PyTrilinos::ML::MultiLevelPreconditioner Class Reference

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List of all members.

Public Member Functions

def __init__
def Label
def PrintUnused
def GetList
def GetOutputList
def PrintList
def SetParameterList
def Apply
def ApplyInverse
def ComputePreconditioner
def ReComputePreconditioner
def ComputeAdaptivePreconditioner
def IsPreconditionerComputed
def SetOwnership
def SetUseTranspose
def NormInf
def UseTranspose
def HasNormInf
def Comm
def OperatorDomainMap
def OperatorRangeMap
def DestroyPreconditioner
def RowMatrix
def Map
def NumGlobalRows
def NumGlobalCols
def NumMyRows
def NumMyCols
def PrintStencil2D
def AnalyzeHierarchy
def AnalyzeSmoothers
def AnalyzeCoarse
def AnalyzeCycle
def TestSmoothers
def GetML
def SolvingMaxwell
def GetML_Aggregate
def Visualize
def VisualizeAggregates
def VisualizeSmoothers
def VisualizeCycle
def CreateLabel
def ReportTime
def SetParameterListAndNullSpace
def __init__
def Label
def PrintUnused
def GetList
def GetOutputList
def PrintList
def SetParameterList
def Apply
def ApplyInverse
def ComputePreconditioner
def ReComputePreconditioner
def ComputeAdaptivePreconditioner
def IsPreconditionerComputed
def SetOwnership
def SetUseTranspose
def NormInf
def UseTranspose
def HasNormInf
def Comm
def OperatorDomainMap
def OperatorRangeMap
def DestroyPreconditioner
def RowMatrix
def Map
def NumGlobalRows
def NumGlobalCols
def NumMyRows
def NumMyCols
def PrintStencil2D
def AnalyzeHierarchy
def AnalyzeSmoothers
def AnalyzeCoarse
def AnalyzeCycle
def TestSmoothers
def GetML
def SolvingMaxwell
def GetML_Aggregate
def Visualize
def VisualizeAggregates
def VisualizeSmoothers
def VisualizeCycle
def CreateLabel
def ReportTime
def SetParameterListAndNullSpace

Public Attributes

 this


Detailed Description

ML black-box preconditioner for Epetra_RowMatrix derived classes.

C++ includes: ml_MultiLevelPreconditioner.h 

Member Function Documentation

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,
  args 
)

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,
  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::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,
  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::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,
  args 
)

GetML(self, int WhichML = -1) -> ML

def PyTrilinos::ML::MultiLevelPreconditioner::GetML_Aggregate (   self,
  args 
)

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,
  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::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,
  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::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


The documentation for this class was generated from the following files:

Generated on Thu Dec 17 11:00:22 2009 for PyTrilinos by  doxygen 1.5.9