ML black-box preconditioner for Epetra_RowMatrix derived classes. C++ includes: ml_MultiLevelPreconditioner.h
def PyTrilinos::ML::MultiLevelPreconditioner::__init__ | ( | self, | ||
args | ||||
) |
__init__(self, RowMatrix RowMatrix, bool ComputePrec = True) -> MultiLevelPreconditioner __init__(self, RowMatrix RowMatrix, ParameterList List, bool ComputePrec = True) -> MultiLevelPreconditioner __init__(self, ML_Operator Operator, ParameterList List, bool ComputePrec = True) -> MultiLevelPreconditioner __init__(self, RowMatrix EdgeMatrix, RowMatrix GradMatrix, RowMatrix NodeMatrix, ParameterList List, bool ComputePrec = True, bool UseNodeMatrixForSmoother = False) -> MultiLevelPreconditioner __init__(self, RowMatrix CurlCurlMatrix, RowMatrix MassMatrix, RowMatrix TMatrix, RowMatrix NodeMatrix, ParameterList List, bool ComputePrec = True) -> MultiLevelPreconditioner __init__(self, Epetra_MsrMatrix EdgeMatrix, ML_Operator GradMatrix, AZ_MATRIX NodeMatrix, int proc_config, ParameterList List, bool ComputePrec = True) -> MultiLevelPreconditioner ML black-box preconditioner for Epetra_RowMatrix derived classes. C++ includes: ml_MultiLevelPreconditioner.h
Reimplemented from PyTrilinos::Epetra::Operator.
def PyTrilinos::ML::MultiLevelPreconditioner::__init__ | ( | self, | ||
args | ||||
) |
__init__(self, RowMatrix RowMatrix, bool ComputePrec = True) -> MultiLevelPreconditioner __init__(self, RowMatrix RowMatrix, ParameterList List, bool ComputePrec = True) -> MultiLevelPreconditioner __init__(self, ML_Operator Operator, ParameterList List, bool ComputePrec = True) -> MultiLevelPreconditioner __init__(self, RowMatrix EdgeMatrix, RowMatrix GradMatrix, RowMatrix NodeMatrix, ParameterList List, bool ComputePrec = True, bool UseNodeMatrixForSmoother = False) -> MultiLevelPreconditioner __init__(self, RowMatrix CurlCurlMatrix, RowMatrix MassMatrix, RowMatrix TMatrix, RowMatrix NodeMatrix, ParameterList List, bool ComputePrec = True) -> MultiLevelPreconditioner __init__(self, Epetra_MsrMatrix EdgeMatrix, ML_Operator GradMatrix, AZ_MATRIX NodeMatrix, int proc_config, ParameterList List, bool ComputePrec = True) -> MultiLevelPreconditioner ML black-box preconditioner for Epetra_RowMatrix derived classes. C++ includes: ml_MultiLevelPreconditioner.h
Reimplemented from PyTrilinos::Epetra::Operator.
def PyTrilinos::ML::MultiLevelPreconditioner::AnalyzeCoarse | ( | self, | ||
args | ||||
) |
AnalyzeCoarse(self) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::AnalyzeCoarse | ( | self, | ||
args | ||||
) |
AnalyzeCoarse(self) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::AnalyzeCycle | ( | self, | ||
args | ||||
) |
AnalyzeCycle(self, int NumCycles = 1) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::AnalyzeCycle | ( | self, | ||
args | ||||
) |
AnalyzeCycle(self, int NumCycles = 1) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::AnalyzeHierarchy | ( | self, | ||
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) |
AnalyzeHierarchy(self, bool AnalyzeMatrices, int PreCycles, int PostCycles, int MLCycles) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::AnalyzeHierarchy | ( | self, | ||
args | ||||
) |
AnalyzeHierarchy(self, bool AnalyzeMatrices, int PreCycles, int PostCycles, int MLCycles) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::AnalyzeSmoothers | ( | self, | ||
args | ||||
) |
AnalyzeSmoothers(self, int NumPreCycles = 1, int NumPostCycles = 1) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::AnalyzeSmoothers | ( | self, | ||
args | ||||
) |
AnalyzeSmoothers(self, int NumPreCycles = 1, int NumPostCycles = 1) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::Apply | ( | self, | ||
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) |
