ROL_lDFP.hpp

Go to the documentation of this file.

// @HEADER
// ************************************************************************
//
//               Rapid Optimization Library (ROL) Package
//                 Copyright (2014) Sandia Corporation
//
// Under terms of Contract DE-AC04-94AL85000, there is a non-exclusive
// license for use of this work by or on behalf of the U.S. Government.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// 1. Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
//
// 2. Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
//
// 3. Neither the name of the Corporation nor the names of the
// contributors may be used to endorse or promote products derived from
// this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY SANDIA CORPORATION "AS IS" AND ANY
// EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL SANDIA CORPORATION OR THE
// CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
//
// Questions? Contact lead developers:
//              Drew Kouri   (dpkouri@sandia.gov) and
//              Denis Ridzal (dridzal@sandia.gov)
//
// ************************************************************************
// @HEADER

#ifndef ROL_LDFP_H
#define ROL_LDFP_H

#include "ROL_Secant.hpp"

namespace ROL {

template<class Real>
class lDFP : public Secant<Real> {
public:
  lDFP(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 ROL::Ptr<SecantState<Real> >& state = Secant<Real>::get_state();
    Real one(1);

    // Apply initial Hessian approximation to v
    applyH0(Hv,v);

    std::vector<ROL::Ptr<Vector<Real> > > a(state->current+1);
    std::vector<ROL::Ptr<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] = Hv.clone();
      b[i]->set(*(state->iterDiff[i]));
      b[i]->scale(1.0/sqrt(state->product[i]));
      bv = b[i]->dot(v.dual());
      Hv.axpy(bv,*b[i]);

      a[i] = Hv.clone();
      applyH0(*a[i],*(state->gradDiff[i]));

      for (int j = 0; j < i; j++) {
        bs = b[j]->dot((state->gradDiff[i])->dual());
        a[i]->axpy(bs,*b[j]);
        as = a[j]->dot((state->gradDiff[i])->dual());
        a[i]->axpy(-as,*a[j]);
      }
      as = a[i]->dot((state->gradDiff[i])->dual());
      a[i]->scale(one/sqrt(as));
      av = a[i]->dot(v.dual());
      Hv.axpy(-av,*a[i]);
    }
  }

  // Apply Initial Secant Approximate Hessian
  virtual void applyH0( Vector<Real> &Hv, const Vector<Real> &v ) const {
    // Get Generic Secant State
    const ROL::Ptr<SecantState<Real> >& state = Secant<Real>::get_state();

    Hv.set(v.dual());
    if (state->iter != 0 && state->current != -1) {
      Real ss = state->iterDiff[state->current]->dot(*(state->iterDiff[state->current]));
      Hv.scale(state->product[state->current]/ss);
    }
  }

  // Apply lBFGS Approximate Hessian
  void applyB( Vector<Real> &Bv, const Vector<Real> &v ) const {
    // Get Generic Secant State
    const ROL::Ptr<SecantState<Real> >& state = Secant<Real>::get_state();
    Real zero(0);

    Bv.set(v.dual());
    std::vector<Real> alpha(state->current+1,zero);
    for (int i = state->current; i>=0; i--) {
      alpha[i]  = state->gradDiff[i]->dot(Bv);
      alpha[i] /= state->product[i];
      Bv.axpy(-alpha[i],(state->iterDiff[i])->dual());
    }

    // Apply initial inverse Hessian approximation to v
    ROL::Ptr<Vector<Real> > tmp = Bv.clone();
    applyB0(*tmp,Bv);
    Bv.set(*tmp);

    Real beta(0);
    for (int i = 0; i <= state->current; i++) {
      beta  = state->iterDiff[i]->dot(Bv.dual());
      beta /= state->product[i];
      Bv.axpy((alpha[i]-beta),*(state->gradDiff[i]));
    }
  }

  // Apply Initial Secant Approximate Hessian
  virtual void applyB0( Vector<Real> &Bv, const Vector<Real> &v ) const {
    // Get Generic Secant State
    const ROL::Ptr<SecantState<Real> >& state = Secant<Real>::get_state();

    Bv.set(v.dual());
    if (state->iter != 0 && state->current != -1) {
      Real ss = state->iterDiff[state->current]->dot(*(state->iterDiff[state->current]));
      Bv.scale(ss/state->product[state->current]);
    }
  }
};

}

#endif