ROL
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ROL::StochasticConstraint< Real > Class Template Reference

#include <ROL_StochasticConstraint.hpp>

+ Inheritance diagram for ROL::StochasticConstraint< Real >:

Public Member Functions

 StochasticConstraint (const Ptr< Objective< Real >> &obj, const Ptr< SampleGenerator< Real >> &sampler, ROL::ParameterList &parlist, const int index=0)
 
 StochasticConstraint (const Ptr< Constraint< Real >> &con, const Ptr< SampleGenerator< Real >> &sampler, ROL::ParameterList &parlist, const int index=0)
 
Real computeStatistic (const Vector< Real > &x) const
 
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 > &param)
 

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
 

Detailed Description

template<class Real>
class ROL::StochasticConstraint< Real >

Definition at line 54 of file ROL_StochasticConstraint.hpp.

Constructor & Destructor Documentation

template<class Real >
ROL::StochasticConstraint< Real >::StochasticConstraint ( const Ptr< Objective< Real >> &  obj,
const Ptr< SampleGenerator< Real >> &  sampler,
ROL::ParameterList &  parlist,
const int  index = 0 
)
inline
template<class Real >
ROL::StochasticConstraint< Real >::StochasticConstraint ( const Ptr< Constraint< Real >> &  con,
const Ptr< SampleGenerator< Real >> &  sampler,
ROL::ParameterList &  parlist,
const int  index = 0 
)
inline

Member Function Documentation

template<class Real >
Real ROL::StochasticConstraint< Real >::computeStatistic ( const Vector< Real > &  x) const
inline
template<class Real >
void ROL::StochasticConstraint< Real >::update ( const Vector< Real > &  x,
bool  flag = true,
int  iter = -1 
)
inlinevirtual

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 91 of file ROL_StochasticConstraint.hpp.

References ROL::StochasticConstraint< Real >::con_.

template<class Real >
void ROL::StochasticConstraint< Real >::value ( Vector< Real > &  c,
const Vector< Real > &  x,
Real &  tol 
)
inlinevirtual

Evaluate the constraint operator \(c:\mathcal{X} \rightarrow \mathcal{C}\) at \(x\).

Parameters
[out]cis the result of evaluating the constraint operator at x; a constraint-space vector
[in]xis the constraint argument; an optimization-space vector
[in,out]tolis 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 95 of file ROL_StochasticConstraint.hpp.

References ROL::StochasticConstraint< Real >::con_.

template<class Real >
void ROL::StochasticConstraint< Real >::applyJacobian ( Vector< Real > &  jv,
const Vector< Real > &  v,
const Vector< Real > &  x,
Real &  tol 
)
inlinevirtual

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#76, 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 99 of file ROL_StochasticConstraint.hpp.

References ROL::StochasticConstraint< Real >::con_.

template<class Real >
void ROL::StochasticConstraint< Real >::applyAdjointJacobian ( Vector< Real > &  ajv,
const Vector< Real > &  v,
const Vector< Real > &  x,
Real &  tol 
)
inlinevirtual

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#80, 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 103 of file ROL_StochasticConstraint.hpp.

References ROL::StochasticConstraint< Real >::con_.

template<class Real >
void ROL::StochasticConstraint< Real >::applyAdjointHessian ( Vector< Real > &  ahuv,
const Vector< Real > &  u,
const Vector< Real > &  v,
const Vector< Real > &  x,
Real &  tol 
)
inlinevirtual

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#85, 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 107 of file ROL_StochasticConstraint.hpp.

References ROL::StochasticConstraint< Real >::con_.

Member Data Documentation

template<class Real >
Ptr<StochasticObjective<Real> > ROL::StochasticConstraint< Real >::robj_
private
template<class Real >
Ptr<Constraint<Real> > ROL::StochasticConstraint< Real >::con_
private
template<class Real >
Ptr<SampleGenerator<Real> > ROL::StochasticConstraint< Real >::sampler_
private

Definition at line 58 of file ROL_StochasticConstraint.hpp.


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