Apply(self, MultiVector x, MultiVector y) -> int In C++, the Apply() method is pure virtual, thus intended to be overridden by derived classes. In python, cross-language polymorphism is supported, and you are expected to derive classes from this base class and redefine the Apply() method. C++ code (e.g., AztecOO solvers) can call back to your Apply() method as needed. You must support two arguments, labeled here MultiVector x and MultiVector y. These will be converted from Epetra_MultiVector C++ objects to numpy-hybrid Epetra.MultiVector objects before they are passed to you. Thus, it is legal to use slice indexing and other numpy features to compute y from x. If application of your operator is successful, return 0; else return some non-zero error code. It is strongly suggested that you prevent Apply() from raising any exceptions. Accidental errors can be prevented by wrapping your code in a try block: try: # Your code goes here... except Exception, e: print 'A python exception was raised by method Apply:' print e return -1 By returning a -1, you inform the calling routine that Apply() was unsuccessful. virtual int Epetra_Operator::Apply(const Epetra_MultiVector &X, Epetra_MultiVector &Y) const =0 Returns the result of a Epetra_Operator applied to a Epetra_MultiVector X in Y. Parameters: ----------- In: X - A Epetra_MultiVector of dimension NumVectors to multiply with matrix. Out: Y -A Epetra_MultiVector of dimension NumVectors containing result. Integer error code, set to 0 if successful.
Reimplemented from PyTrilinos::Epetra::Operator.
def PyTrilinos::ML::MultiLevelPreconditioner::Apply | ( | self, | ||
args | ||||
) |
Apply(self, MultiVector x, MultiVector y) -> int In C++, the Apply() method is pure virtual, thus intended to be overridden by derived classes. In python, cross-language polymorphism is supported, and you are expected to derive classes from this base class and redefine the Apply() method. C++ code (e.g., AztecOO solvers) can call back to your Apply() method as needed. You must support two arguments, labeled here MultiVector x and MultiVector y. These will be converted from Epetra_MultiVector C++ objects to numpy-hybrid Epetra.MultiVector objects before they are passed to you. Thus, it is legal to use slice indexing and other numpy features to compute y from x. If application of your operator is successful, return 0; else return some non-zero error code. It is strongly suggested that you prevent Apply() from raising any exceptions. Accidental errors can be prevented by wrapping your code in a try block: try: # Your code goes here... except Exception, e: print 'A python exception was raised by method Apply:' print e return -1 By returning a -1, you inform the calling routine that Apply() was unsuccessful. virtual int Epetra_Operator::Apply(const Epetra_MultiVector &X, Epetra_MultiVector &Y) const =0 Returns the result of a Epetra_Operator applied to a Epetra_MultiVector X in Y. Parameters: ----------- In: X - A Epetra_MultiVector of dimension NumVectors to multiply with matrix. Out: Y -A Epetra_MultiVector of dimension NumVectors containing result. Integer error code, set to 0 if successful.
Reimplemented from PyTrilinos::Epetra::Operator.
def PyTrilinos::ML::MultiLevelPreconditioner::ApplyInverse | ( | self, | ||
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) |
ApplyInverse(self, MultiVector x, MultiVector y) -> int In C++, the ApplyInverse() method is pure virtual, thus intended to be overridden by derived classes. In python, cross-language polymorphism is supported, and you are expected to derive classes from this base class and redefine the ApplyInverse() method. C++ code (e.g., AztecOO solvers) can call back to your ApplyInverse() method as needed. You must support two arguments, labeled here MultiVector x and MultiVector y. These will be converted from Epetra_MultiVector C++ objects to numpy-hybrid Epetra.MultiVector objects before they are passed to you. Thus, it is legal to use slice indexing and other numpy features to compute y from x. If application of your operator is successful, return 0; else return some non-zero error code. It is strongly suggested that you prevent ApplyInverse() from raising any exceptions. Accidental errors can be prevented by wrapping your code in a try block: try: # Your code goes here... except Exception, e: print 'A python exception was raised by method ApplyInverse:' print e return -1 By returning a -1, you inform the calling routine that ApplyInverse() was unsuccessful. virtual int Epetra_Operator::ApplyInverse(const Epetra_MultiVector &X, Epetra_MultiVector &Y) const =0 Returns the result of a Epetra_Operator inverse applied to an Epetra_MultiVector X in Y. Parameters: ----------- In: X - A Epetra_MultiVector of dimension NumVectors to solve for. Out: Y -A Epetra_MultiVector of dimension NumVectors containing result. Integer error code, set to 0 if successful. WARNING: In order to work with AztecOO, any implementation of this method must support the case where X and Y are the same object.
