int power_method(bool TransA, Epetra_CrsMatrix& A, Epetra_Vector& q, Epetra_Vector& z,
Epetra_Vector& resid, double* lambda, int niters, double tolerance, bool verbose)
{
// Fill z with random Numbers
z.Random();
// variable needed for iteration
double normz, residual;
int ierr = 1;
for(int iter = 0; iter < niters; iter++) {
z.Norm2(&normz); // Compute 2-norm of z
q.Scale(1.0/normz, z);
A.Multiply(TransA, q, z); // Compute z = A*q // SEGFAULT HAPPENS HERE
q.Dot(z, lambda); // Approximate maximum eigenvaluE
if(iter%100==0 || iter+1==niters) {
resid.Update(1.0, z, -(*lambda), q, 0.0); // Compute A*q - lambda*q
resid.Norm2(&residual);
if(verbose) cout << "Iter = " << iter << " Lambda = " << *lambda
<< " Residual of A*q - lambda*q = " << residual << endl;
}
if(residual < tolerance) {
ierr = 0;
break;
}
}
return(ierr);
}
int
powerMethod (double & lambda,
Epetra_CrsMatrix& A,
const int niters,
const double tolerance,
const bool verbose)
{
// In the power iteration, z = A*q. Thus, q must be in the domain
// of A, and z must be in the range of A. The residual vector is of
// course in the range of A.
Epetra_Vector q (A.OperatorDomainMap ());
Epetra_Vector z (A.OperatorRangeMap ());
Epetra_Vector resid (A.OperatorRangeMap ());
Epetra_Flops* counter = A.GetFlopCounter();
if (counter != 0) {
q.SetFlopCounter(A);
z.SetFlopCounter(A);
resid.SetFlopCounter(A);
}
// Initialize the starting vector z with random data.
z.Random();
double normz, residual;
int ierr = 1;
for (int iter = 0; iter < niters; ++iter)
{
z.Norm2 (&normz); // normz := ||z||_2
q.Scale (1.0/normz, z); // q := z / normz
A.Multiply(false, q, z); // z := A * q
q.Dot(z, &lambda); // lambda := dot (q, z)
// Compute the residual vector and display status output every
// 100 iterations, or if we have reached the maximum number of
// iterations.
if (iter % 100 == 0 || iter + 1 == niters)
{
resid.Update (1.0, z, -lambda, q, 0.0); // resid := A*q - lambda*q
resid.Norm2 (&residual); // residual := ||resid||_2
if (verbose)
cout << "Iter = " << iter << " Lambda = " << lambda
<< " Residual of A*q - lambda*q = " << residual << endl;
}
if (residual < tolerance) { // We've converged!
ierr = 0;
break;
}
}
return ierr;
}
int TestOneMatrix( std::string HBname, std::string MMname, std::string TRIname, Epetra_Comm &Comm, bool verbose ) {
if ( Comm.MyPID() != 0 ) verbose = false ;
Epetra_Map * readMap = 0;
Epetra_CrsMatrix * HbA = 0;
Epetra_Vector * Hbx = 0;
Epetra_Vector * Hbb = 0;
Epetra_Vector * Hbxexact = 0;
Epetra_CrsMatrix * TriplesA = 0;
Epetra_Vector * Triplesx = 0;
Epetra_Vector * Triplesb = 0;
Epetra_Vector * Triplesxexact = 0;
Epetra_CrsMatrix * MatrixMarketA = 0;
Epetra_Vector * MatrixMarketx = 0;
Epetra_Vector * MatrixMarketb = 0;
Epetra_Vector * MatrixMarketxexact = 0;
int TRI_Size = TRIname.size() ;
std::string LastFiveBytes = TRIname.substr( EPETRA_MAX(0,TRI_Size-5), TRI_Size );
if ( LastFiveBytes == ".TimD" ) {
// Call routine to read in a file with a Tim Davis header and zero-based indexing
