int main(int argc, char *argv[]) {
int n = 32; // spatial discretization (per dimension)
int num_KL = 2; // number of KL terms
int p = 3; // polynomial order
double mu = 0.1; // mean of exponential random field
double s = 0.2; // std. dev. of exponential r.f.
bool nonlinear_expansion = false; // nonlinear expansion of diffusion coeff
// (e.g., log-normal)
bool symmetric = false; // use symmetric formulation
double g_mean_exp = 0.172988; // expected response mean
double g_std_dev_exp = 0.0380007; // expected response std. dev.
double g_tol = 1e-6; // tolerance on determining success
// Initialize MPI
#ifdef HAVE_MPI
MPI_Init(&argc,&argv);
#endif
int MyPID;
try {
{
TEUCHOS_FUNC_TIME_MONITOR("Total PCE Calculation Time");
// Create a communicator for Epetra objects
Teuchos::RCP<const Epetra_Comm> globalComm;
#ifdef HAVE_MPI
globalComm = Teuchos::rcp(new Epetra_MpiComm(MPI_COMM_WORLD));
#else
globalComm = Teuchos::rcp(new Epetra_SerialComm);
#endif
MyPID = globalComm->MyPID();
// Create Stochastic Galerkin basis and expansion
Teuchos::Array< Teuchos::RCP<const Stokhos::OneDOrthogPolyBasis<int,double> > > bases(num_KL);
for (int i=0; i<num_KL; i++)
bases[i] = Teuchos::rcp(new Stokhos::LegendreBasis<int,double>(p,true));
Teuchos::RCP<const Stokhos::CompletePolynomialBasis<int,double> > basis =
Teuchos::rcp(new Stokhos::CompletePolynomialBasis<int,double>(bases,
1e-12));
int sz = basis->size();
Teuchos::RCP<Stokhos::Sparse3Tensor<int,double> > Cijk;
if (nonlinear_expansion)
Cijk = basis->computeTripleProductTensor(sz);
else
Cijk = basis->computeTripleProductTensor(num_KL+1);
Teuchos::RCP<Stokhos::OrthogPolyExpansion<int,double> > expansion =
Teuchos::rcp(new Stokhos::AlgebraicOrthogPolyExpansion<int,double>(basis,
Cijk));
if (MyPID == 0)
std::cout << "Stochastic Galerkin expansion size = " << sz << std::endl;
// Create stochastic parallel distribution
int num_spatial_procs = -1;
Teuchos::ParameterList parallelParams;
parallelParams.set("Number of Spatial Processors", num_spatial_procs);
// parallelParams.set("Rebalance Stochastic Graph", true);
// Teuchos::ParameterList& isorropia_params =
// parallelParams.sublist("Isorropia");
// isorropia_params.set("Balance objective", "nonzeros");
Teuchos::RCP<Stokhos::ParallelData> sg_parallel_data =
Teuchos::rcp(new Stokhos::ParallelData(basis, Cijk, globalComm,
parallelParams));
Teuchos::RCP<const EpetraExt::MultiComm> sg_comm =
sg_parallel_data->getMultiComm();
Teuchos::RCP<const Epetra_Comm> app_comm =
sg_parallel_data->getSpatialComm();
// Create application
Teuchos::RCP<twoD_diffusion_ME> model =
Teuchos::rcp(new twoD_diffusion_ME(app_comm, n, num_KL, mu, s, basis,
nonlinear_expansion, symmetric));
// Setup stochastic Galerkin algorithmic parameters
Teuchos::RCP<Teuchos::ParameterList> sgParams =
Teuchos::rcp(new Teuchos::ParameterList);
if (!nonlinear_expansion) {
sgParams->set("Parameter Expansion Type", "Linear");
sgParams->set("Jacobian Expansion Type", "Linear");
}
Teuchos::ParameterList precParams;
precParams.set("default values", "SA");
precParams.set("ML output", 0);
precParams.set("max levels",5);
precParams.set("increasing or decreasing","increasing");
precParams.set("aggregation: type", "Uncoupled");
precParams.set("smoother: type","ML symmetric Gauss-Seidel");
precParams.set("smoother: sweeps",2);
precParams.set("smoother: pre or post", "both");
precParams.set("coarse: max size", 200);
//precParams.set("PDE equations",sz);
#ifdef HAVE_ML_AMESOS
precParams.set("coarse: type","Amesos-KLU");
#else
precParams.set("coarse: type","Jacobi");
#endif
// Create stochastic Galerkin model evaluator
Teuchos::RCP<Stokhos::SGModelEvaluator_Interlaced> sg_model =
Teuchos::rcp(new Stokhos::SGModelEvaluator_Interlaced(
model, basis, Teuchos::null,
expansion, sg_parallel_data,
sgParams));
// Set up stochastic parameters
Teuchos::RCP<Stokhos::EpetraVectorOrthogPoly> sg_p_poly =
sg_model->create_p_sg(0);
for (int i=0; i<num_KL; i++) {
sg_p_poly->term(i,0)[i] = 0.0;
sg_p_poly->term(i,1)[i] = 1.0;
}
// Create vectors and operators
Teuchos::RCP<const Epetra_Vector> sg_p = sg_p_poly->getBlockVector();
Teuchos::RCP<Epetra_Vector> sg_x =
Teuchos::rcp(new Epetra_Vector(*(sg_model->get_x_map())));
sg_x->PutScalar(0.0);
Teuchos::RCP<Epetra_Vector> sg_f =
Teuchos::rcp(new Epetra_Vector(*(sg_model->get_f_map())));
Teuchos::RCP<Epetra_Vector> sg_dx =
Teuchos::rcp(new Epetra_Vector(*(sg_model->get_x_map())));
Teuchos::RCP<Epetra_CrsMatrix> sg_J =
Teuchos::rcp_dynamic_cast<Epetra_CrsMatrix>(sg_model->create_W());
Teuchos::RCP<ML_Epetra::MultiLevelPreconditioner> sg_M =
Teuchos::rcp(new ML_Epetra::MultiLevelPreconditioner(*sg_J, precParams,
false));
// Setup InArgs and OutArgs
EpetraExt::ModelEvaluator::InArgs sg_inArgs = sg_model->createInArgs();
EpetraExt::ModelEvaluator::OutArgs sg_outArgs = sg_model->createOutArgs();
sg_inArgs.set_p(1, sg_p);
sg_inArgs.set_x(sg_x);
sg_outArgs.set_f(sg_f);
sg_outArgs.set_W(sg_J);
// Evaluate model
sg_model->evalModel(sg_inArgs, sg_outArgs);
sg_M->ComputePreconditioner();
// Print initial residual norm
double norm_f;
sg_f->Norm2(&norm_f);
if (MyPID == 0)
std::cout << "\nInitial residual norm = " << norm_f << std::endl;
// Setup AztecOO solver
AztecOO aztec;
if (symmetric)
aztec.SetAztecOption(AZ_solver, AZ_cg);
else
aztec.SetAztecOption(AZ_solver, AZ_gmres);
aztec.SetAztecOption(AZ_precond, AZ_none);
aztec.SetAztecOption(AZ_kspace, 20);
aztec.SetAztecOption(AZ_conv, AZ_r0);
aztec.SetAztecOption(AZ_output, 1);
aztec.SetUserOperator(sg_J.get());
aztec.SetPrecOperator(sg_M.get());
aztec.SetLHS(sg_dx.get());
aztec.SetRHS(sg_f.get());
// Solve linear system
aztec.Iterate(1000, 1e-12);
// Update x
sg_x->Update(-1.0, *sg_dx, 1.0);
// Save solution to file
EpetraExt::VectorToMatrixMarketFile("stochastic_solution_interlaced.mm",
*sg_x);
// Save RHS to file
EpetraExt::VectorToMatrixMarketFile("stochastic_RHS_interlaced.mm",
*sg_f);
// Save operator to file
EpetraExt::RowMatrixToMatrixMarketFile("stochastic_operator_interlaced.mm",
*sg_J);
// Save mean and variance to file
Teuchos::RCP<Stokhos::EpetraVectorOrthogPoly> sg_x_poly =
sg_model->create_x_sg(View, sg_x.get());
Epetra_Vector mean(*(model->get_x_map()));
Epetra_Vector std_dev(*(model->get_x_map()));
sg_x_poly->computeMean(mean);
sg_x_poly->computeStandardDeviation(std_dev);
EpetraExt::VectorToMatrixMarketFile("mean_gal_interlaced.mm", mean);
EpetraExt::VectorToMatrixMarketFile("std_dev_gal_interlaced.mm", std_dev);
// Compute new residual & response function
EpetraExt::ModelEvaluator::OutArgs sg_outArgs2 = sg_model->createOutArgs();
Teuchos::RCP<Epetra_Vector> sg_g =
Teuchos::rcp(new Epetra_Vector(*(sg_model->get_g_map(0))));
sg_f->PutScalar(0.0);
sg_outArgs2.set_f(sg_f);
sg_outArgs2.set_g(0, sg_g);
sg_model->evalModel(sg_inArgs, sg_outArgs2);
// Print initial residual norm
sg_f->Norm2(&norm_f);
if (MyPID == 0)
std::cout << "\nFinal residual norm = " << norm_f << std::endl;
// Print mean and standard deviation of responses
Teuchos::RCP<Stokhos::EpetraVectorOrthogPoly> sg_g_poly =
sg_model->create_g_sg(0, View, sg_g.get());
Epetra_Vector g_mean(*(model->get_g_map(0)));
Epetra_Vector g_std_dev(*(model->get_g_map(0)));
sg_g_poly->computeMean(g_mean);
sg_g_poly->computeStandardDeviation(g_std_dev);
std::cout.precision(16);
// std::cout << "\nResponse Expansion = " << std::endl;
// std::cout.precision(12);
// sg_g_poly->print(std::cout);
std::cout << "\nResponse Mean = " << std::endl << g_mean << std::endl;
std::cout << "Response Std. Dev. = " << std::endl << g_std_dev << std::endl;
// Determine if example passed
bool passed = false;
if (norm_f < 1.0e-10 &&
std::abs(g_mean[0]-g_mean_exp) < g_tol &&
std::abs(g_std_dev[0]-g_std_dev_exp) < g_tol)
passed = true;
if (MyPID == 0) {
if (passed)
std::cout << "Example Passed!" << std::endl;
else
std::cout << "Example Failed!" << std::endl;
}
}
Teuchos::TimeMonitor::summarize(std::cout);
Teuchos::TimeMonitor::zeroOutTimers();
}
catch (std::exception& e) {
std::cout << e.what() << std::endl;
}
catch (string& s) {
std::cout << s << std::endl;
}
catch (char *s) {
std::cout << s << std::endl;
}
catch (...) {
std::cout << "Caught unknown exception!" <<std:: endl;
}
#ifdef HAVE_MPI
MPI_Finalize() ;
#endif
}
int main(int argc, char *argv[]) {
int n = 32; // spatial discretization (per dimension)
int num_KL = 2; // number of KL terms
int p = 3; // polynomial order
double mu = 0.1; // mean of exponential random field
double s = 0.2; // std. dev. of exponential r.f.
