This file is indexed.

/usr/include/trilinos/ROL_lDFP.hpp is in libtrilinos-rol-dev 12.12.1-5.

This file is owned by root:root, with mode 0o644.

The actual contents of the file can be viewed below.

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
// @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

/** \class ROL::lDFP
    \brief Provides definitions for limited-memory DFP operators.
*/

#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 Teuchos::RCP<SecantState<Real> >& state = Secant<Real>::get_state();
    Real one(1);

    // Apply initial Hessian approximation to v
    applyH0(Hv,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] = 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 Teuchos::RCP<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 Teuchos::RCP<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
    Teuchos::RCP<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 Teuchos::RCP<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