EPETRA_CHK_ERR( Trilinos_Util_ReadTriples2Epetra64( &TRIname[0], false, Comm,
readMap, TriplesA, Triplesx,
Triplesb, Triplesxexact, false, true, true ) );
delete readMap;
} else {
if ( LastFiveBytes == ".triU" ) {
// Call routine to read in unsymmetric Triplet matrix
EPETRA_CHK_ERR( Trilinos_Util_ReadTriples2Epetra64( &TRIname[0], false, Comm,
readMap, TriplesA, Triplesx,
Triplesb, Triplesxexact, false, false ) );
delete readMap;
} else {
if ( LastFiveBytes == ".triS" ) {
// Call routine to read in symmetric Triplet matrix
EPETRA_CHK_ERR( Trilinos_Util_ReadTriples2Epetra64( &TRIname[0], true, Comm,
readMap, TriplesA, Triplesx,
Triplesb, Triplesxexact, false, false ) );
delete readMap;
} else {
assert( false ) ;
}
}
}
EPETRA_CHK_ERR( Trilinos_Util_ReadMatrixMarket2Epetra64( &MMname[0], Comm, readMap,
MatrixMarketA, MatrixMarketx,
MatrixMarketb, MatrixMarketxexact) );
delete readMap;
// Call routine to read in HB problem
Trilinos_Util_ReadHb2Epetra64( &HBname[0], Comm, readMap, HbA, Hbx,
Hbb, Hbxexact) ;
#if 0
std::cout << " HbA " ;
HbA->Print( std::cout ) ;
std::cout << std::endl ;
std::cout << " MatrixMarketA " ;
MatrixMarketA->Print( std::cout ) ;
std::cout << std::endl ;
std::cout << " TriplesA " ;
TriplesA->Print( std::cout ) ;
std::cout << std::endl ;
#endif
int TripleErr = 0 ;
int MMerr = 0 ;
for ( int i = 0 ; i < 10 ; i++ )
{
double resid_Hb_Triples;
double resid_Hb_Matrix_Market;
double norm_A ;
Hbx->Random();
//
// Set the output vectors to different values:
//
Triplesb->PutScalar(1.1);
Hbb->PutScalar(1.2);
MatrixMarketb->PutScalar(1.3);
HbA->Multiply( false, *Hbx, *Hbb );
norm_A = HbA->NormOne( ) ;
TriplesA->Multiply( false, *Hbx, *Triplesb );
Triplesb->Update( 1.0, *Hbb, -1.0 ) ;
MatrixMarketA->Multiply( false, *Hbx, *MatrixMarketb );
MatrixMarketb->Update( 1.0, *Hbb, -1.0 ) ;
Triplesb->Norm1( &resid_Hb_Triples ) ;
MatrixMarketb->Norm1( &resid_Hb_Matrix_Market ) ;
TripleErr += ( resid_Hb_Triples > 1e-11 * norm_A ) ;
MMerr += ( resid_Hb_Matrix_Market > 1e-11 * norm_A ) ;
if ( verbose && resid_Hb_Triples > 1e-11 * norm_A )
std::cout << " resid_Hb_Triples = " << resid_Hb_Triples
<< " norm_A = " << norm_A << std::endl ;
if ( verbose && resid_Hb_Matrix_Market > 1e-11 * norm_A )
std::cout << " resid_Hb_Matrix_Market = " << resid_Hb_Matrix_Market
<< " norm_A = " << norm_A << std::endl ;
}
if ( verbose ) {
if ( TripleErr ) std::cout << " Error in reading " << HBname << " or " << TRIname << std::endl ;
if ( MMerr ) std::cout << " Error in reading " << HBname << " or " << MMname << std::endl ;
}
delete HbA;
delete Hbx;
delete Hbb;
delete Hbxexact;
delete TriplesA;
delete Triplesx;
delete Triplesb;
delete Triplesxexact;
delete MatrixMarketA;
delete MatrixMarketx;
delete MatrixMarketb;
delete MatrixMarketxexact;
delete readMap;
return TripleErr+MMerr ;
}
// ================================================ ====== ==== ==== == =
// the tentative null space is in input because the user
// has to remember to allocate and fill it, and then to delete
// it after calling this method.