bool nonlinear_expansion = false; // nonlinear expansion of diffusion coeff
// (e.g., log-normal)
bool matrix_free = true; // use matrix-free stochastic operator
bool symmetric = false; // use symmetric formulation
double g_mean_exp = 0.172988; // expected response mean
double g_std_dev_exp = 0.0380007; // expected response std. dev.
double g_tol = 1e-6; // tolerance on determining success
// Initialize MPI
#ifdef HAVE_MPI
MPI_Init(&argc,&argv);
#endif
int MyPID;
try {
{
TEUCHOS_FUNC_TIME_MONITOR("Total PCE Calculation Time");
// Create a communicator for Epetra objects
Teuchos::RCP<const Epetra_Comm> globalComm;
#ifdef HAVE_MPI
globalComm = Teuchos::rcp(new Epetra_MpiComm(MPI_COMM_WORLD));
#else
globalComm = Teuchos::rcp(new Epetra_SerialComm);
#endif
MyPID = globalComm->MyPID();
// Create Stochastic Galerkin basis and expansion
Teuchos::Array< Teuchos::RCP<const Stokhos::OneDOrthogPolyBasis<int,double> > > bases(num_KL);
for (int i=0; i<num_KL; i++)
bases[i] = Teuchos::rcp(new Stokhos::LegendreBasis<int,double>(p, true));
Teuchos::RCP<const Stokhos::CompletePolynomialBasis<int,double> > basis =
Teuchos::rcp(new Stokhos::CompletePolynomialBasis<int,double>(bases));
int sz = basis->size();
Teuchos::RCP<Stokhos::Sparse3Tensor<int,double> > Cijk;
if (nonlinear_expansion)
Cijk = basis->computeTripleProductTensor();
else
Cijk = basis->computeLinearTripleProductTensor();
Teuchos::RCP<Stokhos::OrthogPolyExpansion<int,double> > expansion =
Teuchos::rcp(new Stokhos::AlgebraicOrthogPolyExpansion<int,double>(basis,
Cijk));
if (MyPID == 0)
std::cout << "Stochastic Galerkin expansion size = " << sz << std::endl;
// Create stochastic parallel distribution
int num_spatial_procs = -1;
if (argc > 1)
num_spatial_procs = std::atoi(argv[1]);
Teuchos::ParameterList parallelParams;
parallelParams.set("Number of Spatial Processors", num_spatial_procs);
Teuchos::RCP<Stokhos::ParallelData> sg_parallel_data =
Teuchos::rcp(new Stokhos::ParallelData(basis, Cijk, globalComm,
parallelParams));
Teuchos::RCP<const EpetraExt::MultiComm> sg_comm =
sg_parallel_data->getMultiComm();
Teuchos::RCP<const Epetra_Comm> app_comm =
sg_parallel_data->getSpatialComm();
// Create application
Teuchos::RCP<twoD_diffusion_ME> model =
Teuchos::rcp(new twoD_diffusion_ME(app_comm, n, num_KL, mu, s, basis,
nonlinear_expansion, symmetric));
// Setup stochastic Galerkin algorithmic parameters
Teuchos::RCP<Teuchos::ParameterList> sgParams =
Teuchos::rcp(new Teuchos::ParameterList);
Teuchos::ParameterList& sgOpParams =
sgParams->sublist("SG Operator");
Teuchos::ParameterList& sgPrecParams =
sgParams->sublist("SG Preconditioner");
if (!nonlinear_expansion) {
sgParams->set("Parameter Expansion Type", "Linear");
sgParams->set("Jacobian Expansion Type", "Linear");
}
if (matrix_free) {
sgOpParams.set("Operator Method", "Matrix Free");
sgPrecParams.set("Preconditioner Method", "Approximate Gauss-Seidel");
sgPrecParams.set("Symmetric Gauss-Seidel", symmetric);
sgPrecParams.set("Mean Preconditioner Type", "ML");
Teuchos::ParameterList& precParams =
sgPrecParams.sublist("Mean Preconditioner Parameters");
precParams.set("default values", "SA");
precParams.set("ML output", 0);
precParams.set("max levels",5);
precParams.set("increasing or decreasing","increasing");
precParams.set("aggregation: type", "Uncoupled");
precParams.set("smoother: type","ML symmetric Gauss-Seidel");
precParams.set("smoother: sweeps",2);
precParams.set("smoother: pre or post", "both");
precParams.set("coarse: max size", 200);
#ifdef HAVE_ML_AMESOS
precParams.set("coarse: type","Amesos-KLU");
#else
precParams.set("coarse: type","Jacobi");
#endif
}
else {
sgOpParams.set("Operator Method", "Fully Assembled");
sgPrecParams.set("Preconditioner Method", "None");
}
// Create stochastic Galerkin model evaluator
Teuchos::RCP<Stokhos::SGModelEvaluator> sg_model =
Teuchos::rcp(new Stokhos::SGModelEvaluator(model, basis, Teuchos::null,
expansion, sg_parallel_data,
sgParams));
// Set up stochastic parameters
// The current implementation of the model doesn't actually use these
// values, but is hard-coded to certain uncertainty models
Teuchos::Array<double> point(num_KL, 1.0);
Teuchos::Array<double> basis_vals(sz);
basis->evaluateBases(point, basis_vals);
Teuchos::RCP<Stokhos::EpetraVectorOrthogPoly> sg_p_init =
sg_model->create_p_sg(0);
for (int i=0; i<num_KL; i++) {
sg_p_init->term(i,0)[i] = 0.0;
sg_p_init->term(i,1)[i] = 1.0 / basis_vals[i+1];
}
sg_model->set_p_sg_init(0, *sg_p_init);
// Setup stochastic initial guess
Teuchos::RCP<Stokhos::EpetraVectorOrthogPoly> sg_x_init =
sg_model->create_x_sg();
sg_x_init->init(0.0);
sg_model->set_x_sg_init(*sg_x_init);
// Set up NOX parameters
Teuchos::RCP<Teuchos::ParameterList> noxParams =
Teuchos::rcp(new Teuchos::ParameterList);
// Set the nonlinear solver method
noxParams->set("Nonlinear Solver", "Line Search Based");
// Set the printing parameters in the "Printing" sublist
Teuchos::ParameterList& printParams = noxParams->sublist("Printing");
printParams.set("MyPID", MyPID);
printParams.set("Output Precision", 3);
printParams.set("Output Processor", 0);
printParams.set("Output Information",
NOX::Utils::OuterIteration +
NOX::Utils::OuterIterationStatusTest +
NOX::Utils::InnerIteration +
NOX::Utils::LinearSolverDetails +
NOX::Utils::Warning +
NOX::Utils::Error);
// Create printing utilities
NOX::Utils utils(printParams);
// Sublist for line search
Teuchos::ParameterList& searchParams = noxParams->sublist("Line Search");
searchParams.set("Method", "Full Step");
// Sublist for direction
Teuchos::ParameterList& dirParams = noxParams->sublist("Direction");
dirParams.set("Method", "Newton");
Teuchos::ParameterList& newtonParams = dirParams.sublist("Newton");
newtonParams.set("Forcing Term Method", "Constant");
// Sublist for linear solver for the Newton method
Teuchos::ParameterList& lsParams = newtonParams.sublist("Linear Solver");
if (symmetric)
lsParams.set("Aztec Solver", "CG");
else
lsParams.set("Aztec Solver", "GMRES");
lsParams.set("Max Iterations", 1000);
lsParams.set("Size of Krylov Subspace", 100);
lsParams.set("Tolerance", 1e-12);
lsParams.set("Output Frequency", 1);
if (matrix_free)
lsParams.set("Preconditioner", "User Defined");
else {
lsParams.set("Preconditioner", "ML");
Teuchos::ParameterList& precParams =
lsParams.sublist("ML");
ML_Epetra::SetDefaults("DD", precParams);
lsParams.set("Write Linear System", false);
}
// Sublist for convergence tests
Teuchos::ParameterList& statusParams = noxParams->sublist("Status Tests");
statusParams.set("Test Type", "Combo");
statusParams.set("Number of Tests", 2);
statusParams.set("Combo Type", "OR");
Teuchos::ParameterList& normF = statusParams.sublist("Test 0");
normF.set("Test Type", "NormF");
normF.set("Tolerance", 1e-10);
normF.set("Scale Type", "Scaled");
Teuchos::ParameterList& maxIters = statusParams.sublist("Test 1");
maxIters.set("Test Type", "MaxIters");
maxIters.set("Maximum Iterations", 1);
// Create NOX interface
Teuchos::RCP<NOX::Epetra::ModelEvaluatorInterface> nox_interface =
Teuchos::rcp(new NOX::Epetra::ModelEvaluatorInterface(sg_model));
// Create NOX linear system object
Teuchos::RCP<const Epetra_Vector> u = sg_model->get_x_init();
Teuchos::RCP<Epetra_Operator> A = sg_model->create_W();
Teuchos::RCP<NOX::Epetra::Interface::Required> iReq = nox_interface;
Teuchos::RCP<NOX::Epetra::Interface::Jacobian> iJac = nox_interface;
Teuchos::RCP<NOX::Epetra::LinearSystemAztecOO> linsys;
if (matrix_free) {
Teuchos::RCP<Epetra_Operator> M = sg_model->create_WPrec()->PrecOp;