Reimplemented from PyTrilinos::Epetra::Operator.
def PyTrilinos::ML::MultiLevelPreconditioner::ApplyInverse | ( | self, | ||
args | ||||
) |
ApplyInverse(self, MultiVector x, MultiVector y) -> int In C++, the ApplyInverse() method is pure virtual, thus intended to be overridden by derived classes. In python, cross-language polymorphism is supported, and you are expected to derive classes from this base class and redefine the ApplyInverse() method. C++ code (e.g., AztecOO solvers) can call back to your ApplyInverse() method as needed. You must support two arguments, labeled here MultiVector x and MultiVector y. These will be converted from Epetra_MultiVector C++ objects to numpy-hybrid Epetra.MultiVector objects before they are passed to you. Thus, it is legal to use slice indexing and other numpy features to compute y from x. If application of your operator is successful, return 0; else return some non-zero error code. It is strongly suggested that you prevent ApplyInverse() from raising any exceptions. Accidental errors can be prevented by wrapping your code in a try block: try: # Your code goes here... except Exception, e: print 'A python exception was raised by method ApplyInverse:' print e return -1 By returning a -1, you inform the calling routine that ApplyInverse() was unsuccessful. virtual int Epetra_Operator::ApplyInverse(const Epetra_MultiVector &X, Epetra_MultiVector &Y) const =0 Returns the result of a Epetra_Operator inverse applied to an Epetra_MultiVector X in Y. Parameters: ----------- In: X - A Epetra_MultiVector of dimension NumVectors to solve for. Out: Y -A Epetra_MultiVector of dimension NumVectors containing result. Integer error code, set to 0 if successful. WARNING: In order to work with AztecOO, any implementation of this method must support the case where X and Y are the same object.
Reimplemented from PyTrilinos::Epetra::Operator.
def PyTrilinos::ML::MultiLevelPreconditioner::Comm | ( | self, | ||
args | ||||
) |
Comm(self) -> Comm virtual const Epetra_Comm& Epetra_Operator::Comm() const =0 Returns a pointer to the Epetra_Comm communicator associated with this operator.
Reimplemented from PyTrilinos::Epetra::Operator.
def PyTrilinos::ML::MultiLevelPreconditioner::Comm | ( | self, | ||
args | ||||
) |
Comm(self) -> Comm virtual const Epetra_Comm& Epetra_Operator::Comm() const =0 Returns a pointer to the Epetra_Comm communicator associated with this operator.
Reimplemented from PyTrilinos::Epetra::Operator.
def PyTrilinos::ML::MultiLevelPreconditioner::ComputeAdaptivePreconditioner | ( | self, | ||
args | ||||
) |
ComputeAdaptivePreconditioner(self, int TentativeNullSpaceSize, double TentativeNullSpace) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::ComputeAdaptivePreconditioner | ( | self, | ||
args | ||||
) |
ComputeAdaptivePreconditioner(self, int TentativeNullSpaceSize, double TentativeNullSpace) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::ComputePreconditioner | ( | self, | ||
args | ||||
) |
ComputePreconditioner(self, bool CheckFiltering = False) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::ComputePreconditioner | ( | self, | ||
args | ||||
) |
ComputePreconditioner(self, bool CheckFiltering = False) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::CreateLabel | ( | self, | ||
args | ||||
) |
CreateLabel(self) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::CreateLabel | ( | self, | ||
args | ||||
) |
CreateLabel(self) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::DestroyPreconditioner | ( | self, | ||
args | ||||
) |
DestroyPreconditioner(self) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::DestroyPreconditioner | ( | self, | ||
args | ||||
) |
DestroyPreconditioner(self) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::GetList | ( | self, | ||
args | ||||
) |
GetList(self) -> ParameterList
def PyTrilinos::ML::MultiLevelPreconditioner::GetList | ( | self, | ||
args | ||||
) |
GetList(self) -> ParameterList
def PyTrilinos::ML::MultiLevelPreconditioner::GetML | ( | self, | ||
args | ||||
) |
GetML(self, int WhichML = -1) -> ML
def PyTrilinos::ML::MultiLevelPreconditioner::GetML | ( | self, | ||
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) |
GetML(self, int WhichML = -1) -> ML
def PyTrilinos::ML::MultiLevelPreconditioner::GetML_Aggregate | ( | self, | ||
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) |
GetML_Aggregate(self) -> ML_Aggregate
def PyTrilinos::ML::MultiLevelPreconditioner::GetML_Aggregate | ( | self, | ||
args | ||||
) |
GetML_Aggregate(self) -> ML_Aggregate
def PyTrilinos::ML::MultiLevelPreconditioner::GetOutputList | ( | self, | ||
args | ||||
) |
GetOutputList(self) -> ParameterList
def PyTrilinos::ML::MultiLevelPreconditioner::GetOutputList | ( | self, | ||
args | ||||
) |
GetOutputList(self) -> ParameterList
def PyTrilinos::ML::MultiLevelPreconditioner::HasNormInf | ( | self, | ||
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) |
HasNormInf(self) -> bool virtual bool Epetra_Operator::HasNormInf() const =0 Returns true if the this object can provide an approximate Inf-norm, false otherwise.
