar eta = globalErrorIndicator;
// Select Index to refine
it = activeIndex.end(); it--; // Decrement to position of final value
Scalar G = it->first; // Largest Error Indicator is at End
eta -= G; // Update global error indicator
std::vector<int> index = it->second; // Get Corresponding index
activeIndex.erase(it); // Erase Index from active index set
// Insert Index into old index set
oldIndex.insert(it1,index); it1 = oldIndex.end();
// Refinement process
for (int k=0; k<dimension; k++) {
index[k]++; // index + ek
// Check Max Level
maxLevelFlag = problem_data.max_level(index);
if (maxLevelFlag) {
// Check Admissibility
isAdmissibleFlag = isAdmissible(index,k,oldIndex,problem_data);
if (isAdmissibleFlag) { // If admissible
// Build Differential Quarature Rule
CubatureTensorSorted<Scalar> diffRule(0,dimension);
build_diffRule(diffRule,index,problem_data);
// Apply Rule to function
problem_data.eval_cubature(*s,diffRule);
// Update integral value
integralValue.Update(*s);
// Update local error indicator and index set
G = problem_data.error_indicator(*s);
if (activeIndex.end()!=activeIndex.begin())
activeIndex.insert(activeIndex.end()--,
std::pair<Scalar,std::vector<int> >(G,index));
else
activeIndex.insert(std::pair<Scalar,std::vector<int> >(G,index));
// Update global error indicators
eta += G;
// Update adapted quadrature rule nodes and weights
cubRule.update(1.0,diffRule,1.0);
}
}
else { // Max Level Exceeded
//std::cout << "Maximum Level Exceeded" << std::endl;
}
index[k]--;
}
return eta;
}
