ROL
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#include <ROL_StochasticConstraint.hpp>
Public Member Functions | |
StochasticConstraint (const Ptr< Objective< Real >> &obj, const Ptr< SampleGenerator< Real >> &sampler, ParameterList &parlist, const int index=0) | |
StochasticConstraint (const Ptr< Constraint< Real >> &con, const Ptr< SampleGenerator< Real >> &sampler, ParameterList &parlist, const int index=0) | |
Real | computeStatistic (const Vector< Real > &x) const |
void | setIndex (int ind) |
void | update (const Vector< Real > &x, UpdateType type, int iter=-1) |
Update constraint function. More... | |
void | update (const Vector< Real > &x, bool flag=true, int iter=-1) |
Update constraint functions. x is the optimization variable, flag = true if optimization variable is changed, iter is the outer algorithm iterations count. More... | |
void | value (Vector< Real > &c, const Vector< Real > &x, Real &tol) |
Evaluate the constraint operator \(c:\mathcal{X} \rightarrow \mathcal{C}\) at \(x\). More... | |
void | applyJacobian (Vector< Real > &jv, const Vector< Real > &v, const Vector< Real > &x, Real &tol) |
Apply the constraint Jacobian at \(x\), \(c'(x) \in L(\mathcal{X}, \mathcal{C})\), to vector \(v\). More... | |
void | applyAdjointJacobian (Vector< Real > &ajv, const Vector< Real > &v, const Vector< Real > &x, Real &tol) |
Apply the adjoint of the the constraint Jacobian at \(x\), \(c'(x)^* \in L(\mathcal{C}^*, \mathcal{X}^*)\), to vector \(v\). More... | |
void | applyAdjointHessian (Vector< Real > &ahuv, const Vector< Real > &u, const Vector< Real > &v, const Vector< Real > &x, Real &tol) |
Apply the derivative of the adjoint of the constraint Jacobian at \(x\) to vector \(u\) in direction \(v\), according to \( v \mapsto c''(x)(v,\cdot)^*u \). More... | |
Public Member Functions inherited from ROL::Constraint< Real > | |
virtual | ~Constraint (void) |
Constraint (void) | |
virtual void | applyAdjointJacobian (Vector< Real > &ajv, const Vector< Real > &v, const Vector< Real > &x, const Vector< Real > &dualv, Real &tol) |
Apply the adjoint of the the constraint Jacobian at \(x\), \(c'(x)^* \in L(\mathcal{C}^*, \mathcal{X}^*)\), to vector \(v\). More... | |
virtual std::vector< Real > | solveAugmentedSystem (Vector< Real > &v1, Vector< Real > &v2, const Vector< Real > &b1, const Vector< Real > &b2, const Vector< Real > &x, Real &tol) |
Approximately solves the augmented system
\[ \begin{pmatrix} I & c'(x)^* \\ c'(x) & 0 \end{pmatrix} \begin{pmatrix} v_{1} \\ v_{2} \end{pmatrix} = \begin{pmatrix} b_{1} \\ b_{2} \end{pmatrix} \] where \(v_{1} \in \mathcal{X}\), \(v_{2} \in \mathcal{C}^*\), \(b_{1} \in \mathcal{X}^*\), \(b_{2} \in \mathcal{C}\), \(I : \mathcal{X} \rightarrow \mathcal{X}^*\) is an identity or Riesz operator, and \(0 : \mathcal{C}^* \rightarrow \mathcal{C}\) is a zero operator. More... | |
virtual void | applyPreconditioner (Vector< Real > &pv, const Vector< Real > &v, const Vector< Real > &x, const Vector< Real > &g, Real &tol) |
Apply a constraint preconditioner at \(x\), \(P(x) \in L(\mathcal{C}, \mathcal{C}^*)\), to vector \(v\). Ideally, this preconditioner satisfies the following relationship:
\[ \left[c'(x) \circ R \circ c'(x)^* \circ P(x)\right] v = v \,, \] where R is the appropriate Riesz map in \(L(\mathcal{X}^*, \mathcal{X})\). It is used by the solveAugmentedSystem method. More... | |
void | activate (void) |
Turn on constraints. More... | |
void | deactivate (void) |
Turn off constraints. More... | |
bool | isActivated (void) |
Check if constraints are on. More... | |
virtual std::vector < std::vector< Real > > | checkApplyJacobian (const Vector< Real > &x, const Vector< Real > &v, const Vector< Real > &jv, const std::vector< Real > &steps, const bool printToStream=true, std::ostream &outStream=std::cout, const int order=1) |
