int main(int argc, char *argv[])
{
#ifdef EPETRA_MPI
MPI_Init(&argc,&argv);
Epetra_MpiComm Comm(MPI_COMM_WORLD);
#else
Epetra_SerialComm Comm;
#endif
Epetra_Time Time(Comm);
// Create the linear problem using the class `Trilinos_Util::CrsMatrixGallery.'
// Various matrix examples are supported; please refer to the
// Trilinos tutorial for more details.
// create Aztec stuff
int proc_config[AZ_PROC_SIZE], options[AZ_OPTIONS_SIZE];
#ifdef ML_MPI
/* get number of processors and the name of this processor */
AZ_set_proc_config(proc_config, MPI_COMM_WORLD);
int proc = proc_config[AZ_node];
int nprocs = proc_config[AZ_N_procs];
#else
AZ_set_proc_config(proc_config, AZ_NOT_MPI);
int proc = 0;
int nprocs = 1;
#endif
// read in the matrix size
FILE *fp = fopen("ExampleMatrices/cantilever2D/data_matrix.txt","r");
int leng;
fscanf(fp,"%d",&leng);
int num_PDE_eqns=2;
int N_grid_pts = leng/num_PDE_eqns;
// make a linear distribution of the matrix respecting the blocks size
int leng1 = leng/nprocs;
int leng2 = leng-leng1*nprocs;
if (proc >= leng2)
{
leng2 += (proc*leng1);
}
else
{
leng1++;
leng2 = proc*leng1;
}
int N_update = leng1;
int* update = new int[N_update+1];
int i;
double *val=NULL;
int *bindx=NULL;
for (i=0; i<N_update; i++) update[i] = i+leng2;
// create the Epetra_CrSMatrix
Epetra_Map* StandardMap = new Epetra_Map(leng,N_update,update,0,Comm);
Epetra_CrsMatrix* A = new Epetra_CrsMatrix(Copy,*StandardMap,1);
AZ_input_msr_matrix("ExampleMatrices/cantilever2D/data_matrix.txt",
update, &val, &bindx, N_update, proc_config);
for (i=0; i<leng; i++)
{
int row = update[i];
A->SumIntoGlobalValues(row,1,&(val[i]),&row);
A->SumIntoGlobalValues(row,bindx[i+1]-bindx[i],&(val[bindx[i]]),&(bindx[bindx[i]]));
}
A->TransformToLocal();
// create solution and right-hand side (MultiVectors are fine as well)
Epetra_Vector* LHS = new Epetra_Vector(A->OperatorDomainMap());
Epetra_Vector* RHS = new Epetra_Vector(A->OperatorRangeMap());
LHS->Random();
RHS->Random();
// build the epetra linear problem
Epetra_LinearProblem Problem(A, LHS, RHS);
// Construct a solver object for this problem
AztecOO solver(Problem);
// =========================== begin of ML part ===========================
// create a parameter list for ML options
ParameterList MLList;
// set defaults for classic smoothed aggregation
ML_Epetra::SetDefaults("SA",MLList);
MLList.set("aggregation: damping factor", 0.0);
// number of relaxation sweeps
MLList.set("adaptive: max sweeps", 10);
// number of additional null space vectors to compute
MLList.set("adaptive: num vectors",2);
#if 1
ML_Epetra::MultiLevelPreconditioner* MLPrec =
new ML_Epetra::MultiLevelPreconditioner(dynamic_cast<Epetra_RowMatrix&>(*A), MLList, false);
// need to allocate and fill the null space (also the
// default one, as in this case). This vector is no longer
// needed after a call to ComputeAdaptivePreconditioner().
int NullSpaceSize = 2;
vector<double> NullSpace((NullSpaceSize*A->NumMyRows()));
for (i = 0 ; i < A->NumMyRows() ; ++i)
{
NullSpace[i] = 1.0;
++i;
NullSpace[i] = 0.0;
}
for (i = A->NumMyRows() ; i < 2*A->NumMyRows() ; ++i)
{
NullSpace[i] = 0.0;
++i;
NullSpace[i] = 1.0;
}
MLPrec->ComputeAdaptivePreconditioner(NullSpaceSize,&NullSpace[0]);
#else
ML_Epetra::MultiLevelPreconditioner* MLPrec =
new ML_Epetra::MultiLevelPreconditioner(dynamic_cast<Epetra_RowMatrix&>(*A), MLList);
#endif
// tell AztecOO to use this preconditioner, then solve
solver.SetPrecOperator(MLPrec);
// =========================== end of ML part =============================
solver.SetAztecOption(AZ_solver, AZ_gmres);
solver.SetAztecOption(AZ_output, 32);
// solve with 500 iterations and 1e-12 tolerance
solver.Iterate(1550, 1e-5);
delete MLPrec;
// compute the real residual
double residual, diff;
if( Comm.MyPID()==0 ) {
cout << "||b-Ax||_2 = " << residual << endl;
cout << "||x_exact - x||_2 = " << diff << endl;
cout << "Total Time = " << Time.ElapsedTime() << endl;
}
#ifdef EPETRA_MPI
MPI_Finalize();
#endif
return(0);
}