int ML_Epetra::MultiLevelPreconditioner::
ComputeAdaptivePreconditioner(int TentativeNullSpaceSize,
double* TentativeNullSpace)
{
if ((TentativeNullSpaceSize == 0) || (TentativeNullSpace == 0))
ML_CHK_ERR(-1);
// ================================== //
// get parameters from the input list //
// ================================== //
// maximum number of relaxation sweeps
int MaxSweeps = List_.get("adaptive: max sweeps", 10);
// number of std::vector to be added to the tentative null space
int NumAdaptiveVectors = List_.get("adaptive: num vectors", 1);
if (verbose_) {
std::cout << PrintMsg_ << "*** Adaptive Smoother Aggregation setup ***" << std::endl;
std::cout << PrintMsg_ << " Maximum relaxation sweeps = " << MaxSweeps << std::endl;
std::cout << PrintMsg_ << " Additional vectors to compute = " << NumAdaptiveVectors << std::endl;
}
// ==================================================== //
// compute the preconditioner, set null space from user //
// (who will have to delete std::vector TentativeNullSpace) //
// ==================================================== //
double* NewNullSpace = 0;
double* OldNullSpace = TentativeNullSpace;
int OldNullSpaceSize = TentativeNullSpaceSize;
// need some work otherwise matvec() with Epetra_Vbr fails.
// Also, don't differentiate between range and domain here
// as ML will not work if range != domain
const Epetra_VbrMatrix* VbrA = NULL;
VbrA = dynamic_cast<const Epetra_VbrMatrix*>(RowMatrix_);
Epetra_Vector* LHS = 0;
Epetra_Vector* RHS = 0;
if (VbrA != 0) {
LHS = new Epetra_Vector(VbrA->DomainMap());
RHS = new Epetra_Vector(VbrA->DomainMap());
} else {
LHS = new Epetra_Vector(RowMatrix_->OperatorDomainMap());
RHS = new Epetra_Vector(RowMatrix_->OperatorDomainMap());
}
// destroy what we may already have
if (IsComputePreconditionerOK_ == true) {
DestroyPreconditioner();
}
// build the preconditioner for the first time
List_.set("null space: type", "pre-computed");
List_.set("null space: dimension", OldNullSpaceSize);
List_.set("null space: vectors", OldNullSpace);
ComputePreconditioner();
// ====================== //
// add one std::vector at time //
// ====================== //
for (int istep = 0 ; istep < NumAdaptiveVectors ; ++istep) {
if (verbose_) {
std::cout << PrintMsg_ << "\tAdaptation step " << istep << std::endl;
std::cout << PrintMsg_ << "\t---------------" << std::endl;
}
// ==================== //
// look for "bad" modes //
// ==================== //
// note: should an error occur, ML_CHK_ERR will return,
// and LHS and RHS will *not* be delete'd (--> memory leak).
// Anyway, this means that something wrong happened in the code
// and should be fixed by the user.
LHS->Random();
double Norm2;
for (int i = 0 ; i < MaxSweeps ; ++i) {
// RHS = (I - ML^{-1} A) LHS
ML_CHK_ERR(RowMatrix_->Multiply(false,*LHS,*RHS));
// FIXME: can do something slightly better here
ML_CHK_ERR(ApplyInverse(*RHS,*RHS));
ML_CHK_ERR(LHS->Update(-1.0,*RHS,1.0));
LHS->Norm2(&Norm2);
if (verbose_) {
std::cout << PrintMsg_ << "\titer " << i << ", ||x||_2 = ";
std::cout << Norm2 << std::endl;
}
}
// scaling vectors
double NormInf;
LHS->NormInf(&NormInf);
LHS->Scale(1.0 / NormInf);
// ========================================================= //
// copy tentative and computed null space into NewNullSpace, //
// which now becomes the standard null space //
// ========================================================= //
int NewNullSpaceSize = OldNullSpaceSize + 1;
NewNullSpace = new double[NumMyRows() * NewNullSpaceSize];
assert (NewNullSpace != 0);
int itmp = OldNullSpaceSize * NumMyRows();
for (int i = 0 ; i < itmp ; ++i) {
NewNullSpace[i] = OldNullSpace[i];
}
for (int j = 0 ; j < NumMyRows() ; ++j) {
NewNullSpace[itmp + j] = (*LHS)[j];
}
// =============== //
// visualize modes //
// =============== //
if (List_.get("adaptive: visualize", false)) {
double* x_coord = List_.get("viz: x-coordinates", (double*)0);
double* y_coord = List_.get("viz: y-coordinates", (double*)0);