Reimplemented from PyTrilinos::Epetra::Operator.
def PyTrilinos::ML::MultiLevelPreconditioner::HasNormInf | ( | self, | ||
args | ||||
) |
HasNormInf(self) -> bool virtual bool Epetra_Operator::HasNormInf() const =0 Returns true if the this object can provide an approximate Inf-norm, false otherwise.
Reimplemented from PyTrilinos::Epetra::Operator.
def PyTrilinos::ML::MultiLevelPreconditioner::IsPreconditionerComputed | ( | self, | ||
args | ||||
) |
IsPreconditionerComputed(self) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::IsPreconditionerComputed | ( | self, | ||
args | ||||
) |
IsPreconditionerComputed(self) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::Label | ( | self, | ||
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) |
Label(self) -> char virtual const char* Epetra_Operator::Label() const =0 Returns a character string describing the operator.
Reimplemented from PyTrilinos::Epetra::Operator.
def PyTrilinos::ML::MultiLevelPreconditioner::Label | ( | self, | ||
args | ||||
) |
Label(self) -> char virtual const char* Epetra_Operator::Label() const =0 Returns a character string describing the operator.
Reimplemented from PyTrilinos::Epetra::Operator.
def PyTrilinos::ML::MultiLevelPreconditioner::Map | ( | self, | ||
args | ||||
) |
Map(self) -> BlockMap
def PyTrilinos::ML::MultiLevelPreconditioner::Map | ( | self, | ||
args | ||||
) |
Map(self) -> BlockMap
def PyTrilinos::ML::MultiLevelPreconditioner::NormInf | ( | self, | ||
args | ||||
) |
NormInf(self) -> double virtual double Epetra_Operator::NormInf() const =0 Returns the infinity norm of the global matrix.
Reimplemented from PyTrilinos::Epetra::Operator.
def PyTrilinos::ML::MultiLevelPreconditioner::NormInf | ( | self, | ||
args | ||||
) |
NormInf(self) -> double virtual double Epetra_Operator::NormInf() const =0 Returns the infinity norm of the global matrix.
Reimplemented from PyTrilinos::Epetra::Operator.
def PyTrilinos::ML::MultiLevelPreconditioner::NumGlobalCols | ( | self, | ||
args | ||||
) |
NumGlobalCols(self) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::NumGlobalCols | ( | self, | ||
args | ||||
) |
NumGlobalCols(self) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::NumGlobalRows | ( | self, | ||
args | ||||
) |
NumGlobalRows(self) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::NumGlobalRows | ( | self, | ||
args | ||||
) |
NumGlobalRows(self) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::NumMyCols | ( | self, | ||
args | ||||
) |
NumMyCols(self) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::NumMyCols | ( | self, | ||
args | ||||
) |
NumMyCols(self) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::NumMyRows | ( | self, | ||
args | ||||
) |
NumMyRows(self) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::NumMyRows | ( | self, | ||
args | ||||
) |
NumMyRows(self) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::OperatorDomainMap | ( | self, | ||
args | ||||
) |
OperatorDomainMap(self) -> Map virtual const Epetra_Map& Epetra_Operator::OperatorDomainMap() const =0 Returns the Epetra_Map object associated with the domain of this operator.