Teuchos::RCP<NOX::Epetra::Interface::Preconditioner> iPrec = nox_interface;
linsys =
Teuchos::rcp(new NOX::Epetra::LinearSystemAztecOO(printParams, lsParams,
iJac, A, iPrec, M,
*u));
}
else {
linsys =
Teuchos::rcp(new NOX::Epetra::LinearSystemAztecOO(printParams, lsParams,
iReq, iJac, A,
*u));
}
// Build NOX group
Teuchos::RCP<NOX::Epetra::Group> grp =
Teuchos::rcp(new NOX::Epetra::Group(printParams, iReq, *u, linsys));
// Create the Solver convergence test
Teuchos::RCP<NOX::StatusTest::Generic> statusTests =
NOX::StatusTest::buildStatusTests(statusParams, utils);
// Create the solver
Teuchos::RCP<NOX::Solver::Generic> solver =
NOX::Solver::buildSolver(grp, statusTests, noxParams);
// Solve the system
NOX::StatusTest::StatusType status = solver->solve();
// Get final solution
const NOX::Epetra::Group& finalGroup =
dynamic_cast<const NOX::Epetra::Group&>(solver->getSolutionGroup());
const Epetra_Vector& finalSolution =
(dynamic_cast<const NOX::Epetra::Vector&>(finalGroup.getX())).getEpetraVector();
// Save final solution to file
EpetraExt::VectorToMatrixMarketFile("nox_stochastic_solution.mm",
finalSolution);
// Save mean and variance to file
Teuchos::RCP<Stokhos::EpetraVectorOrthogPoly> sg_x_poly =
sg_model->create_x_sg(View, &finalSolution);
Epetra_Vector mean(*(model->get_x_map()));
Epetra_Vector std_dev(*(model->get_x_map()));
sg_x_poly->computeMean(mean);
sg_x_poly->computeStandardDeviation(std_dev);
EpetraExt::VectorToMatrixMarketFile("mean_gal.mm", mean);
EpetraExt::VectorToMatrixMarketFile("std_dev_gal.mm", std_dev);
// Evaluate SG responses at SG parameters
EpetraExt::ModelEvaluator::InArgs sg_inArgs = sg_model->createInArgs();
EpetraExt::ModelEvaluator::OutArgs sg_outArgs =
sg_model->createOutArgs();
Teuchos::RCP<const Epetra_Vector> sg_p = sg_model->get_p_init(1);
Teuchos::RCP<Epetra_Vector> sg_g =
Teuchos::rcp(new Epetra_Vector(*(sg_model->get_g_map(0))));
sg_inArgs.set_p(1, sg_p);
sg_inArgs.set_x(Teuchos::rcp(&finalSolution,false));
sg_outArgs.set_g(0, sg_g);
sg_model->evalModel(sg_inArgs, sg_outArgs);
// Print mean and standard deviation of response
Teuchos::RCP<Stokhos::EpetraVectorOrthogPoly> sg_g_poly =
sg_model->create_g_sg(0, View, sg_g.get());
Epetra_Vector g_mean(*(model->get_g_map(0)));
Epetra_Vector g_std_dev(*(model->get_g_map(0)));
sg_g_poly->computeMean(g_mean);
sg_g_poly->computeStandardDeviation(g_std_dev);
std::cout.precision(16);
// std::cout << "\nResponse Expansion = " << std::endl;
// std::cout.precision(12);
// sg_g_poly->print(std::cout);
std::cout << std::endl;
std::cout << "Response Mean = " << std::endl << g_mean << std::endl;
std::cout << "Response Std. Dev. = " << std::endl << g_std_dev << std::endl;
// Determine if example passed
bool passed = false;
if (status == NOX::StatusTest::Converged &&
std::abs(g_mean[0]-g_mean_exp) < g_tol &&
std::abs(g_std_dev[0]-g_std_dev_exp) < g_tol)
passed = true;
if (MyPID == 0) {
if (passed)
std::cout << "Example Passed!" << std::endl;
else
std::cout << "Example Failed!" << std::endl;
}
}
Teuchos::TimeMonitor::summarize(std::cout);
Teuchos::TimeMonitor::zeroOutTimers();
}
catch (std::exception& e) {
std::cout << e.what() << std::endl;
}
catch (string& s) {
std::cout << s << std::endl;
}
catch (char *s) {
std::cout << s << std::endl;
}
catch (...) {
std::cout << "Caught unknown exception!" <<std:: endl;
}
#ifdef HAVE_MPI
MPI_Finalize() ;
#endif
}
int main(int argc, char *argv[]) {
// Initialize MPI
#ifdef HAVE_MPI
MPI_Init(&argc,&argv);
#endif
// Create a communicator for Epetra objects
Teuchos::RCP<const Epetra_Comm> globalComm;
#ifdef HAVE_MPI
globalComm = Teuchos::rcp(new Epetra_MpiComm(MPI_COMM_WORLD));
#else
globalComm = Teuchos::rcp(new Epetra_SerialComm);
#endif
int MyPID = globalComm->MyPID();
try {
// Setup command line options
Teuchos::CommandLineProcessor CLP;
CLP.setDocString(
"This example runs a variety of stochastic Galerkin solvers.\n");
int n = 32;
CLP.setOption("num_mesh", &n, "Number of mesh points in each direction");
bool symmetric = false;
CLP.setOption("symmetric", "unsymmetric", &symmetric,
"Symmetric discretization");
int num_spatial_procs = -1;
CLP.setOption("num_spatial_procs", &num_spatial_procs, "Number of spatial processors (set -1 for all available procs)");
bool rebalance_stochastic_graph = false;
CLP.setOption("rebalance", "no-rebalance", &rebalance_stochastic_graph,
"Rebalance parallel stochastic graph (requires Isorropia)");
SG_RF randField = UNIFORM;
CLP.setOption("rand_field", &randField,
num_sg_rf, sg_rf_values, sg_rf_names,
"Random field type");
double mean = 0.2;
CLP.setOption("mean", &mean, "Mean");
double sigma = 0.1;
CLP.setOption("std_dev", &sigma, "Standard deviation");
double weightCut = 1.0;
CLP.setOption("weight_cut", &weightCut, "Weight cut");
int num_KL = 2;
CLP.setOption("num_kl", &num_KL, "Number of KL terms");
int p = 3;
CLP.setOption("order", &p, "Polynomial order");
bool normalize_basis = true;
CLP.setOption("normalize", "unnormalize", &normalize_basis,
"Normalize PC basis");
SG_Solver solve_method = SG_KRYLOV;
CLP.setOption("sg_solver", &solve_method,
num_sg_solver, sg_solver_values, sg_solver_names,
"SG solver method");
Krylov_Method outer_krylov_method = GMRES;
CLP.setOption("outer_krylov_method", &outer_krylov_method,
num_krylov_method, krylov_method_values, krylov_method_names,
"Outer Krylov method (for Krylov-based SG solver)");
Krylov_Solver outer_krylov_solver = AZTECOO;
CLP.setOption("outer_krylov_solver", &outer_krylov_solver,
num_krylov_solver, krylov_solver_values, krylov_solver_names,
"Outer linear solver");
double outer_tol = 1e-12;
CLP.setOption("outer_tol", &outer_tol, "Outer solver tolerance");
int outer_its = 1000;
CLP.setOption("outer_its", &outer_its, "Maximum outer iterations");
Krylov_Method inner_krylov_method = GMRES;
CLP.setOption("inner_krylov_method", &inner_krylov_method,
num_krylov_method, krylov_method_values, krylov_method_names,
"Inner Krylov method (for G-S, Jacobi, etc...)");
Krylov_Solver inner_krylov_solver = AZTECOO;
CLP.setOption("inner_krylov_solver", &inner_krylov_solver,
num_krylov_solver, krylov_solver_values, krylov_solver_names,
"Inner linear solver");
double inner_tol = 3e-13;
CLP.setOption("inner_tol", &inner_tol, "Inner solver tolerance");
int inner_its = 1000;
CLP.setOption("inner_its", &inner_its, "Maximum inner iterations");
SG_Op opMethod = MATRIX_FREE;
CLP.setOption("sg_operator_method", &opMethod,
num_sg_op, sg_op_values, sg_op_names,
"Operator method");
SG_Prec precMethod = AGS;
CLP.setOption("sg_prec_method", &precMethod,
num_sg_prec, sg_prec_values, sg_prec_names,
"Preconditioner method");
double gs_prec_tol = 1e-1;
CLP.setOption("gs_prec_tol", &gs_prec_tol, "Gauss-Seidel preconditioner tolerance");
int gs_prec_its = 1;
CLP.setOption("gs_prec_its", &gs_prec_its, "Maximum Gauss-Seidel preconditioner iterations");
CLP.parse( argc, argv );
if (MyPID == 0) {
std::cout << "Summary of command line options:" << std::endl
<< "\tnum_mesh = " << n << std::endl
<< "\tsymmetric = " << symmetric << std::endl
<< "\tnum_spatial_procs = " << num_spatial_procs << std::endl
<< "\trebalance = " << rebalance_stochastic_graph
<< std::endl
<< "\trand_field = " << sg_rf_names[randField]
<< std::endl
<< "\tmean = " << mean << std::endl
<< "\tstd_dev = " << sigma << std::endl
<< "\tweight_cut = " << weightCut << std::endl
<< "\tnum_kl = " << num_KL << std::endl
<< "\torder = " << p << std::endl
<< "\tnormalize_basis = " << normalize_basis << std::endl
<< "\tsg_solver = " << sg_solver_names[solve_method]
<< std::endl
<< "\touter_krylov_method = "