Finite-difference check for the constraint Jacobian application. More... | |
virtual std::vector < std::vector< Real > > | checkApplyJacobian (const Vector< Real > &x, const Vector< Real > &v, const Vector< Real > &jv, const bool printToStream=true, std::ostream &outStream=std::cout, const int numSteps=ROL_NUM_CHECKDERIV_STEPS, const int order=1) |
Finite-difference check for the constraint Jacobian application. More... | |
virtual std::vector < std::vector< Real > > | checkApplyAdjointJacobian (const Vector< Real > &x, const Vector< Real > &v, const Vector< Real > &c, const Vector< Real > &ajv, const bool printToStream=true, std::ostream &outStream=std::cout, const int numSteps=ROL_NUM_CHECKDERIV_STEPS) |
Finite-difference check for the application of the adjoint of constraint Jacobian. More... | |
virtual Real | checkAdjointConsistencyJacobian (const Vector< Real > &w, const Vector< Real > &v, const Vector< Real > &x, const bool printToStream=true, std::ostream &outStream=std::cout) |
virtual Real | checkAdjointConsistencyJacobian (const Vector< Real > &w, const Vector< Real > &v, const Vector< Real > &x, const Vector< Real > &dualw, const Vector< Real > &dualv, const bool printToStream=true, std::ostream &outStream=std::cout) |
virtual std::vector < std::vector< Real > > | checkApplyAdjointHessian (const Vector< Real > &x, const Vector< Real > &u, const Vector< Real > &v, const Vector< Real > &hv, const std::vector< Real > &step, const bool printToScreen=true, std::ostream &outStream=std::cout, const int order=1) |
Finite-difference check for the application of the adjoint of constraint Hessian. More... | |
virtual std::vector < std::vector< Real > > | checkApplyAdjointHessian (const Vector< Real > &x, const Vector< Real > &u, const Vector< Real > &v, const Vector< Real > &hv, const bool printToScreen=true, std::ostream &outStream=std::cout, const int numSteps=ROL_NUM_CHECKDERIV_STEPS, const int order=1) |
Finite-difference check for the application of the adjoint of constraint Hessian. More... | |
virtual void | setParameter (const std::vector< Real > ¶m) |
Private Attributes | |
Ptr< StochasticObjective< Real > > | robj_ |
Ptr< Constraint< Real > > | con_ |
Ptr< SampleGenerator< Real > > | sampler_ |
Additional Inherited Members | |
Protected Member Functions inherited from ROL::Constraint< Real > | |
const std::vector< Real > | getParameter (void) const |
Definition at line 20 of file ROL_StochasticConstraint.hpp.
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Definition at line 27 of file ROL_StochasticConstraint.hpp.
References ROL::StochasticConstraint< Real >::con_, and ROL::StochasticConstraint< Real >::robj_.
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Definition at line 36 of file ROL_StochasticConstraint.hpp.
References ROL::StochasticConstraint< Real >::con_, and ROL::StochasticConstraint< Real >::robj_.
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Definition at line 53 of file ROL_StochasticConstraint.hpp.
References ROL::StochasticConstraint< Real >::robj_.
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Definition at line 57 of file ROL_StochasticConstraint.hpp.
References ROL::StochasticConstraint< Real >::robj_.
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Update constraint function.
This function updates the constraint function at new iterations.
[in] | x | is the new iterate. |
[in] | type | is the type of update requested. |
[in] | iter | is the outer algorithm iterations count. |
Reimplemented from ROL::Constraint< Real >.
Definition at line 61 of file ROL_StochasticConstraint.hpp.
References ROL::StochasticConstraint< Real >::con_.
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Update constraint functions. x is the optimization variable, flag = true if optimization variable is changed, iter is the outer algorithm iterations count.
Reimplemented from ROL::Constraint< Real >.
