void eval_integrand(UserVector & output, std::vector<Scalar> & input) {
int dimension = (int)alpha.size();
Scalar total = 0.0;
Scalar point = 0.0;
for (int i=0; i<dimension; i++) {
point = 0.5*input[i]+0.5;
total += powl(alpha[i]*(point-beta[i]),(long double)2.0);
}
output.clear(); output.resize(1,std::exp(-total));
}
Scalar error_indicator(UserVector & input) {
int dimension = (int)input.size();
Scalar norm2 = 0.0;
for (int i=0; i<dimension; i++)
norm2 += input[i]*input[i];
Scalar ID = AdaptiveSparseGridInterface<Scalar,UserVector>::
getInitialDiff();
norm2 = std::sqrt(norm2)/ID;
return norm2;
}
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;
}
Scalar AdaptiveSparseGrid<Scalar,UserVector>::refine_grid(
typename std::multimap<Scalar,std::vector<int> > & indexSet,
UserVector & integralValue,
AdaptiveSparseGridInterface<Scalar,UserVector> & problem_data) {
int dimension = problem_data.getDimension();
std::vector<EIntrepidBurkardt> rule1D; problem_data.getRule(rule1D);
std::vector<EIntrepidGrowth> growth1D; problem_data.getGrowth(growth1D);
// Copy Multimap into a Set for ease of use
typename std::multimap<Scalar,std::vector<int> >::iterator it;
std::set<std::vector<int> > oldSet;
std::set<std::vector<int> >::iterator it1(oldSet.begin());
for (it=indexSet.begin(); it!=indexSet.end(); it++) {
oldSet.insert(it1,it->second);
it1++;
}
indexSet.clear();
// Find Possible Active Points
int flag = 1;
std::vector<int> index(dimension,0);
typename std::multimap<Scalar,std::vector<int> > activeSet;
for (it1=oldSet.begin(); it1!=oldSet.end(); it1++) {
index = *it1;
for (int i=0; i<dimension; i++) {
index[i]++;
flag = (int)(!oldSet.count(index));
index[i]--;
if (flag) {
activeSet.insert(std::pair<Scalar,std::vector<int> >(1.0,index));
oldSet.erase(it1);
break;
}
}
}
// Compute local and global error indicators for active set
typename std::multimap<Scalar,std::vector<int> >::iterator it2;
Scalar eta = 0.0;
Scalar G = 0.0;
Teuchos::RCP<UserVector> s = integralValue.Create();
for (it2=activeSet.begin(); it2!=activeSet.end(); it2++) {
// Build Differential Quarature Rule
index = it2->second;
CubatureTensorSorted<Scalar> diffRule(0,dimension);
build_diffRule(diffRule,index,problem_data);
// Apply Rule to function
problem_data.eval_cubature(*s,diffRule);
// Update local error indicator and index set
G = problem_data.error_indicator(*s);
activeSet.erase(it2);
activeSet.insert(it2,std::pair<Scalar,std::vector<int> >(G,index));
eta += G;
}
// Refine Sparse Grid
eta = refine_grid(activeSet,oldSet,integralValue,eta,
dimension,rule1D,growth1D);
// Insert New Active and Old Index sets into indexSet
indexSet.insert(activeSet.begin(),activeSet.end());
for (it1=oldSet.begin(); it1!=oldSet.end(); it1++) {
index = *it1;
indexSet.insert(std::pair<Scalar,std::vector<int> >(-1.0,index));
}
return eta;
}
void eval_integrand(UserVector & output, std::vector<Scalar> & input) {
output.clear(); output.resize(1,std::exp(-input[0]*input[0])
+10.0*std::exp(-input[1]*input[1]));
}
void eval_integrand(UserVector & output, std::vector<Scalar> & input) {
output.clear(); output.resize(1,powl(input[0]+input[1],(long double)6.0));
}