<< krylov_method_names[outer_krylov_method] << std::endl
<< "\touter_krylov_solver = "
<< krylov_solver_names[outer_krylov_solver] << std::endl
<< "\touter_tol = " << outer_tol << std::endl
<< "\touter_its = " << outer_its << std::endl
<< "\tinner_krylov_method = "
<< krylov_method_names[inner_krylov_method] << std::endl
<< "\tinner_krylov_solver = "
<< krylov_solver_names[inner_krylov_solver] << std::endl
<< "\tinner_tol = " << inner_tol << std::endl
<< "\tinner_its = " << inner_its << std::endl
<< "\tsg_operator_method = " << sg_op_names[opMethod]
<< std::endl
<< "\tsg_prec_method = " << sg_prec_names[precMethod]
<< std::endl
<< "\tgs_prec_tol = " << gs_prec_tol << std::endl
<< "\tgs_prec_its = " << gs_prec_its << std::endl;
}
bool nonlinear_expansion = false;
if (randField == UNIFORM || randField == RYS)
nonlinear_expansion = false;
else if (randField == LOGNORMAL)
nonlinear_expansion = true;
bool scaleOP = true;
{
TEUCHOS_FUNC_TIME_MONITOR("Total PCE Calculation Time");
// Create Stochastic Galerkin basis and expansion
Teuchos::Array< Teuchos::RCP<const Stokhos::OneDOrthogPolyBasis<int,double> > > bases(num_KL);
for (int i=0; i<num_KL; i++)
if (randField == UNIFORM)
bases[i] = Teuchos::rcp(new Stokhos::LegendreBasis<int,double>(p,normalize_basis));
else if (randField == RYS)
bases[i] = Teuchos::rcp(new Stokhos::RysBasis<int,double>(p,weightCut,normalize_basis));
else if (randField == LOGNORMAL)
bases[i] = Teuchos::rcp(new Stokhos::HermiteBasis<int,double>(p,normalize_basis));
// bases[i] = Teuchos::rcp(new Stokhos::DiscretizedStieltjesBasis<int,double>("beta",p,&uniform_weight,-weightCut,weightCut,true));
Teuchos::RCP<const Stokhos::CompletePolynomialBasis<int,double> > basis =
Teuchos::rcp(new Stokhos::CompletePolynomialBasis<int,double>(bases));
int sz = basis->size();
Teuchos::RCP<Stokhos::Sparse3Tensor<int,double> > Cijk;
if (nonlinear_expansion)
Cijk = basis->computeTripleProductTensor(sz);
else
Cijk = basis->computeTripleProductTensor(num_KL+1);
Teuchos::RCP<Stokhos::OrthogPolyExpansion<int,double> > expansion =
Teuchos::rcp(new Stokhos::AlgebraicOrthogPolyExpansion<int,double>(basis,
Cijk));
if (MyPID == 0)
std::cout << "Stochastic Galerkin expansion size = " << sz << std::endl;
// Create stochastic parallel distribution
Teuchos::ParameterList parallelParams;
parallelParams.set("Number of Spatial Processors", num_spatial_procs);
parallelParams.set("Rebalance Stochastic Graph",
rebalance_stochastic_graph);
Teuchos::RCP<Stokhos::ParallelData> sg_parallel_data =
Teuchos::rcp(new Stokhos::ParallelData(basis, Cijk, globalComm,
parallelParams));
Teuchos::RCP<const EpetraExt::MultiComm> sg_comm =
sg_parallel_data->getMultiComm();
Teuchos::RCP<const Epetra_Comm> app_comm =
sg_parallel_data->getSpatialComm();
// Create application
Teuchos::RCP<twoD_diffusion_ME> model =
Teuchos::rcp(new twoD_diffusion_ME(app_comm, n, num_KL, sigma,
mean, basis, nonlinear_expansion,
symmetric));
// Set up NOX parameters
Teuchos::RCP<Teuchos::ParameterList> noxParams =
Teuchos::rcp(new Teuchos::ParameterList);
// Set the nonlinear solver method
noxParams->set("Nonlinear Solver", "Line Search Based");
// Set the printing parameters in the "Printing" sublist
Teuchos::ParameterList& printParams = noxParams->sublist("Printing");
printParams.set("MyPID", MyPID);
printParams.set("Output Precision", 3);
printParams.set("Output Processor", 0);
printParams.set("Output Information",
NOX::Utils::OuterIteration +
NOX::Utils::OuterIterationStatusTest +
NOX::Utils::InnerIteration +
//NOX::Utils::Parameters +
NOX::Utils::Details +
NOX::Utils::LinearSolverDetails +
NOX::Utils::Warning +
NOX::Utils::Error);
// Create printing utilities
NOX::Utils utils(printParams);
// Sublist for line search
Teuchos::ParameterList& searchParams = noxParams->sublist("Line Search");
searchParams.set("Method", "Full Step");
// Sublist for direction
Teuchos::ParameterList& dirParams = noxParams->sublist("Direction");
dirParams.set("Method", "Newton");
Teuchos::ParameterList& newtonParams = dirParams.sublist("Newton");
newtonParams.set("Forcing Term Method", "Constant");
// Sublist for linear solver for the Newton method
Teuchos::ParameterList& lsParams = newtonParams.sublist("Linear Solver");
// Alternative linear solver list for Stratimikos
Teuchos::ParameterList& stratLinSolParams =
newtonParams.sublist("Stratimikos Linear Solver");
// Teuchos::ParameterList& noxStratParams =
// stratLinSolParams.sublist("NOX Stratimikos Options");
Teuchos::ParameterList& stratParams =
stratLinSolParams.sublist("Stratimikos");
// Sublist for convergence tests
Teuchos::ParameterList& statusParams = noxParams->sublist("Status Tests");
statusParams.set("Test Type", "Combo");
statusParams.set("Number of Tests", 2);
statusParams.set("Combo Type", "OR");
Teuchos::ParameterList& normF = statusParams.sublist("Test 0");
normF.set("Test Type", "NormF");
normF.set("Tolerance", outer_tol);
normF.set("Scale Type", "Scaled");
Teuchos::ParameterList& maxIters = statusParams.sublist("Test 1");
maxIters.set("Test Type", "MaxIters");
maxIters.set("Maximum Iterations", 1);
// Create NOX interface
Teuchos::RCP<NOX::Epetra::ModelEvaluatorInterface> det_nox_interface =
Teuchos::rcp(new NOX::Epetra::ModelEvaluatorInterface(model));
// Create NOX linear system object
Teuchos::RCP<const Epetra_Vector> det_u = model->get_x_init();
Teuchos::RCP<Epetra_Operator> det_A = model->create_W();
Teuchos::RCP<NOX::Epetra::Interface::Required> det_iReq = det_nox_interface;
Teuchos::RCP<NOX::Epetra::Interface::Jacobian> det_iJac = det_nox_interface;
Teuchos::ParameterList det_printParams;
det_printParams.set("MyPID", MyPID);
det_printParams.set("Output Precision", 3);
det_printParams.set("Output Processor", 0);
det_printParams.set("Output Information", NOX::Utils::Error);
Teuchos::ParameterList det_lsParams;
Teuchos::ParameterList& det_stratParams =
det_lsParams.sublist("Stratimikos");
if (inner_krylov_solver == AZTECOO) {
det_stratParams.set("Linear Solver Type", "AztecOO");
Teuchos::ParameterList& aztecOOParams =
det_stratParams.sublist("Linear Solver Types").sublist("AztecOO").sublist("Forward Solve");
Teuchos::ParameterList& aztecOOSettings =
aztecOOParams.sublist("AztecOO Settings");
if (inner_krylov_method == GMRES) {
aztecOOSettings.set("Aztec Solver","GMRES");
}
else if (inner_krylov_method == CG) {
aztecOOSettings.set("Aztec Solver","CG");
}
aztecOOSettings.set("Output Frequency", 0);
aztecOOSettings.set("Size of Krylov Subspace", 100);
aztecOOParams.set("Max Iterations", inner_its);
aztecOOParams.set("Tolerance", inner_tol);
Teuchos::ParameterList& verbParams =
det_stratParams.sublist("Linear Solver Types").sublist("AztecOO").sublist("VerboseObject");
verbParams.set("Verbosity Level", "none");
}
else if (inner_krylov_solver == BELOS) {
det_stratParams.set("Linear Solver Type", "Belos");
Teuchos::ParameterList& belosParams =
det_stratParams.sublist("Linear Solver Types").sublist("Belos");
Teuchos::ParameterList* belosSolverParams = NULL;
if (inner_krylov_method == GMRES || inner_krylov_method == FGMRES) {
belosParams.set("Solver Type","Block GMRES");
belosSolverParams =
&(belosParams.sublist("Solver Types").sublist("Block GMRES"));
if (inner_krylov_method == FGMRES)
belosSolverParams->set("Flexible Gmres", true);
}
else if (inner_krylov_method == CG) {
belosParams.set("Solver Type","Block CG");
belosSolverParams =
&(belosParams.sublist("Solver Types").sublist("Block CG"));
}
else if (inner_krylov_method == RGMRES) {
belosParams.set("Solver Type","GCRODR");
