This file is indexed.

/usr/include/trilinos/ROL_Secant.hpp is in libtrilinos-rol-dev 12.10.1-3.

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
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
// @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_SECANT_H
#define ROL_SECANT_H

/** \class ROL::Secant
    \brief Provides interface for and implements limited-memory secant operators.
*/

#include "Teuchos_ParameterList.hpp"
#include "ROL_LinearOperator.hpp"
#include "ROL_Types.hpp"

namespace ROL {

template<class Real>
struct SecantState {
  Teuchos::RCP<Vector<Real> >               iterate;
  std::vector<Teuchos::RCP<Vector<Real> > > iterDiff; // Step Storage
  std::vector<Teuchos::RCP<Vector<Real> > > gradDiff; // Gradient Storage
  std::vector<Real>                         product;  // Step-Gradient Inner Product Storage
  std::vector<Real>                         product2; // Step-Gradient Inner Product Storage
  int storage;                                        // Storage Size
  int current;                                        // Current Storage Size
  int iter;                                           // Current Optimization Iteration
};

template<class Real>
class Secant : public LinearOperator<Real> {
private:

  Teuchos::RCP<SecantState<Real> > state_; // Secant State
  bool isInitialized_;

public:

  virtual ~Secant() {}

  // Constructor
  Secant( int M = 10 ) : isInitialized_(false) {
    state_ = Teuchos::rcp( new SecantState<Real> ); 
    state_->storage = M;
    state_->current = -1;
    state_->iter    = 0;
  }

  Teuchos::RCP<SecantState<Real> >& get_state() { return state_; }
  const Teuchos::RCP<SecantState<Real> >& get_state() const { return state_; }

  // Update Secant Approximation
  virtual void updateStorage( const Vector<Real> &x,  const Vector<Real> &grad,
                              const Vector<Real> &gp, const Vector<Real> &s,
                              const Real snorm,       const int iter ) {
    Real one(1);
    if ( !isInitialized_ ) {
      state_->iterate = x.clone();
      isInitialized_ = true;
    }
    state_->iterate->set(x);
    state_->iter = iter;
    Teuchos::RCP<Vector<Real> > gradDiff = grad.clone();
    gradDiff->set(grad);
    gradDiff->axpy(-one,gp);

    Real sy = s.dot(gradDiff->dual());
    if (sy > ROL_EPSILON<Real>()*snorm*snorm) {
      if (state_->current < state_->storage-1) {
        state_->current++;                                // Increment Storage
      }
      else {
        state_->iterDiff.erase(state_->iterDiff.begin()); // Remove first element of s list 
        state_->gradDiff.erase(state_->gradDiff.begin()); // Remove first element of y list
        state_->product.erase(state_->product.begin());   // Remove first element of rho list
      }
      state_->iterDiff.push_back(s.clone()); 
      state_->iterDiff[state_->current]->set(s);          // s=x_{k+1}-x_k
      state_->gradDiff.push_back(grad.clone()); 
      state_->gradDiff[state_->current]->set(*gradDiff);  // y=g_{k+1}-g_k
      state_->product.push_back(sy);                      // ys=1/rho  
    }
  }

  // Apply Secant Approximate Inverse Hessian
  virtual void applyH( Vector<Real> &Hv, const Vector<Real> &v ) const = 0;

  // Apply Initial Secant Approximate Inverse Hessian
  virtual void applyH0( Vector<Real> &Hv, const Vector<Real> &v ) const {
    Hv.set(v.dual());
    if (state_->iter != 0 && state_->current != -1) {
      Real yy = state_->gradDiff[state_->current]->dot(*(state_->gradDiff[state_->current]));
      Hv.scale(state_->product[state_->current]/yy);
    }
  }

  // Apply Secant Approximate Hessian
  virtual void applyB( Vector<Real> &Bv, const Vector<Real> &v ) const = 0;

  // Apply Initial Secant Approximate Hessian 
  virtual void applyB0( Vector<Real> &Bv, const Vector<Real> &v ) const {
    Bv.set(v.dual());
    if (state_->iter != 0 && state_->current != -1) {
      Real yy = state_->gradDiff[state_->current]->dot(*(state_->gradDiff[state_->current]));
      Bv.scale(yy/state_->product[state_->current]);
    }
  }

  // Test Secant Approximations 
  void test( const Vector<Real> &x, const Vector<Real> &s ) const {
    Teuchos::RCP<Vector<Real> > vec  = x.clone();
    Teuchos::RCP<Vector<Real> > