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Richardson.cpp
/* ******************************************************************** */
/* See the file COPYRIGHT for a complete copyright notice, contact */
/* person and disclaimer. */
/* ******************************************************************** */
#include "ml_config.h"
#include "ml_common.h"
#if defined(HAVE_ML_MLAPI) && defined(HAVE_ML_GALERI)
#include "MLAPI_Space.h"
#include "MLAPI_Operator.h"
#include "MLAPI_Gallery.h"
using namespace Teuchos;
using namespace MLAPI;
// ============== //
// example driver //
// ============== //
int main(int argc, char *argv[])
{
#ifdef HAVE_MPI
MPI_Init(&argc,&argv);
#endif
try {
// Initialize the workspace and set the output level
Init();
// global dimension of the problem
int NumGlobalElements = 10000;
// define the space for fine level vectors and operators.
Space S(NumGlobalElements);
// define the linear system matrix.
Operator A = Gallery("Laplace2D", S);
// set parameters for aggregation and smoothers
// NOTE: only a limited subset of the parameters accepted by
// class ML_Epetra::MultiLevelPreconditioner is supported
// by MLAPI::MultiLevelSA
Teuchos::ParameterList MLList;
MLList.set("max levels",3);
MLList.set("aggregation: type", "Uncoupled");
MLList.set("aggregation: damping factor", 1.333);
MLList.set("smoother: type","symmetric Gauss-Seidel");
MLList.set("smoother: sweeps",1);
MLList.set("smoother: damping factor",1.0);
MLList.set("coarse: max size",3);
MLList.set("coarse: type","Amesos-KLU");
MultiLevelSA P(A, MLList);
// Here we define a simple Richardson method for the
// solution of A x = b. The preconditioner is P,
// the exact solution (x_ex) is a random vector, the
// starting solution (x) is the zero vector.
MultiVector x_ex(S);
x_ex.Random();
b = A * x_ex;
x = 0.0;
double OldNorm = 1.0;
double Tolerance = 1e-13;
int MaxIters = 30;
// ================ //
// Richardson cycle //
// ================ //
for (int i = 0 ; i < MaxIters ; ++i) {
r = b - A * x; // new residual
z = P * r; // apply preconditioner with zero initial guess
x = x + z; // update solution
// compute the A-norm of the error
double NewNorm = sqrt((x - x_ex) * (A * (x - x_ex)));
if (GetMyPID() == 0 && i) {
std::cout << "||x - x_ex||_A = ";
std::cout.width(15);
std::cout << NewNorm << ", ";
std::cout << "reduction = ";
std::cout.width(15);
std::cout << NewNorm / OldNorm << std::endl;
}
if (NewNorm < Tolerance)
break;
OldNorm = NewNorm;
}
// finalize the MLAPI workspace
}
catch (const int e) {
std::cout << "Caught integer exception, code = " << e << std::endl;
}
catch (...) {
std::cout << "problems here..." << std::endl;
}
#ifdef HAVE_MPI
MPI_Finalize();
#endif
return(0);
}
#else
#include "ml_include.h"
int main(int argc, char *argv[])
{
#ifdef HAVE_MPI
MPI_Init(&argc,&argv);
#endif
puts("This MLAPI example requires the following configuration options:");
puts("\t--enable-epetra");
puts("\t--enable-teuchos");
puts("\t--enable-ifpack");
puts("\t--enable-amesos");
puts("\t--enable-galeri");
puts("Please check your configure line.");
#ifdef HAVE_MPI
MPI_Finalize();
#endif
return(0);
}
#endif // if defined(HAVE_ML_MLAPI)
Overloaded operators for MultiVector's, Operator's, and InverseOpereator's.
Standard smoothed aggregation multilevel preconditioner.
MLAPI wrapper for double vectors.
Basic class to define operators within MLAPI.
Class to specify the number and distribution among processes of elements.
Black-box multilevel smoothed aggregation preconditioner.
Definition MLAPI_MultiLevelSA.h:47
Basic class for distributed double-precision vectors.
Definition MLAPI_MultiVector.h:103
Operator: basic class to define operators within MLAPI.
Definition MLAPI_Operator.h:44
Specifies the number and distribution among processes of elements.
Definition MLAPI_Space.h:40
MLAPI: Default namespace for all MLAPI objects and functions.
Definition MLAPI_Aggregation.h:24
void Finalize()
Destroys the MLAPI workspace.