Reimplemented from PyTrilinos::Epetra::Operator.
def PyTrilinos::ML::MultiLevelPreconditioner::OperatorDomainMap | ( | self, | ||
args | ||||
) |
OperatorDomainMap(self) -> Map virtual const Epetra_Map& Epetra_Operator::OperatorDomainMap() const =0 Returns the Epetra_Map object associated with the domain of this operator.
Reimplemented from PyTrilinos::Epetra::Operator.
def PyTrilinos::ML::MultiLevelPreconditioner::OperatorRangeMap | ( | self, | ||
args | ||||
) |
OperatorRangeMap(self) -> Map virtual const Epetra_Map& Epetra_Operator::OperatorRangeMap() const =0 Returns the Epetra_Map object associated with the range of this operator.
Reimplemented from PyTrilinos::Epetra::Operator.
def PyTrilinos::ML::MultiLevelPreconditioner::OperatorRangeMap | ( | self, | ||
args | ||||
) |
OperatorRangeMap(self) -> Map virtual const Epetra_Map& Epetra_Operator::OperatorRangeMap() const =0 Returns the Epetra_Map object associated with the range of this operator.
Reimplemented from PyTrilinos::Epetra::Operator.
def PyTrilinos::ML::MultiLevelPreconditioner::PrintList | ( | self, | ||
args | ||||
) |
PrintList(self)
def PyTrilinos::ML::MultiLevelPreconditioner::PrintList | ( | self, | ||
args | ||||
) |
PrintList(self)
def PyTrilinos::ML::MultiLevelPreconditioner::PrintStencil2D | ( | self, | ||
args | ||||
) |
PrintStencil2D(self, int nx, int ny, int NodeID = -1, int EquationID = 0) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::PrintStencil2D | ( | self, | ||
args | ||||
) |
PrintStencil2D(self, int nx, int ny, int NodeID = -1, int EquationID = 0) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::PrintUnused | ( | self, | ||
args | ||||
) |
PrintUnused(self) PrintUnused(self, std::ostream os) PrintUnused(self, int MyPID)
def PyTrilinos::ML::MultiLevelPreconditioner::PrintUnused | ( | self, | ||
args | ||||
) |
PrintUnused(self) PrintUnused(self, std::ostream os) PrintUnused(self, int MyPID)
def PyTrilinos::ML::MultiLevelPreconditioner::ReComputePreconditioner | ( | self, | ||
args | ||||
) |
ReComputePreconditioner(self) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::ReComputePreconditioner | ( | self, | ||
args | ||||
) |
ReComputePreconditioner(self) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::ReportTime | ( | self, | ||
args | ||||
) |
ReportTime(self)
def PyTrilinos::ML::MultiLevelPreconditioner::ReportTime | ( | self, | ||
args | ||||
) |
ReportTime(self)
def PyTrilinos::ML::MultiLevelPreconditioner::RowMatrix | ( | self, | ||
args | ||||
) |
RowMatrix(self) -> RowMatrix
def PyTrilinos::ML::MultiLevelPreconditioner::RowMatrix | ( | self, | ||
args | ||||
) |
RowMatrix(self) -> RowMatrix
def PyTrilinos::ML::MultiLevelPreconditioner::SetOwnership | ( | self, | ||
args | ||||
) |
SetOwnership(self, bool ownership) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::SetOwnership | ( | self, | ||
args | ||||
) |
SetOwnership(self, bool ownership) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::SetParameterList | ( | self, | ||
args | ||||
) |
SetParameterList(self, ParameterList List) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::SetParameterList | ( | self, | ||
args | ||||
) |
SetParameterList(self, ParameterList List) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::SetParameterListAndNullSpace | ( | self, | ||
args | ||||
) |
SetParameterListAndNullSpace(self, PyObject obj, Epetra_MultiVector NullSpace) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::SetParameterListAndNullSpace | ( | self, | ||
args | ||||
) |
SetParameterListAndNullSpace(self, PyObject obj, Epetra_MultiVector NullSpace) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::SetUseTranspose | ( | self, | ||
args | ||||
) |
SetUseTranspose(self, bool UseTranspose) -> int virtual int Epetra_Operator::SetUseTranspose(bool UseTranspose)=0 If set true, transpose of this operator will be applied. This flag allows the transpose of the given operator to be used implicitly. Setting this flag affects only the Apply() and ApplyInverse() methods. If the implementation of this interface does not support transpose use, this method should return a value of -1. Parameters: ----------- In: UseTranspose -If true, multiply by the transpose of operator, otherwise just use operator. Integer error code, set to 0 if successful. Set to -1 if this implementation does not support transpose.