belosSolverParams =
&(belosParams.sublist("Solver Types").sublist("GCRODR"));
}
belosSolverParams->set("Convergence Tolerance", inner_tol);
belosSolverParams->set("Maximum Iterations", inner_its);
belosSolverParams->set("Output Frequency",0);
belosSolverParams->set("Output Style",1);
belosSolverParams->set("Verbosity",0);
Teuchos::ParameterList& verbParams = belosParams.sublist("VerboseObject");
verbParams.set("Verbosity Level", "none");
}
det_stratParams.set("Preconditioner Type", "ML");
Teuchos::ParameterList& det_ML =
det_stratParams.sublist("Preconditioner Types").sublist("ML").sublist("ML Settings");
ML_Epetra::SetDefaults("SA", det_ML);
det_ML.set("ML output", 0);
det_ML.set("max levels",5);
det_ML.set("increasing or decreasing","increasing");
det_ML.set("aggregation: type", "Uncoupled");
det_ML.set("smoother: type","ML symmetric Gauss-Seidel");
det_ML.set("smoother: sweeps",2);
det_ML.set("smoother: pre or post", "both");
det_ML.set("coarse: max size", 200);
#ifdef HAVE_ML_AMESOS
det_ML.set("coarse: type","Amesos-KLU");
#else
det_ML.set("coarse: type","Jacobi");
#endif
Teuchos::RCP<NOX::Epetra::LinearSystem> det_linsys =
Teuchos::rcp(new NOX::Epetra::LinearSystemStratimikos(
det_printParams, det_lsParams, det_iJac,
det_A, *det_u));
// Setup stochastic Galerkin algorithmic parameters
Teuchos::RCP<Teuchos::ParameterList> sgParams =
Teuchos::rcp(new Teuchos::ParameterList);
Teuchos::ParameterList& sgOpParams =
sgParams->sublist("SG Operator");
Teuchos::ParameterList& sgPrecParams =
sgParams->sublist("SG Preconditioner");
if (!nonlinear_expansion) {
sgParams->set("Parameter Expansion Type", "Linear");
sgParams->set("Jacobian Expansion Type", "Linear");
}
if (opMethod == MATRIX_FREE)
sgOpParams.set("Operator Method", "Matrix Free");
else if (opMethod == KL_MATRIX_FREE)
sgOpParams.set("Operator Method", "KL Matrix Free");
else if (opMethod == KL_REDUCED_MATRIX_FREE) {
sgOpParams.set("Operator Method", "KL Reduced Matrix Free");
if (randField == UNIFORM || randField == RYS)
sgOpParams.set("Number of KL Terms", num_KL);
else
sgOpParams.set("Number of KL Terms", basis->size());
sgOpParams.set("KL Tolerance", outer_tol);
sgOpParams.set("Sparse 3 Tensor Drop Tolerance", outer_tol);
sgOpParams.set("Do Error Tests", true);
}
else if (opMethod == FULLY_ASSEMBLED)
sgOpParams.set("Operator Method", "Fully Assembled");
else
TEUCHOS_TEST_FOR_EXCEPTION(true, std::logic_error,
"Error! Unknown operator method " << opMethod
<< "." << std::endl);
if (precMethod == MEAN) {
sgPrecParams.set("Preconditioner Method", "Mean-based");
sgPrecParams.set("Mean Preconditioner Type", "ML");
Teuchos::ParameterList& precParams =
sgPrecParams.sublist("Mean Preconditioner Parameters");
precParams = det_ML;
}
else if(precMethod == GS) {
sgPrecParams.set("Preconditioner Method", "Gauss-Seidel");
sgPrecParams.sublist("Deterministic Solver Parameters") = det_lsParams;
sgPrecParams.set("Deterministic Solver", det_linsys);
sgPrecParams.set("Max Iterations", gs_prec_its);
sgPrecParams.set("Tolerance", gs_prec_tol);
}
else if (precMethod == AGS) {
sgPrecParams.set("Preconditioner Method", "Approximate Gauss-Seidel");
if (outer_krylov_method == CG)
sgPrecParams.set("Symmetric Gauss-Seidel", true);
sgPrecParams.set("Mean Preconditioner Type", "ML");
Teuchos::ParameterList& precParams =
sgPrecParams.sublist("Mean Preconditioner Parameters");
precParams = det_ML;
}
else if (precMethod == AJ) {
sgPrecParams.set("Preconditioner Method", "Approximate Jacobi");
sgPrecParams.set("Mean Preconditioner Type", "ML");
Teuchos::ParameterList& precParams =
sgPrecParams.sublist("Mean Preconditioner Parameters");
precParams = det_ML;
Teuchos::ParameterList& jacobiOpParams =
sgPrecParams.sublist("Jacobi SG Operator");
jacobiOpParams.set("Only Use Linear Terms", true);
}
else if (precMethod == ASC) {
sgPrecParams.set("Preconditioner Method", "Approximate Schur Complement");
sgPrecParams.set("Mean Preconditioner Type", "ML");
Teuchos::ParameterList& precParams =
sgPrecParams.sublist("Mean Preconditioner Parameters");
precParams = det_ML;
}
else if (precMethod == KP) {
sgPrecParams.set("Preconditioner Method", "Kronecker Product");
sgPrecParams.set("Only Use Linear Terms", true);
sgPrecParams.set("Mean Preconditioner Type", "ML");
Teuchos::ParameterList& meanPrecParams =
sgPrecParams.sublist("Mean Preconditioner Parameters");
meanPrecParams = det_ML;
sgPrecParams.set("G Preconditioner Type", "Ifpack");
Teuchos::ParameterList& GPrecParams =
sgPrecParams.sublist("G Preconditioner Parameters");
if (outer_krylov_method == GMRES || outer_krylov_method == FGMRES)
GPrecParams.set("Ifpack Preconditioner", "ILUT");
if (outer_krylov_method == CG)
GPrecParams.set("Ifpack Preconditioner", "ICT");
GPrecParams.set("Overlap", 1);
GPrecParams.set("fact: drop tolerance", 1e-4);
GPrecParams.set("fact: ilut level-of-fill", 1.0);
GPrecParams.set("schwarz: combine mode", "Add");
}
else if (precMethod == NONE) {
sgPrecParams.set("Preconditioner Method", "None");
}
else
TEUCHOS_TEST_FOR_EXCEPTION(true, std::logic_error,
"Error! Unknown preconditioner method " << precMethod
<< "." << std::endl);
// Create stochastic Galerkin model evaluator
Teuchos::RCP<Stokhos::SGModelEvaluator> sg_model =
Teuchos::rcp(new Stokhos::SGModelEvaluator(model, basis, Teuchos::null,
expansion, sg_parallel_data,
sgParams, scaleOP));
EpetraExt::ModelEvaluator::InArgs sg_inArgs = sg_model->createInArgs();
EpetraExt::ModelEvaluator::OutArgs sg_outArgs =
sg_model->createOutArgs();
// Set up stochastic parameters
Teuchos::RCP<Stokhos::EpetraVectorOrthogPoly> sg_p_init =
sg_model->create_p_sg(0);
for (int i=0; i<num_KL; i++) {
sg_p_init->term(i,0)[i] = 0.0;
sg_p_init->term(i,1)[i] = 1.0;
}
sg_model->set_p_sg_init(0, *sg_p_init);
// Setup stochastic initial guess
Teuchos::RCP<Stokhos::EpetraVectorOrthogPoly> sg_x_init =
sg_model->create_x_sg();
sg_x_init->init(0.0);
sg_model->set_x_sg_init(*sg_x_init);
// Create NOX interface
Teuchos::RCP<NOX::Epetra::ModelEvaluatorInterface> nox_interface =
Teuchos::rcp(new NOX::Epetra::ModelEvaluatorInterface(sg_model));
// Create NOX stochastic linear system object
Teuchos::RCP<const Epetra_Vector> u = sg_model->get_x_init();
Teuchos::RCP<const Epetra_Map> base_map = model->get_x_map();
Teuchos::RCP<const Epetra_Map> sg_map = sg_model->get_x_map();
Teuchos::RCP<Epetra_Operator> A = sg_model->create_W();
Teuchos::RCP<NOX::Epetra::Interface::Required> iReq = nox_interface;
Teuchos::RCP<NOX::Epetra::Interface::Jacobian> iJac = nox_interface;
// Build linear solver
Teuchos::RCP<NOX::Epetra::LinearSystem> linsys;
if (solve_method==SG_KRYLOV) {
bool has_M =
sg_outArgs.supports(EpetraExt::ModelEvaluator::OUT_ARG_WPrec);
Teuchos::RCP<Epetra_Operator> M;
Teuchos::RCP<NOX::Epetra::Interface::Preconditioner> iPrec;
if (has_M) {
M = sg_model->create_WPrec()->PrecOp;
iPrec = nox_interface;
}
stratParams.set("Preconditioner Type", "None");
if (outer_krylov_solver == AZTECOO) {
stratParams.set("Linear Solver Type", "AztecOO");
Teuchos::ParameterList& aztecOOParams =
stratParams.sublist("Linear Solver Types").sublist("AztecOO").sublist("Forward Solve");
Teuchos::ParameterList& aztecOOSettings =
aztecOOParams.sublist("AztecOO Settings");
if (outer_krylov_method == GMRES) {
aztecOOSettings.set("Aztec Solver","GMRES");
}
else if (outer_krylov_method == CG) {
aztecOOSettings.set("Aztec Solver","CG");
}
aztecOOSettings.set("Output Frequency", 1);
aztecOOSettings.set("Size of Krylov Subspace", 100);
aztecOOParams.set("Max Iterations", outer_its);
aztecOOParams.set("Tolerance", outer_tol);
