

Pure virtual class interface for allowing different linear solvers to be used by the NOX::Epetra::Group. C++ includes: NOX_Epetra_LinearSystem.H
| def PyTrilinos::NOX::Epetra::LinearSystem::__init__ | ( | self, | ||
| args | ||||
| ) |
__init__(self) -> LinearSystem NOX::Epetra::LinearSystem::LinearSystem() Constructor.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::__init__ | ( | self, | ||
| args | ||||
| ) |
__init__(self) -> LinearSystem NOX::Epetra::LinearSystem::LinearSystem() Constructor.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::applyJacobian | ( | self, | ||
| args | ||||
| ) |
applyJacobian(self, Vector input, Vector nox_result) -> bool virtual bool NOX::Epetra::LinearSystem::applyJacobian(const NOX::Epetra::Vector &input, NOX::Epetra::Vector &result) const =0 Applies Jacobian to the given input vector and puts the answer in the result. Computes \\[ v = J u, \\] where $J$ is the Jacobian, $u$ is the input vector, and $v$ is the result vector. Returns true if successful.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::applyJacobian | ( | self, | ||
| args | ||||
| ) |
applyJacobian(self, Vector input, Vector nox_result) -> bool virtual bool NOX::Epetra::LinearSystem::applyJacobian(const NOX::Epetra::Vector &input, NOX::Epetra::Vector &result) const =0 Applies Jacobian to the given input vector and puts the answer in the result. Computes \\[ v = J u, \\] where $J$ is the Jacobian, $u$ is the input vector, and $v$ is the result vector. Returns true if successful.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::applyJacobianInverse | ( | self, | ||
| args | ||||
| ) |
applyJacobianInverse(self, ParameterList params, Vector input, Vector nox_result) -> bool
virtual bool
NOX::Epetra::LinearSystem::applyJacobianInverse(Teuchos::ParameterList
¶ms, const NOX::Epetra::Vector &input, NOX::Epetra::Vector
&result)=0
Applies the inverse of the Jacobian matrix to the given input vector
and puts the answer in result.
Computes \\[ v = J^{-1} u, \\] where $J$ is the Jacobian, $u$ is
the input vector, and $v$ is the result vector.
The parameter list contains the linear solver options.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::applyJacobianInverse | ( | self, | ||
| args | ||||
| ) |
applyJacobianInverse(self, ParameterList params, Vector input, Vector nox_result) -> bool
virtual bool
NOX::Epetra::LinearSystem::applyJacobianInverse(Teuchos::ParameterList
¶ms, const NOX::Epetra::Vector &input, NOX::Epetra::Vector
&result)=0
Applies the inverse of the Jacobian matrix to the given input vector
and puts the answer in result.
Computes \\[ v = J^{-1} u, \\] where $J$ is the Jacobian, $u$ is
the input vector, and $v$ is the result vector.
The parameter list contains the linear solver options.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::applyJacobianTranspose | ( | self, | ||
| args | ||||
| ) |
applyJacobianTranspose(self, Vector input, Vector nox_result) -> bool virtual bool NOX::Epetra::LinearSystem::applyJacobianTranspose(const NOX::Epetra::Vector &input, NOX::Epetra::Vector &result) const =0 Applies Jacobian-Transpose to the given input vector and puts the answer in the result. Computes \\[ v = J^T u, \\] where $J$ is the Jacobian, $u$ is the input vector, and $v$ is the result vector. Returns true if successful.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::applyJacobianTranspose | ( | self, | ||
| args | ||||
| ) |
applyJacobianTranspose(self, Vector input, Vector nox_result) -> bool virtual bool NOX::Epetra::LinearSystem::applyJacobianTranspose(const NOX::Epetra::Vector &input, NOX::Epetra::Vector &result) const =0 Applies Jacobian-Transpose to the given input vector and puts the answer in the result. Computes \\[ v = J^T u, \\] where $J$ is the Jacobian, $u$ is the input vector, and $v$ is the result vector. Returns true if successful.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::applyRightPreconditioning | ( | self, | ||
| args | ||||
| ) |
applyRightPreconditioning(self, bool useTranspose, ParameterList params, Vector input,
Vector nox_result) -> bool
virtual bool
NOX::Epetra::LinearSystem::applyRightPreconditioning(bool
useTranspose, Teuchos::ParameterList ¶ms, const
NOX::Epetra::Vector &input, NOX::Epetra::Vector &result) const =0
Apply right preconditiong to the given input vector.
