/usr/include/trilinos/ROL_Sacado_Objective.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_OBJECTIVE
#define ROL_SACADO_OBJECTIVE
#include "Sacado.hpp"
#include "ROL_StdVector.hpp"
#include "ROL_Objective.hpp"
using namespace ROL;
/** \brief Generic objective wrapper class for class that uses Sacado */
template<class Real, template<class> class Obj>
class Sacado_Objective : public Objective<Real> {
protected:
Obj<Real> obj_;
/* Evaluate the gradient at x */
template<class ScalarT>
void gradientAD( Vector<ScalarT> &g, const Vector<ScalarT> &x, Real &tol );
/* Compute the action of the Hessian evaluated at x on a vector v */
template<class ScalarT>
void hessVecAD( Vector<ScalarT> &hv, const Vector<ScalarT> &v, const Vector<ScalarT> &x, Real &tol );
public:
Sacado_Objective() : obj_(Obj<Real>()) {}
Sacado_Objective(const Obj<Real> &obj) : obj_(obj) {}
/* Evaluate the objective function at x */
Real value( const Vector<Real> &x, Real &tol ) {
return obj_.value(x,tol);
}
/* Evaluate the gradient at x */
void gradient( Vector<Real> &g, const Vector<Real> &x, Real &tol ) {
this->gradientAD(g,x,tol);
}
/* Compute the action of the Hessian evaluated at x on a vector v */
void hessVec( Vector<Real> &hv, const Vector<Real> &v, const Vector<Real> &x, Real &tol ) {
this->hessVecAD(hv,v,x,tol);
}
};
template<class Real, template<class> class Obj>
template<class ScalarT>
void Sacado_Objective<Real,Obj>::gradientAD(Vector<ScalarT> &g, 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 gradient vector
RCP<vector> gp = dyn_cast<SV>(g).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]));
}
StdVector<FadType> x_fad(x_fad_rcp);
// AD access to objective function
FadType J_fad = obj_.value(x_fad,tol);
// Evaluate gradient
for(int i=0; i<n; ++i) {
(*gp)[i] = J_fad.dx(i);
}
}
template <class Real, template<class> class Obj>
template <class ScalarT>
void Sacado_Objective<Real,Obj>::hessVecAD( Vector<ScalarT> &hv, 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 direction vector
RCP<const vector> vp = dyn_cast<const SV>(v).getVector();
RCP<vector> hvp = dyn_cast<SV>(hv).getVector();
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 gradient
RCP<Fadvector> g_fad_rcp = rcp( new Fadvector );
g_fad_rcp->reserve(n);
for(int i=0; i<n; ++i) {
x_fad_rcp->push_back(FadType(1,(*xp)[i]));
}
// Set directional derivative
for(int i=0; i<n; ++i) {
(*x_fad_rcp)[i].fastAccessDx(0) = (*vp)[i];
}
StdVector<FadType> x_fad(x_fad_rcp);
StdVector<FadType> g_fad(g_fad_rcp);
this->gradientAD(g_fad,x_fad,tol);
for(int i=0; i<n; ++i) {
(*hvp)[i] = (*g_fad_rcp)[i].dx(0);
}
}
#endif
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