stratLinSolParams.set("Preconditioner", "User Defined");
if (has_M)
linsys =
Teuchos::rcp(new NOX::Epetra::LinearSystemStratimikos(
printParams, stratLinSolParams, iJac, A, iPrec, M,
*u, true));
else
linsys =
Teuchos::rcp(new NOX::Epetra::LinearSystemStratimikos(
printParams, stratLinSolParams, iJac, A, *u));
}
else if (outer_krylov_solver == BELOS){
stratParams.set("Linear Solver Type", "Belos");
Teuchos::ParameterList& belosParams =
stratParams.sublist("Linear Solver Types").sublist("Belos");
Teuchos::ParameterList* belosSolverParams = NULL;
if (outer_krylov_method == GMRES || outer_krylov_method == FGMRES) {
belosParams.set("Solver Type","Block GMRES");
belosSolverParams =
&(belosParams.sublist("Solver Types").sublist("Block GMRES"));
if (outer_krylov_method == FGMRES)
belosSolverParams->set("Flexible Gmres", true);
}
else if (outer_krylov_method == CG) {
belosParams.set("Solver Type","Block CG");
belosSolverParams =
&(belosParams.sublist("Solver Types").sublist("Block CG"));
}
else if (inner_krylov_method == RGMRES) {
belosParams.set("Solver Type","GCRODR");
belosSolverParams =
&(belosParams.sublist("Solver Types").sublist("GCRODR"));
}
belosSolverParams->set("Convergence Tolerance", outer_tol);
belosSolverParams->set("Maximum Iterations", outer_its);
belosSolverParams->set("Output Frequency",1);
belosSolverParams->set("Output Style",1);
belosSolverParams->set("Verbosity",33);
stratLinSolParams.set("Preconditioner", "User Defined");
if (has_M)
linsys =
Teuchos::rcp(new NOX::Epetra::LinearSystemStratimikos(
printParams, stratLinSolParams, iJac, A, iPrec, M,
*u, true));
else
linsys =
Teuchos::rcp(new NOX::Epetra::LinearSystemStratimikos(
printParams, stratLinSolParams, iJac, A, *u));
}
}
else if (solve_method==SG_GS) {
lsParams.sublist("Deterministic Solver Parameters") = det_lsParams;
lsParams.set("Max Iterations", outer_its);
lsParams.set("Tolerance", outer_tol);
linsys =
Teuchos::rcp(new NOX::Epetra::LinearSystemSGGS(
printParams, lsParams, det_linsys, iReq, iJac,
basis, sg_parallel_data, A, base_map, sg_map));
}
else {
lsParams.sublist("Deterministic Solver Parameters") = det_lsParams;
lsParams.set("Max Iterations", outer_its);
lsParams.set("Tolerance", outer_tol);
Teuchos::ParameterList& jacobiOpParams =
lsParams.sublist("Jacobi SG Operator");
jacobiOpParams.set("Only Use Linear Terms", true);
linsys =
Teuchos::rcp(new NOX::Epetra::LinearSystemSGJacobi(
printParams, lsParams, det_linsys, iReq, iJac,
basis, sg_parallel_data, A, base_map, sg_map));
}
// Build NOX group
Teuchos::RCP<NOX::Epetra::Group> grp =
Teuchos::rcp(new NOX::Epetra::Group(printParams, iReq, *u, linsys));
// Create the Solver convergence test
Teuchos::RCP<NOX::StatusTest::Generic> statusTests =
NOX::StatusTest::buildStatusTests(statusParams, utils);
// Create the solver
Teuchos::RCP<NOX::Solver::Generic> solver =
NOX::Solver::buildSolver(grp, statusTests, noxParams);
// Solve the system
NOX::StatusTest::StatusType status;
{
TEUCHOS_FUNC_TIME_MONITOR("Total Solve Time");
status = solver->solve();
}
// Get final solution
const NOX::Epetra::Group& finalGroup =
dynamic_cast<const NOX::Epetra::Group&>(solver->getSolutionGroup());
const Epetra_Vector& finalSolution =
(dynamic_cast<const NOX::Epetra::Vector&>(finalGroup.getX())).getEpetraVector();
// Save final solution to file
EpetraExt::VectorToMatrixMarketFile("nox_solver_stochastic_solution.mm",
finalSolution);
// Save mean and variance to file
Teuchos::RCP<Stokhos::EpetraVectorOrthogPoly> sg_x_poly =
sg_model->create_x_sg(View, &finalSolution);
Epetra_Vector mean(*(model->get_x_map()));
Epetra_Vector std_dev(*(model->get_x_map()));
sg_x_poly->computeMean(mean);
sg_x_poly->computeStandardDeviation(std_dev);
EpetraExt::VectorToMatrixMarketFile("mean_gal.mm", mean);
EpetraExt::VectorToMatrixMarketFile("std_dev_gal.mm", std_dev);
// Evaluate SG responses at SG parameters
Teuchos::RCP<const Epetra_Vector> sg_p = sg_model->get_p_init(1);
Teuchos::RCP<Epetra_Vector> sg_g =
Teuchos::rcp(new Epetra_Vector(*(sg_model->get_g_map(0))));
sg_inArgs.set_p(1, sg_p);
sg_inArgs.set_x(Teuchos::rcp(&finalSolution,false));
sg_outArgs.set_g(0, sg_g);
sg_model->evalModel(sg_inArgs, sg_outArgs);
// Print mean and standard deviation of response
Teuchos::RCP<Stokhos::EpetraVectorOrthogPoly> sg_g_poly =
sg_model->create_g_sg(0, View, sg_g.get());
Epetra_Vector g_mean(*(model->get_g_map(0)));
Epetra_Vector g_std_dev(*(model->get_g_map(0)));
sg_g_poly->computeMean(g_mean);
sg_g_poly->computeStandardDeviation(g_std_dev);
std::cout.precision(16);
// std::cout << "\nResponse Expansion = " << std::endl;
// std::cout.precision(12);
// sg_g_poly->print(std::cout);
std::cout << "\nResponse Mean = " << std::endl << g_mean << std::endl;
std::cout << "Response Std. Dev. = " << std::endl << g_std_dev << std::endl;
if (status == NOX::StatusTest::Converged && MyPID == 0)
utils.out() << "Example Passed!" << std::endl;
}
Teuchos::TimeMonitor::summarize(std::cout);
Teuchos::TimeMonitor::zeroOutTimers();
}
catch (std::exception& e) {
std::cout << e.what() << std::endl;
}
catch (string& s) {
std::cout << s << std::endl;
}
catch (char *s) {
std::cout << s << std::endl;
}
catch (...) {
std::cout << "Caught unknown exception!" <<std:: endl;
}
#ifdef HAVE_MPI
MPI_Finalize() ;
#endif
}
int main(int argc, char *argv[]) {
int status=0; // 0 = pass, failures are incremented
bool success = true;
Teuchos::GlobalMPISession mpiSession(&argc,&argv);
Teuchos::RCP<Teuchos::FancyOStream> out(Teuchos::VerboseObjectBase::getDefaultOStream());
// Command-line argument for input file
Albany::CmdLineArgs cmd("input.xml", "inputSG.xml", "inputSG_adjoint.xml");
cmd.parse_cmdline(argc, argv, *out);
std::string xmlfilename;
std::string sg_xmlfilename;
std::string adjsg_xmlfilename;
bool do_initial_guess;
if (cmd.has_third_xml_file) {
xmlfilename = cmd.xml_filename;
sg_xmlfilename = cmd.xml_filename2;
adjsg_xmlfilename = cmd.xml_filename3;
do_initial_guess = true;
}
else if (cmd.has_second_xml_file) {
xmlfilename = "";
sg_xmlfilename = cmd.xml_filename;
adjsg_xmlfilename = cmd.xml_filename2;
do_initial_guess = false;
}
else {
*out << argv[0] << ": must supply at least 2 input files!\n";
std::exit(1);
}
try {
Teuchos::RCP<Teuchos::Time> totalTime =
Teuchos::TimeMonitor::getNewTimer("AlbanySGAdjoint: ***Total Time***");
Teuchos::TimeMonitor totalTimer(*totalTime); //start timer
// Setup communication objects
Teuchos::RCP<Epetra_Comm> globalComm =
Albany::createEpetraCommFromMpiComm(Albany_MPI_COMM_WORLD);
Teuchos::RCP<Stokhos::EpetraVectorOrthogPoly> sg_forward_solution;
//
// Solve forward problem
//
{
Teuchos::RCP<Teuchos::Time> forwardTime =
Teuchos::TimeMonitor::getNewTimer("AlbanySGAdjoint: ***Forward Solver Time***");
Teuchos::TimeMonitor forwardTimer(*forwardTime); //start timer
// Parse parameters
Teuchos::RCP<const Teuchos_Comm> comm =
Tpetra::DefaultPlatform::getDefaultPlatform().getComm();
// Connect vtune for performance profiling
if (cmd.vtune) {
Albany::connect_vtune(comm->getRank());
}
Albany::SolverFactory sg_slvrfctry(sg_xmlfilename, comm);
Teuchos::ParameterList& albanyParams = sg_slvrfctry.getParameters();
Teuchos::RCP< Teuchos::ParameterList> piroParams =
Teuchos::rcp(&(albanyParams.sublist("Piro")),false);
// Create stochastic Galerkin solver
Teuchos::RCP<Piro::Epetra::StokhosSolver> sg_solver =
Teuchos::rcp(new Piro::Epetra::StokhosSolver(piroParams, globalComm));
// Get comm for spatial problem
Teuchos::RCP<const Epetra_Comm> app_comm = sg_solver->getSpatialComm();
//MP, can we use comm instead of tappComm?