Let $M$ be a right preconditioner for the Jacobian $J$; in other
words, $M$ is a matrix such that \\[ JM \\approx I. \\]
Compute \\[ u = M^{-1} v, \\] where $u$ is the input vector and
$v$ is the result vector.
If useTranspose is true, then the transpose of the preconditioner is
applied: \\[ u = {M^{-1}}^T v, \\] The transpose preconditioner is
currently only required for Tensor methods.
The parameter list contains the linear solver options.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::applyRightPreconditioning | ( | self, | ||
| args | ||||
| ) |
applyRightPreconditioning(self, bool useTranspose, ParameterList params, Vector input,
Vector nox_result) -> bool
virtual bool
NOX::Epetra::LinearSystem::applyRightPreconditioning(bool
useTranspose, Teuchos::ParameterList ¶ms, const
NOX::Epetra::Vector &input, NOX::Epetra::Vector &result) const =0
Apply right preconditiong to the given input vector.
Let $M$ be a right preconditioner for the Jacobian $J$; in other
words, $M$ is a matrix such that \\[ JM \\approx I. \\]
Compute \\[ u = M^{-1} v, \\] where $u$ is the input vector and
$v$ is the result vector.
If useTranspose is true, then the transpose of the preconditioner is
applied: \\[ u = {M^{-1}}^T v, \\] The transpose preconditioner is
currently only required for Tensor methods.
The parameter list contains the linear solver options.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::computeJacobian | ( | self, | ||
| args | ||||
| ) |
computeJacobian(self, Vector x) -> bool virtual bool NOX::Epetra::LinearSystem::computeJacobian(const NOX::Epetra::Vector &x)=0 Evaluates the Jacobian based on the solution vector x.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::computeJacobian | ( | self, | ||
| args | ||||
| ) |
computeJacobian(self, Vector x) -> bool virtual bool NOX::Epetra::LinearSystem::computeJacobian(const NOX::Epetra::Vector &x)=0 Evaluates the Jacobian based on the solution vector x.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::createPreconditioner | ( | self, | ||
| args | ||||
| ) |
createPreconditioner(self, Vector x, ParameterList p, bool recomputeGraph) -> bool virtual bool NOX::Epetra::LinearSystem::createPreconditioner(const NOX::Epetra::Vector &x, Teuchos::ParameterList &p, bool recomputeGraph) const =0 Explicitly constructs a preconditioner based on the solution vector x and the parameter list p. The user has the option of recomputing the graph when a new preconditioner is created. The NOX::Epetra::Group controls the isValid flag for the preconditioner and will control when to call this.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::createPreconditioner | ( | self, | ||
| args | ||||
| ) |
createPreconditioner(self, Vector x, ParameterList p, bool recomputeGraph) -> bool virtual bool NOX::Epetra::LinearSystem::createPreconditioner(const NOX::Epetra::Vector &x, Teuchos::ParameterList &p, bool recomputeGraph) const =0 Explicitly constructs a preconditioner based on the solution vector x and the parameter list p. The user has the option of recomputing the graph when a new preconditioner is created. The NOX::Epetra::Group controls the isValid flag for the preconditioner and will control when to call this.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::destroyPreconditioner | ( | self, | ||
| args | ||||
| ) |
destroyPreconditioner(self) -> bool virtual bool NOX::Epetra::LinearSystem::destroyPreconditioner() const =0 Deletes the preconditioner. The NOX::Epetra::Group controls the isValid flag for the preconditioner and will control when to call this.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::destroyPreconditioner | ( | self, | ||
| args | ||||
| ) |
destroyPreconditioner(self) -> bool virtual bool NOX::Epetra::LinearSystem::destroyPreconditioner() const =0 Deletes the preconditioner. The NOX::Epetra::Group controls the isValid flag for the preconditioner and will control when to call this.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::getGeneratedPrecOperator | ( | self, | ||
