/usr/include/trilinos/ROL_Sacado_EqualityConstraint.hpp is in libtrilinos-rol-dev 12.12.1-5.
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// ************************************************************************
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// Rapid Optimization Library (ROL) Package
// Copyright (2014) Sandia Corporation
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#ifndef ROL_SACADO_EQUALITYCONSTRAINT
#define ROL_SACADO_EQUALITYCONSTRAINT
#include "Sacado.hpp"
#include "ROL_StdVector.hpp"
#include "ROL_EqualityConstraint.hpp"
#include "ROL_InequalityConstraint.hpp"
namespace ROL {
//! \brief ROL interface wrapper for Sacado Constraint
template<class Real, template<class> class Constr>
class Sacado_EqualityConstraint : public virtual EqualityConstraint<Real> {
protected:
Constr<Real> constr_;
int dim_;
template<class ScalarT>
void applyJacobianAD( Vector<ScalarT> &jv, const Vector<ScalarT> &v,
const Vector<ScalarT> &x, Real &tol );
template<class ScalarT>
void applyAdjointJacobianAD( Vector<ScalarT> &aju, const Vector<ScalarT> &u,
const Vector<ScalarT> &x, Real &tol);
template<class ScalarT>
void applyAdjointHessianAD( Vector<ScalarT> &ahuv, const Vector<ScalarT> &u,
const Vector<ScalarT> &v, const Vector<ScalarT> &x, Real &tol);
public:
Sacado_EqualityConstraint(int dim) : dim_(dim) {}
Sacado_EqualityConstraint(const Constr<Real> & constr) : constr_(constr) {}
virtual void value(Vector<Real> &c, const Vector<Real> &x, Real &tol) {
constr_.value(c,x,tol);
}
virtual void applyJacobian(Vector<Real> &jv, const Vector<Real> &v,
const Vector<Real> &x, Real &tol) {
this->applyJacobianAD(jv,v,x,tol);
}
virtual void applyAdjointJacobian(Vector<Real> &aju, const Vector<Real> &u,
const Vector<Real> &x, Real &tol) {
this->applyAdjointJacobianAD(aju,u,x,tol);
}
void applyAdjointHessian(Vector<Real> &ahuv, const Vector<Real> &u,
const Vector<Real> &v, const Vector<Real> &x, Real &tol){
this->applyAdjointHessianAD(ahuv,u,v,x,tol);
}
};
template<class Real, template<class> class Constr>
template<class ScalarT>
void Sacado_EqualityConstraint<Real,Constr>::applyJacobianAD(Vector<ScalarT> &jv, const Vector<ScalarT> &v,
const Vector<ScalarT> &x, Real &tol) {
// Data type which supports automatic differentiation
typedef Sacado::Fad::DFad<ScalarT> FadType;
typedef std::vector<FadType> Fadvector;
typedef std::vector<ScalarT> vector;
typedef StdVector<ScalarT> SV;
using Teuchos::RCP; using Teuchos::rcp;
using Teuchos::dyn_cast;
RCP<const vector> xp = dyn_cast<const SV>(x).getVector();
int n = xp->size();
// Get a pointer to the direction vector
RCP<const vector> vp = dyn_cast<const SV>(v).getVector();
RCP<vector> jvp = dyn_cast<SV>(jv).getVector();
// Create a vector of independent variables
RCP<Fadvector> x_fad_rcp = rcp( new Fadvector );
x_fad_rcp->reserve(n);
// Initialize constructor for each element
for(int i=0; i<n; ++i) {
x_fad_rcp->push_back(FadType(n,i,(*xp)[i]));
}
// Create a vector of independent variables
RCP<Fadvector> c_fad_rcp = rcp( new Fadvector );
c_fad_rcp->reserve(dim_);
for(int j=0; j<dim_; ++j) {
c_fad_rcp->push_back(0);
}
StdVector<FadType> x_fad(x_fad_rcp);
StdVector<FadType> c_fad(c_fad_rcp);
// Evaluate constraint
constr_.value(c_fad,x_fad,tol);
for(int i=0; i<dim_; ++i) {
(*jvp)[i] = 0;
for(int j=0; j<n; ++j) {
(*jvp)[i] += (*vp)[j]*(*c_fad_rcp)[i].dx(j);
}
}
}
template<class Real, template<class> class Constr>
template<class ScalarT>
void Sacado_EqualityConstraint<Real,Constr>::applyAdjointJacobianAD(Vector<ScalarT> &aju, const Vector<ScalarT> &u,
const Vector<ScalarT> &x, Real &tol) {
// Data type which supports automatic differentiation
typedef Sacado::Fad::DFad<ScalarT> FadType;
typedef std::vector<FadType> Fadvector;
typedef std::vector<ScalarT> vector;
typedef StdVector<ScalarT> SV;
using Teuchos::RCP; using Teuchos::rcp;
using Teuchos::dyn_cast;
// Get a pointer to the optimization vector
RCP<const vector> xp = dyn_cast<const SV>(x).getVector();
// Get a pointer to the direction vector