Teuchos::RCP<const Teuchos_Comm> tapp_comm = Albany::createTeuchosCommFromEpetraComm(app_comm);
// Compute initial guess if requested
Teuchos::RCP<Epetra_Vector> ig;
if (do_initial_guess) {
// Create solver
Albany::SolverFactory slvrfctry(xmlfilename, tapp_comm);
Teuchos::RCP<EpetraExt::ModelEvaluator> solver =
slvrfctry.create(tapp_comm, tapp_comm);
// Setup in/out args
EpetraExt::ModelEvaluator::InArgs params_in = solver->createInArgs();
EpetraExt::ModelEvaluator::OutArgs responses_out =
solver->createOutArgs();
int np = params_in.Np();
for (int i=0; i<np; i++) {
Teuchos::RCP<const Epetra_Vector> p = solver->get_p_init(i);
params_in.set_p(i, p);
}
int ng = responses_out.Ng();
for (int i=0; i<ng; i++) {
Teuchos::RCP<Epetra_Vector> g =
Teuchos::rcp(new Epetra_Vector(*(solver->get_g_map(i))));
responses_out.set_g(i, g);
}
// Evaluate model
solver->evalModel(params_in, responses_out);
// Print responses (not last one since that is x)
*out << std::endl;
out->precision(8);
for (int i=0; i<ng-1; i++) {
if (responses_out.get_g(i) != Teuchos::null)
*out << "Response " << i << " = " << std::endl
<< *(responses_out.get_g(i)) << std::endl;
}
}
// Create SG solver
Teuchos::RCP<Albany::Application> app;
Teuchos::RCP<EpetraExt::ModelEvaluator> model; {
model = sg_slvrfctry.createAlbanyAppAndModel(
app, tapp_comm, Petra::EpetraVector_To_TpetraVectorConst(
*ig, tapp_comm));
}
sg_solver->setup(model);
// Evaluate SG responses at SG parameters
EpetraExt::ModelEvaluator::InArgs sg_inArgs = sg_solver->createInArgs();
EpetraExt::ModelEvaluator::OutArgs sg_outArgs =
sg_solver->createOutArgs();
int np = sg_inArgs.Np();
for (int i=0; i<np; i++) {
if (sg_inArgs.supports(EpetraExt::ModelEvaluator::IN_ARG_p_sg, i)) {
Teuchos::RCP<const Stokhos::EpetraVectorOrthogPoly> p_sg =
sg_solver->get_p_sg_init(i);
sg_inArgs.set_p_sg(i, p_sg);
}
}
int ng = sg_outArgs.Ng();
for (int i=0; i<ng; i++) {
if (sg_outArgs.supports(EpetraExt::ModelEvaluator::OUT_ARG_g_sg, i)) {
Teuchos::RCP<Stokhos::EpetraVectorOrthogPoly> g_sg =
sg_solver->create_g_sg(i);
sg_outArgs.set_g_sg(i, g_sg);
}
}
sg_solver->evalModel(sg_inArgs, sg_outArgs);
for (int i=0; i<ng-1; i++) {
// Don't loop over last g which is x, since it is a long vector
// to print out.
if (sg_outArgs.supports(EpetraExt::ModelEvaluator::OUT_ARG_g_sg, i)) {
// Print mean and standard deviation
Teuchos::RCP<Stokhos::EpetraVectorOrthogPoly> g_sg =
sg_outArgs.get_g_sg(i);
Epetra_Vector g_mean(*(sg_solver->get_g_map(i)));
Epetra_Vector g_std_dev(*(sg_solver->get_g_map(i)));
g_sg->computeMean(g_mean);
g_sg->computeStandardDeviation(g_std_dev);
out->precision(12);
*out << "Response " << i << " Mean = " << std::endl
<< g_mean << std::endl;
*out << "Response " << i << " Std. Dev. = " << std::endl
<< g_std_dev << std::endl;
status += sg_slvrfctry.checkSGTestResults(0, g_sg);
}
}
*out << "\nNumber of Failed Comparisons: " << status << std::endl;
sg_forward_solution = sg_outArgs.get_g_sg(ng-1);
}
/* Space reserved for the projection of the forward solution onto
the higher order basis for the adjoint solution.
In general, this will require projecting is physical space
as well as in stochastic space.
*/
//
// Solve adjoint problem
//
{
Teuchos::RCP<Teuchos::Time> adjointTime =
Teuchos::TimeMonitor::getNewTimer("AlbanySG: ***Adjoint Solver Time***");
Teuchos::TimeMonitor adjtotalTimer(*adjointTime); //start timer
// Parse parameters
Albany::SolverFactory sg_slvrfctry(adjsg_xmlfilename,
Albany::createTeuchosCommFromMpiComm(Albany_MPI_COMM_WORLD));
Teuchos::ParameterList& albanyParams = sg_slvrfctry.getParameters();
Teuchos::RCP< Teuchos::ParameterList> piroParams =
Teuchos::rcp(&(albanyParams.sublist("Piro")),false);
// Create stochastic Galerkin solver
Teuchos::RCP<Piro::Epetra::StokhosSolver> sg_solver =
Teuchos::rcp(new Piro::Epetra::StokhosSolver(piroParams, globalComm));
// Get comm for spatial problem
Teuchos::RCP<const Epetra_Comm> app_comm = sg_solver->getSpatialComm();
Teuchos::RCP<const Teuchos_Comm> tapp_comm = Albany::createTeuchosCommFromEpetraComm(app_comm);
// Create SG solver
Teuchos::RCP<Albany::Application> app;
Teuchos::RCP<EpetraExt::ModelEvaluator> model =
sg_slvrfctry.createAlbanyAppAndModel(app, tapp_comm);
sg_solver->setup(model);
// Set projected forward solution as the initial guess
sg_solver->set_x_sg_init(*sg_forward_solution);
// Evaluate SG responses at SG parameters
EpetraExt::ModelEvaluator::InArgs sg_inArgs = sg_solver->createInArgs();
EpetraExt::ModelEvaluator::OutArgs sg_outArgs =
sg_solver->createOutArgs();
int np = sg_inArgs.Np();
for (int i=0; i<np; i++) {
if (sg_inArgs.supports(EpetraExt::ModelEvaluator::IN_ARG_p_sg, i)) {
Teuchos::RCP<const Stokhos::EpetraVectorOrthogPoly> p_sg =
sg_solver->get_p_sg_init(i);
sg_inArgs.set_p_sg(i, p_sg);
}
}
int ng = sg_outArgs.Ng();
for (int i=0; i<ng; i++) {
if (sg_outArgs.supports(EpetraExt::ModelEvaluator::OUT_ARG_g_sg, i)) {
Teuchos::RCP<Stokhos::EpetraVectorOrthogPoly> g_sg =
sg_solver->create_g_sg(i);
sg_outArgs.set_g_sg(i, g_sg);
}
}
sg_solver->evalModel(sg_inArgs, sg_outArgs);
for (int i=0; i<ng-1; i++) {
// Don't loop over last g which is x, since it is a long vector
// to print out.
if (sg_outArgs.supports(EpetraExt::ModelEvaluator::OUT_ARG_g_sg, i)) {
// Print mean and standard deviation
Teuchos::RCP<Stokhos::EpetraVectorOrthogPoly> g_sg =
sg_outArgs.get_g_sg(i);
Epetra_Vector g_mean(*(sg_solver->get_g_map(i)));
Epetra_Vector g_std_dev(*(sg_solver->get_g_map(i)));
g_sg->computeMean(g_mean);
g_sg->computeStandardDeviation(g_std_dev);
out->precision(12);
*out << "Response " << i << " Mean = " << std::endl
<< g_mean << std::endl;
*out << "Response " << i << " Std. Dev. = " << std::endl
<< g_std_dev << std::endl;
status += sg_slvrfctry.checkSGTestResults(0, g_sg);
}
}
*out << "\nNumber of Failed Comparisons: " << status << std::endl;
/* Space reserved for computing the error representation which involves
integrating over both physical and stochastic space and may require
a number of projections.