| args | ||||
| ) |
getGeneratedPrecOperator(self) -> Teuchos::RCP<(q(const).Epetra_Operator)> getGeneratedPrecOperator(self) -> Teuchos::RCP<(Epetra_Operator)> virtual Teuchos::RCP<Epetra_Operator> NOX::Epetra::LinearSystem::getGeneratedPrecOperator()=0 Return preconditioner operator.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::getGeneratedPrecOperator | ( | self, | ||
| args | ||||
| ) |
getGeneratedPrecOperator(self) -> Teuchos::RCP<(q(const).Epetra_Operator)> getGeneratedPrecOperator(self) -> Teuchos::RCP<(Epetra_Operator)> virtual Teuchos::RCP<Epetra_Operator> NOX::Epetra::LinearSystem::getGeneratedPrecOperator()=0 Return preconditioner operator.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::getJacobianOperator | ( | self, | ||
| args | ||||
| ) |
getJacobianOperator(self) -> Teuchos::RCP<(q(const).Epetra_Operator)> getJacobianOperator(self) -> Teuchos::RCP<(Epetra_Operator)> virtual Teuchos::RCP<Epetra_Operator> NOX::Epetra::LinearSystem::getJacobianOperator()=0 Return Jacobian operator.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::getJacobianOperator | ( | self, | ||
| args | ||||
| ) |
getJacobianOperator(self) -> Teuchos::RCP<(q(const).Epetra_Operator)> getJacobianOperator(self) -> Teuchos::RCP<(Epetra_Operator)> virtual Teuchos::RCP<Epetra_Operator> NOX::Epetra::LinearSystem::getJacobianOperator()=0 Return Jacobian operator.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::getPreconditionerPolicy | ( | self, | ||
| args | ||||
| ) |
getPreconditionerPolicy(self, bool advanceReuseCounter = True) -> PreconditionerReusePolicyType virtual PreconditionerReusePolicyType NOX::Epetra::LinearSystem::getPreconditionerPolicy(bool advanceReuseCounter=true)=0 Evaluates the preconditioner policy at the current state. NOTE: This can change values between nonlienar iterations. It is not a static value.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::getPreconditionerPolicy | ( | self, | ||
| args | ||||
| ) |
getPreconditionerPolicy(self, bool advanceReuseCounter = True) -> PreconditionerReusePolicyType virtual PreconditionerReusePolicyType NOX::Epetra::LinearSystem::getPreconditionerPolicy(bool advanceReuseCounter=true)=0 Evaluates the preconditioner policy at the current state. NOTE: This can change values between nonlienar iterations. It is not a static value.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::getScaling | ( | self, | ||
| args | ||||
| ) |
getScaling(self) -> Teuchos::RCP<(NOX::Epetra::Scaling)> virtual Teuchos::RCP<NOX::Epetra::Scaling> NOX::Epetra::LinearSystem::getScaling()=0 Get the scaling object.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::getScaling | ( | self, | ||
| args | ||||
| ) |
getScaling(self) -> Teuchos::RCP<(NOX::Epetra::Scaling)> virtual Teuchos::RCP<NOX::Epetra::Scaling> NOX::Epetra::LinearSystem::getScaling()=0 Get the scaling object.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::hasPreconditioner | ( | self, | ||
| args | ||||
| ) |
hasPreconditioner(self) -> bool virtual bool NOX::Epetra::LinearSystem::hasPreconditioner() const =0 Indicates whether the linear system has a preconditioner.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::hasPreconditioner | ( | self, | ||
| args | ||||
| ) |
hasPreconditioner(self) -> bool virtual bool NOX::Epetra::LinearSystem::hasPreconditioner() const =0 Indicates whether the linear system has a preconditioner.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::isPreconditionerConstructed | ( | self, | ||
| args | ||||
| ) |
isPreconditionerConstructed(self) -> bool virtual bool NOX::Epetra::LinearSystem::isPreconditionerConstructed() const =0 Indicates whether a preconditioner has been constructed.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::isPreconditionerConstructed | ( | self, | ||
| args | ||||
| ) |