RCP<const vector> up = dyn_cast<const SV>(u).getVector();
RCP<vector> ajup = dyn_cast<SV>(aju).getVector();
int n = xp->size();
// Create a vector of independent variables
RCP<Fadvector> x_fad_rcp = rcp( new Fadvector );
x_fad_rcp->reserve(n);
// Initialize constructor for each element
for(int i=0; i<n; ++i) {
x_fad_rcp->push_back(FadType(n,i,(*xp)[i]));
}
RCP<Fadvector> c_fad_rcp = rcp( new Fadvector );
c_fad_rcp->reserve(dim_);
for(int j=0; j<dim_; ++j) {
c_fad_rcp->push_back(0);
}
StdVector<FadType> x_fad(x_fad_rcp);
StdVector<FadType> c_fad(c_fad_rcp);
// Evaluate constraint
constr_.value(c_fad,x_fad,tol);
FadType udotc = 0;
for(int j=0;j<dim_;++j){
udotc += (*c_fad_rcp)[j]*(*up)[j];
}
for(int i=0;i<n;++i){
(*ajup)[i] = udotc.dx(i);
}
}
template<class Real, template<class> class Constr>
template<class ScalarT>
void Sacado_EqualityConstraint<Real,Constr>::applyAdjointHessianAD(Vector<ScalarT> &ahuv, const Vector<ScalarT> &u,
const Vector<ScalarT> &v, const Vector<ScalarT> &x,
Real &tol){
// Data type which supports automatic differentiation
typedef Sacado::Fad::SFad<ScalarT,1> FadType;
typedef std::vector<FadType> Fadvector;
typedef std::vector<ScalarT> vector;
typedef StdVector<ScalarT> SV;
using Teuchos::RCP; using Teuchos::rcp;
using Teuchos::dyn_cast;
// Get a pointer to the optimization vector
RCP<const vector> xp = dyn_cast<const SV>(x).getVector();
// Get a pointer to the dual constraint vector
RCP<const vector> up = dyn_cast<const SV>(u).getVector();
// Get a pointer to the direction vector
RCP<const vector> vp = dyn_cast<const SV>(v).getVector();
// Get a pointer to the directional adjoint Hessian
RCP<vector> ahuvp = dyn_cast<SV>(ahuv).getVector();
// Number of optimization variables
int n = xp->size();
// Create a vector of independent variables
RCP<Fadvector> x_fad_rcp = rcp( new Fadvector );
x_fad_rcp->reserve(n);
// Allocate for directional adjoint Jacobian
RCP<Fadvector> aju_fad_rcp = rcp( new Fadvector );
aju_fad_rcp->reserve(n);
for(int i=0; i<n; ++i) {
x_fad_rcp->push_back(FadType(1,(*xp)[i]));
// Set derivative direction
(*x_fad_rcp)[i].fastAccessDx(0) = (*vp)[i];
aju_fad_rcp->push_back(0);
}
// Allocate for constraint vector
RCP<Fadvector> c_fad_rcp = rcp( new Fadvector );
c_fad_rcp->reserve(dim_);
// Allocate for dual constraint vector
RCP<Fadvector> u_fad_rcp = rcp( new Fadvector );
u_fad_rcp->reserve(dim_);
for(int j=0; j<dim_; ++j) {
u_fad_rcp->push_back((*up)[j]);
}
StdVector<FadType> x_fad(x_fad_rcp);
StdVector<FadType> u_fad(u_fad_rcp);
StdVector<FadType> aju_fad(aju_fad_rcp);
// Evaluate constraint adjoint Jacobian direction
this->applyAdjointJacobianAD( aju_fad, u_fad, x_fad, tol);
for(int i=0; i<n; ++i) {
(*ahuvp)[i] = (*aju_fad_rcp)[i].dx(0);
}
}
/* Here we use multiple inheritance because a distinct InequalityConstraint signature
* is needed to satisfy the OptimizationProblem interface, but all of InequalityConstraint's
* methods do the exact same thing as EqualityConstraint's. Multiple inheritance saves
* on the amount of code needed, however, we do need to specify unique final overriders
* for this class
*/
template<class Real, template<class> class Constr>
class Sacado_InequalityConstraint : public InequalityConstraint<Real>,
public Sacado_EqualityConstraint<Real,Constr> {
typedef Sacado_EqualityConstraint<Real,Constr> SEC;
public:
Sacado_InequalityConstraint(int dim) : SEC(dim) {}
Sacado_InequalityConstraint(const Constr<Real> & constr) : SEC(constr) {}
void value(Vector<Real> &c, const Vector<Real> &x, Real &tol) {
SEC::value(c,x,tol);
}
void applyJacobian(Vector<Real> &jv, const Vector<Real> &v,
const Vector<Real> &x, Real &tol) {
SEC::applyJacobianAD(jv,v,x,tol);
}
void applyAdjointJacobian(Vector<Real> &aju, const Vector<Real> &u,
const Vector<Real> &x, Real &tol) {
SEC::applyAdjointJacobianAD(aju,u,x,tol);
}
void applyAdjointHessian(Vector<Real> &ahuv, const Vector<Real> &u,
const Vector<Real> &v, const Vector<Real> &x, Real &tol){
SEC::applyAdjointHessianAD(ahuv,u,v,x,tol);
}
};
}
#endif
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