/usr/include/trilinos/ROL_lBFGS.hpp is in libtrilinos-rol-dev 12.12.1-5.
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// ************************************************************************
//
// Rapid Optimization Library (ROL) Package
// Copyright (2014) Sandia Corporation
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#ifndef ROL_LBFGS_H
#define ROL_LBFGS_H
/** \class ROL::lBFGS
\brief Provides definitions for limited-memory BFGS operators.
*/
#include "ROL_Secant.hpp"
namespace ROL {
template<class Real>
class lBFGS : public Secant<Real> {
public:
lBFGS(int M) : Secant<Real>(M) {}
// Apply lBFGS Approximate Inverse Hessian
void applyH( Vector<Real> &Hv, const Vector<Real> &v ) const {
// Get Generic Secant State
const Teuchos::RCP<SecantState<Real> >& state = Secant<Real>::get_state();
Real zero(0);
Hv.set(v.dual());
std::vector<Real> alpha(state->current+1,zero);
for (int i = state->current; i>=0; i--) {
alpha[i] = state->iterDiff[i]->dot(Hv);
alpha[i] /= state->product[i];
Hv.axpy(-alpha[i],(state->gradDiff[i])->dual());
}
// Apply initial inverse Hessian approximation to v
Teuchos::RCP<Vector<Real> > tmp = Hv.clone();
Secant<Real>::applyH0(*tmp,Hv.dual());
Hv.set(*tmp);
Real beta(0);
for (int i = 0; i <= state->current; i++) {
beta = Hv.dot((state->gradDiff[i])->dual());
beta /= state->product[i];
Hv.axpy((alpha[i]-beta),*(state->iterDiff[i]));
}
}
// Apply lBFGS Approximate Hessian
void applyB( Vector<Real> &Bv, const Vector<Real> &v ) const {
// Get Generic Secant State
const Teuchos::RCP<SecantState<Real> >& state = Secant<Real>::get_state();
Real one(1);
// Apply initial Hessian approximation to v
Secant<Real>::applyB0(Bv,v);
std::vector<Teuchos::RCP<Vector<Real> > > a(state->current+1);
std::vector<Teuchos::RCP<Vector<Real> > > b(state->current+1);
Real bv(0), av(0), bs(0), as(0);
for (int i = 0; i <= state->current; i++) {
b[i] = Bv.clone();
b[i]->set(*(state->gradDiff[i]));
b[i]->scale(one/sqrt(state->product[i]));
bv = v.dot(b[i]->dual());
Bv.axpy(bv,*b[i]);
a[i] = Bv.clone();
Secant<Real>::applyB0(*a[i],*(state->iterDiff[i]));
for (int j = 0; j < i; j++) {
bs = (state->iterDiff[i])->dot(b[j]->dual());
a[i]->axpy(bs,*b[j]);
as = (state->iterDiff[i])->dot(a[j]->dual());
a[i]->axpy(-as,*a[j]);
}
as = (state->iterDiff[i])->dot(a[i]->dual());
a[i]->scale(one/sqrt(as));
av = v.dot(a[i]->dual());
Bv.axpy(-av,*a[i]);
}
}
};
}
#endif
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