double* z_coord = List_.get("viz: z-coordinates", (double*)0);
assert (x_coord != 0);
std::vector<double> plot_me(NumMyRows()/NumPDEEqns_);
ML_Aggregate_Viz_Stats info;
info.Amatrix = &(ml_->Amat[LevelID_[0]]);
info.x = x_coord;
info.y = y_coord;
info.z = z_coord;
info.Nlocal = NumMyRows() / NumPDEEqns_;
info.Naggregates = 1;
ML_Operator_AmalgamateAndDropWeak(&(ml_->Amat[LevelID_[0]]),
NumPDEEqns_, 0.0);
for (int ieqn = 0 ; ieqn < NumPDEEqns_ ; ++ieqn) {
for (int j = 0 ; j < NumMyRows() ; j+=NumPDEEqns_) {
plot_me[j / NumPDEEqns_] = (*LHS)[j + ieqn];
}
char FileName[80];
sprintf(FileName,"nullspace-mode%d-eq%d.xyz", istep, ieqn);
if (verbose_)
std::cout << PrintMsg_ << "writing file " << FileName << "..." << std::endl;
ML_Aggregate_VisualizeXYZ(info,FileName,
ml_->comm,&plot_me[0]);
}
ML_Operator_UnAmalgamateAndDropWeak(&(ml_->Amat[LevelID_[0]]),
NumPDEEqns_, 0.0);
}
// Destroy the old preconditioner
DestroyPreconditioner();
// ==================================================== //
// build the new preconditioner with the new null space //
// ==================================================== //
List_.set("null space: type", "pre-computed");
List_.set("null space: dimension", NewNullSpaceSize);
List_.set("null space: vectors", NewNullSpace);
ML_CHK_ERR(ComputePreconditioner());
if (istep && (istep != NumAdaptiveVectors))
delete OldNullSpace;
OldNullSpace = NewNullSpace;
OldNullSpaceSize = NewNullSpaceSize;
}
// keep trace of this pointer, it will be delete'd later
NullSpaceToFree_ = NewNullSpace;
delete LHS;
delete RHS;
return(0);
}
void
exampleRoutine (const Epetra_Comm& comm,
std::ostream& out)
{
using std::endl;
// Print out the Epetra software version.
if (comm.MyPID () == 0) {
out << Epetra_Version () << endl << endl;
}
// The type of global indices. You could just set this to int,
// but we want the example to work for Epetra64 as well.
#ifdef EPETRA_NO_32BIT_GLOBAL_INDICES
// Epetra was compiled only with 64-bit global index support, so use
// 64-bit global indices.
typedef long long global_ordinal_type;
#else
// Epetra was compiled with 32-bit global index support. If
// EPETRA_NO_64BIT_GLOBAL_INDICES is defined, it does not also
// support 64-bit indices.
typedef int global_ordinal_type;
#endif // EPETRA_NO_32BIT_GLOBAL_INDICES
//////////////////////////////////////////////////////////////////////
// Create some Epetra_Map objects
//////////////////////////////////////////////////////////////////////
//
// Epetra has local and global Maps. Local maps describe objects
// that are replicated over all participating MPI processes. Global
// maps describe distributed objects. You can do imports and
// exports between local and global maps; this is how you would turn
// locally replicated objects into distributed objects and vice
// versa.
//
// The total (global, i.e., over all MPI processes) number of
// entries in the Map. This has the same type as that of global
// indices, so it can represent very large values if Epetra was
// built with 64-bit global index support.
//
// For this example, we scale the global number of entries in the
// Map with the number of MPI processes. That way, you can run this
// example with any number of MPI processes and every process will
// still have a positive number of entries.
const global_ordinal_type numGlobalEntries = comm.NumProc () * 5;
// Tpetra can index the entries of a Map starting with 0 (C style),
// 1 (Fortran style), or any base you want. 1-based indexing is
// handy when interfacing with Fortran. We choose 0-based indexing
// here. This also has the same type as that of global indices.
const global_ordinal_type indexBase = 0;
// Construct a Map that puts the same number of equations on each
// (MPI) process. The Epetra_Comm is passed in by value, but that's
// OK, because Epetra_Comm has shallow copy semantics. (Its copy
// constructor and assignment operator do not call MPI_Comm_dup;
// they just pass along the MPI_Comm.)