isPreconditionerConstructed(self) -> bool virtual bool NOX::Epetra::LinearSystem::isPreconditionerConstructed() const =0 Indicates whether a preconditioner has been constructed.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::recomputePreconditioner | ( | self, | ||
| args | ||||
| ) |
recomputePreconditioner(self, Vector x, ParameterList linearSolverParams) -> bool virtual bool NOX::Epetra::LinearSystem::recomputePreconditioner(const NOX::Epetra::Vector &x, Teuchos::ParameterList &linearSolverParams) const =0 Recalculates the preconditioner using an already allocated graph. Use this to compute a new preconditioner while using the same graph for the preconditioner. This avoids deleting and reallocating the memory required for the preconditioner and results in a big speed-up for large-scale jobs.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::recomputePreconditioner | ( | self, | ||
| args | ||||
| ) |
recomputePreconditioner(self, Vector x, ParameterList linearSolverParams) -> bool virtual bool NOX::Epetra::LinearSystem::recomputePreconditioner(const NOX::Epetra::Vector &x, Teuchos::ParameterList &linearSolverParams) const =0 Recalculates the preconditioner using an already allocated graph. Use this to compute a new preconditioner while using the same graph for the preconditioner. This avoids deleting and reallocating the memory required for the preconditioner and results in a big speed-up for large-scale jobs.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::resetScaling | ( | self, | ||
| args | ||||
| ) |
resetScaling(self, Teuchos::RCP<(NOX::Epetra::Scaling)> s) virtual void NOX::Epetra::LinearSystem::resetScaling(const Teuchos::RCP< NOX::Epetra::Scaling > &s)=0 Sets the diagonal scaling vector(s) used in scaling the linear system. See NOX::Epetra::Scaling for details on how to specify scaling of the linear system.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::resetScaling | ( | self, | ||
| args | ||||
| ) |
resetScaling(self, Teuchos::RCP<(NOX::Epetra::Scaling)> s) virtual void NOX::Epetra::LinearSystem::resetScaling(const Teuchos::RCP< NOX::Epetra::Scaling > &s)=0 Sets the diagonal scaling vector(s) used in scaling the linear system. See NOX::Epetra::Scaling for details on how to specify scaling of the linear system.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::setJacobianOperatorForSolve | ( | self, | ||
| args | ||||
| ) |
setJacobianOperatorForSolve(self, Teuchos::RCP<(q(const).Epetra_Operator)> solveJacOp) virtual void NOX::Epetra::LinearSystem::setJacobianOperatorForSolve(const Teuchos::RCP< const Epetra_Operator > &solveJacOp)=0 Set Jacobian operator for solve.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::setJacobianOperatorForSolve | ( | self, | ||
| args | ||||
| ) |
setJacobianOperatorForSolve(self, Teuchos::RCP<(q(const).Epetra_Operator)> solveJacOp) virtual void NOX::Epetra::LinearSystem::setJacobianOperatorForSolve(const Teuchos::RCP< const Epetra_Operator > &solveJacOp)=0 Set Jacobian operator for solve.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::setPrecOperatorForSolve | ( | self, | ||
| args | ||||
| ) |
setPrecOperatorForSolve(self, Teuchos::RCP<(q(const).Epetra_Operator)> solvePrecOp) virtual void NOX::Epetra::LinearSystem::setPrecOperatorForSolve(const Teuchos::RCP< const Epetra_Operator > &solvePrecOp)=0 Set preconditioner operator for solve. Note: This should only be called if hasPreconditioner() returns true.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
| def PyTrilinos::NOX::Epetra::LinearSystem::setPrecOperatorForSolve | ( | self, | ||
| args | ||||
| ) |
setPrecOperatorForSolve(self, Teuchos::RCP<(q(const).Epetra_Operator)> solvePrecOp) virtual void NOX::Epetra::LinearSystem::setPrecOperatorForSolve(const Teuchos::RCP< const Epetra_Operator > &solvePrecOp)=0 Set preconditioner operator for solve. Note: This should only be called if hasPreconditioner() returns true.
Reimplemented in PyTrilinos::NOX::Epetra::LinearSystemAztecOO, and PyTrilinos::NOX::Epetra::LinearSystemAztecOO.
1.5.9