// ======================================================================
Operator GetTranspose(const Operator& A, const bool byrow = true)
{
ML_Operator* ML_transp;
ML_transp = ML_Operator_Create(GetML_Comm());
if (byrow)
ML_Operator_Transpose_byrow(A.GetML_Operator(),ML_transp);
else
ML_Operator_Transpose(A.GetML_Operator(),ML_transp);
Operator transp(A.GetRangeSpace(),A.GetDomainSpace(), ML_transp,true);
return(transp);
}
// ======================================================================
Operator GetJacobiIterationOperator(const Operator& Amat, double Damping)
{
struct ML_AGG_Matrix_Context* widget = new struct ML_AGG_Matrix_Context;
widget->near_bdry = 0;
widget->aggr_info = 0;
widget->drop_tol = 0.0;
widget->Amat = Amat.GetML_Operator();
widget->omega = Damping;
ML_Operator* tmp_ML = ML_Operator_Create(GetML_Comm());
ML_Operator_Set_ApplyFuncData(tmp_ML, widget->Amat->invec_leng,
widget->Amat->outvec_leng, widget,
widget->Amat->matvec->Nrows, NULL, 0);
tmp_ML->data_destroy = widget_destroy;
ML_Operator_Set_Getrow(tmp_ML, widget->Amat->getrow->Nrows,
ML_AGG_JacobiSmoother_Getrows);
// Creates a new copy of pre_comm, so that the old pre_comm
// can be destroyed without worry
ML_CommInfoOP_Clone(&(tmp_ML->getrow->pre_comm),
widget->Amat->getrow->pre_comm);
Operator tmp(Amat.GetDomainSpace(), Amat.GetRangeSpace(), tmp_ML, true,
Amat.GetRCPOperatorBox());
return(tmp);
}
// ======================================================================
double MaxEigPowerMethod(const Operator& Op, const bool DiagonalScaling)
{
ML_Krylov *kdata;
double MaxEigen;
kdata = ML_Krylov_Create(GetML_Comm());
if (DiagonalScaling == false)
kdata->ML_dont_scale_by_diag = ML_TRUE;
else
kdata->ML_dont_scale_by_diag = ML_FALSE;
ML_Krylov_Set_PrintFreq(kdata, 0);
ML_Krylov_Set_ComputeNonSymEigenvalues(kdata);
ML_Krylov_Set_Amatrix(kdata, Op.GetML_Operator());
ML_Krylov_Solve(kdata, Op.GetML_Operator()->outvec_leng, NULL, NULL);
MaxEigen = ML_Krylov_Get_MaxEigenvalue(kdata);
ML_Krylov_Destroy(&kdata);
return(MaxEigen);
}
Operator GetRAP(const Operator& R, const Operator& A,
const Operator& P)
{
ML_Operator* Rmat = R.GetML_Operator();
ML_Operator* Amat = A.GetML_Operator();
ML_Operator* Pmat = P.GetML_Operator();
ML_Operator* result = 0;
result = ML_Operator_Create (Rmat->comm);
/* The fixing of coarse matrix only works if it is in MSR format */
int myMatrixType = ML_MSR_MATRIX;
ML_rap(Rmat, Amat, Pmat, result, myMatrixType);
result->num_PDEs = Pmat->num_PDEs;
#ifdef MB_MODIF_QR
ML_fixCoarseMtx(result, myMatrixType);
#endif/*MB_MODIF_QR*/
Operator op(P.GetDomainSpace(),P.GetDomainSpace(), result);
return(op);
}
// ======================================================================
Operator GetScaledOperator(const Operator& A, const double alpha)
{
ML_Operator* ScaledA = 0;
ScaledA = ML_Operator_ExplicitlyScale(A.GetML_Operator(),
(double)alpha);
if (ScaledA == 0)
ML_THROW("ML_Operator_ExplicitlyScale returned 0", -1);
Operator res;
res.Reshape(A.GetDomainSpace(), A.GetRangeSpace(), ScaledA,
true, A.GetRCPOperatorBox());
return(res);
}
// ======================================================================
double MaxEigAnasazi(const Operator& Op, const bool DiagonalScaling)
{
double MaxEigen = 0.0;
#if defined(HAVE_ML_EPETRA) && defined(HAVE_ML_ANASAxI) && defined(HAVE_ML_TEUCHOS)
bool DiagScal;
if (DiagonalScaling)
DiagScal = ML_TRUE;
else
DiagScal = ML_FALSE;
ML_Anasazi_Get_SpectralNorm_Anasazi(Op.GetML_Operator(), 0, 10, 1e-5,
ML_FALSE, DiagScal, &MaxEigen);
#else
//ML_THROW("Configure w/ --enable-epetra --enable-anasazi --enable-teuchos", -1);
ML_THROW("Anasazi is no longer supported", -1);
#endif
return(MaxEigen);
}
// ======================================================================
MultiVector GetDiagonal(const Operator& A, const int offset)
{
// FIXME
if (A.GetDomainSpace() != A.GetRangeSpace())
ML_THROW("Currently only square matrices are supported", -1);