Epetra_Map contigMap (numGlobalEntries, indexBase, comm);
// contigMap is contiguous by construction.
if (! contigMap.LinearMap ()) {
throw std::logic_error ("The supposedly contiguous Map isn't contiguous.");
}
// Let's create a second Map. It will have the same number of
// global entries per process, but will distribute them differently,
// in round-robin (1-D cyclic) fashion instead of contiguously.
// We'll use the version of the Map constructor that takes, on each
// MPI process, a list of the global indices in the Map belonging to
// that process. You can use this constructor to construct an
// overlapping (also called "not 1-to-1") Map, in which one or more
// entries are owned by multiple processes. We don't do that here;
// we make a nonoverlapping (also called "1-to-1") Map.
const int numGblIndsPerProc = 5;
global_ordinal_type* gblIndList = new global_ordinal_type [numGblIndsPerProc];
const int numProcs = comm.NumProc ();
const int myRank = comm.MyPID ();
for (int k = 0; k < numGblIndsPerProc; ++k) {
gblIndList[k] = myRank + k*numProcs;
}
Epetra_Map cyclicMap (numGlobalEntries, numGblIndsPerProc,
gblIndList, indexBase, comm);
// The above constructor makes a deep copy of the input index list,
// so it's safe to deallocate that list after this constructor
// completes.
if (gblIndList != NULL) {
delete [] gblIndList;
gblIndList = NULL;
}
// If there's more than one MPI process in the communicator,
// then cyclicMap is definitely NOT contiguous.
if (comm.NumProc () > 1 && cyclicMap.LinearMap ()) {
throw std::logic_error ("The cyclic Map claims to be contiguous.");
}
// contigMap and cyclicMap should always be compatible. However, if
// the communicator contains more than 1 process, then contigMap and
// cyclicMap are NOT the same.
// if (! contigMap.isCompatible (*cyclicMap)) {
// throw std::logic_error ("contigMap should be compatible with cyclicMap, "
// "but it's not.");
// }
if (comm.NumProc () > 1 && contigMap.SameAs (cyclicMap)) {
throw std::logic_error ("contigMap should not be the same as cyclicMap.");
}
//////////////////////////////////////////////////////////////////////
// We have maps now, so we can create vectors.
//////////////////////////////////////////////////////////////////////
// Create an Epetra_Vector with the contiguous Map we created above.
// This version of the constructor will fill the vector with zeros.
// The Vector constructor takes a Map by value, but that's OK,
// because Epetra_Map has shallow copy semantics. It uses reference
// counting internally to avoid copying data unnecessarily.
Epetra_Vector x (contigMap);
// The copy constructor performs a deep copy.
// x and y have the same Map.
Epetra_Vector y (x);
// Create a Vector with the 1-D cyclic Map. Calling the constructor
// with false for the second argument leaves the data uninitialized,
// so that you can fill it later without paying the cost of
// initially filling it with zeros.
Epetra_Vector z (cyclicMap, false);
// Set the entries of z to (pseudo)random numbers. Please don't
// consider this a good parallel pseudorandom number generator.
(void) z.Random ();
// Set the entries of x to all ones.
(void) x.PutScalar (1.0);
// Define some constants for use below.
const double alpha = 3.14159;
const double beta = 2.71828;
const double gamma = -10.0;
// x = beta*x + alpha*z
//
// This is a legal operation! Even though the Maps of x and z are
// not the same, their Maps are compatible. Whether it makes sense
// or not depends on your application.
(void) x.Update (alpha, z, beta);
(void) y.PutScalar (42.0); // Set all entries of y to 42.0
// y = gamma*y + alpha*x + beta*z
y.Update (alpha, x, beta, z, gamma);
// Compute the 2-norm of y.
//
// The norm may have a different type than scalar_type.
// For example, if scalar_type is complex, then the norm is real.
// The ScalarTraits "traits class" gives us the type of the norm.
double theNorm = 0.0;
(void) y.Norm2 (&theNorm);
// Print the norm of y on Proc 0.
out << "Norm of y: " << theNorm << endl;
}
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);
}