*/
}
totalTimer.~TimeMonitor();
Teuchos::TimeMonitor::summarize(std::cout,false,true,false);
}
TEUCHOS_STANDARD_CATCH_STATEMENTS(true, std::cerr, success);
if (!success) status+=10000;
return status;
}
int main(int argc, char *argv[]) {
int status=0; // 0 = pass, failures are incremented
bool success = true;
Teuchos::GlobalMPISession mpiSession(&argc,&argv);
Teuchos::RCP<Teuchos::FancyOStream> out(Teuchos::VerboseObjectBase::getDefaultOStream());
// Command-line argument for input file
Albany::CmdLineArgs cmd("input.xml", "inputSG.xml");
cmd.parse_cmdline(argc, argv, *out);
std::string xmlfilename;
std::string sg_xmlfilename;
bool do_initial_guess;
if (cmd.has_second_xml_file) {
xmlfilename = cmd.xml_filename;
sg_xmlfilename = cmd.xml_filename2;
do_initial_guess = true;
}
else if (cmd.has_first_xml_file) {
xmlfilename = "";
sg_xmlfilename = cmd.xml_filename;
do_initial_guess = false;
}
else {
xmlfilename = "";
sg_xmlfilename = "inputSG.xml";
do_initial_guess = false;
}
try {
Teuchos::RCP<Teuchos::Time> totalTime =
Teuchos::TimeMonitor::getNewTimer("AlbanySG: ***Total Time***");
Teuchos::TimeMonitor totalTimer(*totalTime); //start timer
// Setup communication objects
Teuchos::RCP<Epetra_Comm> globalComm =
Albany::createEpetraCommFromMpiComm(Albany_MPI_COMM_WORLD);
Teuchos::RCP<const Teuchos_Comm> tcomm =
Tpetra::DefaultPlatform::getDefaultPlatform().getComm();
// Connect vtune for performance profiling
if (cmd.vtune) {
Albany::connect_vtune(tcomm->getRank());
}
// Parse parameters
Albany::SolverFactory sg_slvrfctry(sg_xmlfilename, tcomm);
Teuchos::ParameterList& albanyParams = sg_slvrfctry.getParameters();
Teuchos::RCP< Teuchos::ParameterList> piroParams =
Teuchos::rcp(&(albanyParams.sublist("Piro")),false);
// If SG is not enabled and user chose "Direct" or "AD",
// for "SG Method", change this "Global", which always works
#ifndef ALBANY_SG
std::string sg_method =
piroParams->sublist("Stochastic Galerkin").get("SG Method", "Global");
if (sg_method == "Direct" || sg_method == "AD") {
piroParams->sublist("Stochastic Galerkin").set("SG Method", "Global");
*out << "**********************************************************\n"
<< "* WARNING! Direct SG method was chosen, however *\n"
<< "* ALBANY_SG is not enabled. Changing to Global method. *\n"
<< "**********************************************************\n"
<< std::endl;
}
#endif
// Create stochastic Galerkin solver
Teuchos::RCP<Piro::Epetra::StokhosSolver> sg_solver =
Teuchos::rcp(new Piro::Epetra::StokhosSolver(piroParams, globalComm));
// Get comm for spatial problem
Teuchos::RCP<const Epetra_Comm> app_comm = sg_solver->getSpatialComm();
Teuchos::RCP<const Teuchos_Comm> tapp_comm = Albany::createTeuchosCommFromEpetraComm(app_comm);
// Compute initial guess if requested
Teuchos::RCP<Epetra_Vector> ig;
if (do_initial_guess) {
// Create solver
Albany::SolverFactory slvrfctry(
xmlfilename, tcomm);
Teuchos::RCP<EpetraExt::ModelEvaluator> solver =
slvrfctry.create(tapp_comm, tapp_comm);
// Setup in/out args
EpetraExt::ModelEvaluator::InArgs params_in = solver->createInArgs();
EpetraExt::ModelEvaluator::OutArgs responses_out =
solver->createOutArgs();
int np = params_in.Np();
for (int i=0; i<np; i++) {
Teuchos::RCP<const Epetra_Vector> p = solver->get_p_init(i);
params_in.set_p(i, p);
}
int ng = responses_out.Ng();
for (int i=0; i<ng; i++) {
Teuchos::RCP<Epetra_Vector> g =
Teuchos::rcp(new Epetra_Vector(*(solver->get_g_map(i))));
responses_out.set_g(i, g);
}
// Evaluate model
solver->evalModel(params_in, responses_out);
// Print responses (not last one since that is x)
*out << std::endl;
out->precision(8);
for (int i=0; i<ng-1; i++) {
if (responses_out.get_g(i) != Teuchos::null)
*out << "Response " << i << " = " << std::endl
<< *(responses_out.get_g(i)) << std::endl;
}
// Get final solution as initial guess
ig = responses_out.get_g(ng-1);
Teuchos::TimeMonitor::summarize(std::cout,false,true,false);
}
// Create SG solver
Teuchos::RCP<Albany::Application> app;
Teuchos::RCP<const Tpetra_Vector> initial_guessT;
if (Teuchos::nonnull(ig)) {
initial_guessT = Petra::EpetraVector_To_TpetraVectorConst(*ig, tcomm);
}
Teuchos::RCP<EpetraExt::ModelEvaluator> model =
sg_slvrfctry.createAlbanyAppAndModel(app, tcomm, initial_guessT);
// Hack in rigid body modes for ML
{
Teuchos::RCP<Teuchos::ParameterList> sg_solver_params =
Teuchos::sublist(Teuchos::sublist(piroParams, "Stochastic Galerkin"), "SG Solver Parameters");
Teuchos::RCP<Teuchos::ParameterList> sg_prec_params =
Teuchos::sublist(sg_solver_params, "SG Preconditioner");
if (sg_prec_params->isParameter("Mean Preconditioner Type")) {
if (sg_prec_params->get<std::string>("Mean Preconditioner Type") == "ML") {
Teuchos::RCP<Teuchos::ParameterList> ml_params =
Teuchos::sublist(sg_prec_params, "Mean Preconditioner Parameters");
const Teuchos::RCP<Albany::RigidBodyModes>&
rbm = app->getProblem()->getNullSpace();
// Previously, updateMLPL called importML, but there was no coordinate
// data yet. Now we just update the parameter list.
rbm->updatePL(ml_params);
sg_solver->resetSolverParameters(*sg_solver_params);
}
}
}
// Setup SG solver
{
const Teuchos::RCP<NOX::Epetra::Observer > NOX_observer =
Teuchos::rcp(new Albany_NOXObserver(app));
sg_solver->setup(model, NOX_observer);
}
// Evaluate SG responses at SG parameters
EpetraExt::ModelEvaluator::InArgs sg_inArgs = sg_solver->createInArgs();
EpetraExt::ModelEvaluator::OutArgs sg_outArgs =
sg_solver->createOutArgs();
int np = sg_inArgs.Np();
for (int i=0; i<np; i++) {
if (sg_inArgs.supports(EpetraExt::ModelEvaluator::IN_ARG_p_sg, i)) {
Teuchos::RCP<const Stokhos::EpetraVectorOrthogPoly> p_sg =
sg_solver->get_p_sg_init(i);
sg_inArgs.set_p_sg(i, p_sg);
}
}
// By default, request the sensitivities if not explicitly disabled
const bool computeSensitivities =
sg_slvrfctry.getAnalysisParameters().sublist("Solve").get("Compute Sensitivities", true);
int ng = sg_outArgs.Ng();
for (int i=0; i<ng; i++) {
if (sg_outArgs.supports(EpetraExt::ModelEvaluator::OUT_ARG_g_sg, i)) {
Teuchos::RCP<Stokhos::EpetraVectorOrthogPoly> g_sg =
sg_solver->create_g_sg(i);
sg_outArgs.set_g_sg(i, g_sg);
}
for (int j=0; j<np; j++) {
EpetraExt::ModelEvaluator::DerivativeSupport ds =
sg_outArgs.supports(EpetraExt::ModelEvaluator::OUT_ARG_DgDp_sg,i,j);
if (computeSensitivities &&
ds.supports(EpetraExt::ModelEvaluator::DERIV_MV_BY_COL)) {
int ncol = sg_solver->get_p_map(j)->NumMyElements();
Teuchos::RCP<Stokhos::EpetraMultiVectorOrthogPoly> dgdp_sg =
sg_solver->create_g_mv_sg(i,ncol);
sg_outArgs.set_DgDp_sg(i, j, dgdp_sg);
}
}
}
sg_solver->evalModel(sg_inArgs, sg_outArgs);
bool printResponse =
albanyParams.sublist("Problem").get("Print Response Expansion", true);
for (int i=0; i<ng-1; i++) {
// Don't loop over last g which is x, since it is a long vector
// to print out.
if (sg_outArgs.supports(EpetraExt::ModelEvaluator::OUT_ARG_g_sg, i)) {
// Print mean and standard deviation
Teuchos::RCP<Stokhos::EpetraVectorOrthogPoly> g_sg =
sg_outArgs.get_g_sg(i);
if (g_sg != Teuchos::null && app->getResponse(i)->isScalarResponse()) {
Epetra_Vector g_mean(*(sg_solver->get_g_map(i)));
Epetra_Vector g_std_dev(*(sg_solver->get_g_map(i)));
g_sg->computeMean(g_mean);
g_sg->computeStandardDeviation(g_std_dev);
out->precision(12);
out->setf(std::ios::scientific);
*out << "Response " << i << " Mean = " << std::endl
<< g_mean << std::endl;
*out << "Response " << i << " Std. Dev. = " << std::endl
<< g_std_dev << std::endl;
if (printResponse) {
*out << "Response " << i << " = " << std::endl
<< *g_sg << std::endl;
for (int j=0; j<np; j++) {
EpetraExt::ModelEvaluator::DerivativeSupport ds =
sg_outArgs.supports(EpetraExt::ModelEvaluator::OUT_ARG_DgDp_sg,i,j);
if (!ds.none()) {
Teuchos::RCP<Stokhos::EpetraMultiVectorOrthogPoly> dgdp_sg =
sg_outArgs.get_DgDp_sg(i,j).getMultiVector();
if (dgdp_sg != Teuchos::null)
*out << "Response " << i << " Derivative " << j << " = "
<< std::endl << *dgdp_sg << std::endl;
}
}
}
status += sg_slvrfctry.checkSGTestResults(i, g_sg, &g_mean, &g_std_dev);
}
}
}
*out << "\nNumber of Failed Comparisons: " << status << std::endl;
totalTimer.~TimeMonitor();
Teuchos::TimeMonitor::summarize(std::cout,false,true,false);
Teuchos::TimeMonitor::zeroOutTimers();
}
TEUCHOS_STANDARD_CATCH_STATEMENTS(true, std::cerr, success);
if (!success) status+=10000;
return status;
}