Definition at line 65 of file ROL_StochasticConstraint.hpp.
References ROL::StochasticConstraint< Real >::con_.
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Evaluate the constraint operator \(c:\mathcal{X} \rightarrow \mathcal{C}\) at \(x\).
[out] | c | is the result of evaluating the constraint operator at x; a constraint-space vector |
[in] | x | is the constraint argument; an optimization-space vector |
[in,out] | tol | is a tolerance for inexact evaluations; currently unused |
On return, \(\mathsf{c} = c(x)\), where \(\mathsf{c} \in \mathcal{C}\), \(\mathsf{x} \in \mathcal{X}\).
Implements ROL::Constraint< Real >.
Definition at line 69 of file ROL_StochasticConstraint.hpp.
References ROL::StochasticConstraint< Real >::con_.
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Apply the constraint Jacobian at \(x\), \(c'(x) \in L(\mathcal{X}, \mathcal{C})\), to vector \(v\).
@param[out] jv is the result of applying the constraint Jacobian to @b v at @b x; a constraint-space vector @param[in] v is an optimization-space vector @param[in] x is the constraint argument; an optimization-space vector @param[in,out] tol is a tolerance for inexact evaluations; currently unused On return, \form#91, where
\(v \in \mathcal{X}\), \(\mathsf{jv} \in \mathcal{C}\).
The default implementation is a finite-difference approximation.
Reimplemented from ROL::Constraint< Real >.
Definition at line 73 of file ROL_StochasticConstraint.hpp.
References ROL::StochasticConstraint< Real >::con_.
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Apply the adjoint of the the constraint Jacobian at \(x\), \(c'(x)^* \in L(\mathcal{C}^*, \mathcal{X}^*)\), to vector \(v\).
@param[out] ajv is the result of applying the adjoint of the constraint Jacobian to @b v at @b x; a dual optimization-space vector @param[in] v is a dual constraint-space vector @param[in] x is the constraint argument; an optimization-space vector @param[in,out] tol is a tolerance for inexact evaluations; currently unused On return, \form#95, where
\(v \in \mathcal{C}^*\), \(\mathsf{ajv} \in \mathcal{X}^*\).
The default implementation is a finite-difference approximation.
Reimplemented from ROL::Constraint< Real >.
Definition at line 77 of file ROL_StochasticConstraint.hpp.
References ROL::StochasticConstraint< Real >::con_.
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Apply the derivative of the adjoint of the constraint Jacobian at \(x\) to vector \(u\) in direction \(v\), according to \( v \mapsto c''(x)(v,\cdot)^*u \).
@param[out] ahuv is the result of applying the derivative of the adjoint of the constraint Jacobian at @b x to vector @b u in direction @b v; a dual optimization-space vector @param[in] u is the direction vector; a dual constraint-space vector @param[in] v is an optimization-space vector @param[in] x is the constraint argument; an optimization-space vector @param[in,out] tol is a tolerance for inexact evaluations; currently unused On return, \form#100, where
\(u \in \mathcal{C}^*\), \(v \in \mathcal{X}\), and \(\mathsf{ahuv} \in \mathcal{X}^*\).
The default implementation is a finite-difference approximation based on the adjoint Jacobian.
Reimplemented from ROL::Constraint< Real >.
Definition at line 81 of file ROL_StochasticConstraint.hpp.
References ROL::StochasticConstraint< Real >::con_.
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Definition at line 22 of file ROL_StochasticConstraint.hpp.
Referenced by ROL::StochasticConstraint< Real >::computeStatistic(), ROL::StochasticConstraint< Real >::setIndex(), and ROL::StochasticConstraint< Real >::StochasticConstraint().
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Definition at line 23 of file ROL_StochasticConstraint.hpp.
Referenced by ROL::StochasticConstraint< Real >::applyAdjointHessian(), ROL::StochasticConstraint< Real >::applyAdjointJacobian(), ROL::StochasticConstraint< Real >::applyJacobian(), ROL::StochasticConstraint< Real >::StochasticConstraint(), ROL::StochasticConstraint< Real >::update(), and ROL::StochasticConstraint< Real >::value().
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Definition at line 24 of file ROL_StochasticConstraint.hpp.