Reimplemented from PyTrilinos::Epetra::Operator.
def PyTrilinos::ML::MultiLevelPreconditioner::SetUseTranspose | ( | self, | ||
args | ||||
) |
SetUseTranspose(self, bool UseTranspose) -> int virtual int Epetra_Operator::SetUseTranspose(bool UseTranspose)=0 If set true, transpose of this operator will be applied. This flag allows the transpose of the given operator to be used implicitly. Setting this flag affects only the Apply() and ApplyInverse() methods. If the implementation of this interface does not support transpose use, this method should return a value of -1. Parameters: ----------- In: UseTranspose -If true, multiply by the transpose of operator, otherwise just use operator. Integer error code, set to 0 if successful. Set to -1 if this implementation does not support transpose.
Reimplemented from PyTrilinos::Epetra::Operator.
def PyTrilinos::ML::MultiLevelPreconditioner::SolvingMaxwell | ( | self, | ||
args | ||||
) |
SolvingMaxwell(self) -> bool
def PyTrilinos::ML::MultiLevelPreconditioner::SolvingMaxwell | ( | self, | ||
args | ||||
) |
SolvingMaxwell(self) -> bool
def PyTrilinos::ML::MultiLevelPreconditioner::TestSmoothers | ( | self, | ||
args | ||||
) |
TestSmoothers(self, ParameterList InputList, bool IsSymmetric = False) -> int TestSmoothers(self, bool IsSymmetric = False) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::TestSmoothers | ( | self, | ||
args | ||||
) |
TestSmoothers(self, ParameterList InputList, bool IsSymmetric = False) -> int TestSmoothers(self, bool IsSymmetric = False) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::UseTranspose | ( | self, | ||
args | ||||
) |
UseTranspose(self) -> bool virtual bool Epetra_Operator::UseTranspose() const =0 Returns the current UseTranspose setting.
Reimplemented from PyTrilinos::Epetra::Operator.
def PyTrilinos::ML::MultiLevelPreconditioner::UseTranspose | ( | self, | ||
args | ||||
) |
UseTranspose(self) -> bool virtual bool Epetra_Operator::UseTranspose() const =0 Returns the current UseTranspose setting.
Reimplemented from PyTrilinos::Epetra::Operator.
def PyTrilinos::ML::MultiLevelPreconditioner::Visualize | ( | self, | ||
args | ||||
) |
Visualize(self, bool VizAggre, bool VizPreSmoother, bool VizPostSmoother, bool VizCycle, int NumApplPreSmoother, int NumApplPostSmoother, int NumCycleSmoother) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::Visualize | ( | self, | ||
args | ||||
) |
Visualize(self, bool VizAggre, bool VizPreSmoother, bool VizPostSmoother, bool VizCycle, int NumApplPreSmoother, int NumApplPostSmoother, int NumCycleSmoother) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::VisualizeAggregates | ( | self, | ||
args | ||||
) |
VisualizeAggregates(self) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::VisualizeAggregates | ( | self, | ||
args | ||||
) |
VisualizeAggregates(self) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::VisualizeCycle | ( | self, | ||
args | ||||
) |
VisualizeCycle(self, int NumCycles = 1) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::VisualizeCycle | ( | self, | ||
args | ||||
) |
VisualizeCycle(self, int NumCycles = 1) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::VisualizeSmoothers | ( | self, | ||
args | ||||
) |
VisualizeSmoothers(self, int NumPrecCycles = 1, int NumPostCycles = 1) -> int
def PyTrilinos::ML::MultiLevelPreconditioner::VisualizeSmoothers | ( | self, | ||
args | ||||
) |
VisualizeSmoothers(self, int NumPrecCycles = 1, int NumPostCycles = 1) -> int