MultiVector D(A.GetDomainSpace());
D = 0.0;
ML_Operator* matrix = A.GetML_Operator();
if (matrix->getrow == NULL)
ML_THROW("getrow() not set!", -1);
int row_length;
int allocated = 128;
int* bindx = (int *) ML_allocate(allocated*sizeof(int ));
double* val = (double *) ML_allocate(allocated*sizeof(double));
for (int i = 0 ; i < matrix->getrow->Nrows; i++) {
int GlobalRow = A.GetGRID(i);
ML_get_matrix_row(matrix, 1, &i, &allocated, &bindx, &val,
&row_length, 0);
for (int j = 0; j < row_length; j++) {
D(i) = 0.0;
if (A.GetGCID(bindx[j]) == GlobalRow + offset) {
D(i) = val[j];
break;
}
}
}
ML_free(val);
ML_free(bindx);
return (D);
}
// ======================================================================
double MaxEigAnorm(const Operator& Op, const bool DiagonalScaling)
{
return(ML_Operator_MaxNorm(Op.GetML_Operator(), DiagonalScaling));
}
// ======================================================================
void GetPtent(const Operator& A, Teuchos::ParameterList& List,
const MultiVector& ThisNS,
Operator& Ptent, MultiVector& NextNS)
{
std::string CoarsenType = List.get("aggregation: type", "Uncoupled");
/* old version
int NodesPerAggr = List.get("aggregation: per aggregate", 64);
*/
double Threshold = List.get("aggregation: threshold", 0.0);
int NumPDEEquations = List.get("PDE equations", 1);
ML_Aggregate* agg_object;
ML_Aggregate_Create(&agg_object);
ML_Aggregate_Set_MaxLevels(agg_object,2);
ML_Aggregate_Set_StartLevel(agg_object,0);
ML_Aggregate_Set_Threshold(agg_object,Threshold);
//agg_object->curr_threshold = 0.0;
ML_Operator* ML_Ptent = 0;
ML_Ptent = ML_Operator_Create(GetML_Comm());
if (ThisNS.GetNumVectors() == 0)
ML_THROW("zero-dimension null space", -1);
int size = ThisNS.GetMyLength();
double* null_vect = 0;
ML_memory_alloc((void **)(&null_vect), sizeof(double) * size * ThisNS.GetNumVectors(), "ns");
int incr = 1;
for (int v = 0 ; v < ThisNS.GetNumVectors() ; ++v)
DCOPY_F77(&size, (double*)ThisNS.GetValues(v), &incr,
null_vect + v * ThisNS.GetMyLength(), &incr);
ML_Aggregate_Set_NullSpace(agg_object, NumPDEEquations,
ThisNS.GetNumVectors(), null_vect,
ThisNS.GetMyLength());
if (CoarsenType == "Uncoupled")
agg_object->coarsen_scheme = ML_AGGR_UNCOUPLED;
else if (CoarsenType == "Uncoupled-MIS")
agg_object->coarsen_scheme = ML_AGGR_HYBRIDUM;
else if (CoarsenType == "MIS") {
/* needed for MIS, otherwise it sets the number of equations to
* the null space dimension */
agg_object->max_levels = -7;
agg_object->coarsen_scheme = ML_AGGR_MIS;
}
else if (CoarsenType == "METIS")
agg_object->coarsen_scheme = ML_AGGR_METIS;
else {
ML_THROW("Requested aggregation scheme (" + CoarsenType +
") not recognized", -1);
}
int NextSize = ML_Aggregate_Coarsen(agg_object, A.GetML_Operator(),
&ML_Ptent, GetML_Comm());
/* This is the old version
int NextSize;
if (CoarsenType == "Uncoupled") {
NextSize = ML_Aggregate_CoarsenUncoupled(agg_object, A.GetML_Operator(),
}
else if (CoarsenType == "MIS") {
NextSize = ML_Aggregate_CoarsenMIS(agg_object, A.GetML_Operator(),
&ML_Ptent, GetML_Comm());
}
else if (CoarsenType == "METIS") {
ML ml_object;
ml_object.ML_num_levels = 1; // crap for line below
ML_Aggregate_Set_NodesPerAggr(&ml_object,agg_object,0,NodesPerAggr);
NextSize = ML_Aggregate_CoarsenMETIS(agg_object, A.GetML_Operator(),
&ML_Ptent, GetML_Comm());
}
else {
ML_THROW("Requested aggregation scheme (" + CoarsenType +
") not recognized", -1);
}
*/
ML_Operator_ChangeToSinglePrecision(ML_Ptent);
int NumMyElements = NextSize;
Space CoarseSpace(-1,NumMyElements);
Ptent.Reshape(CoarseSpace,A.GetRangeSpace(),ML_Ptent,true);
assert (NextSize * ThisNS.GetNumVectors() != 0);
NextNS.Reshape(CoarseSpace, ThisNS.GetNumVectors());
size = NextNS.GetMyLength();
for (int v = 0 ; v < NextNS.GetNumVectors() ; ++v)
DCOPY_F77(&size, agg_object->nullspace_vect + v * size, &incr,
NextNS.GetValues(v), &incr);
ML_Aggregate_Destroy(&agg_object);
ML_memory_free((void**)(&null_vect));
}
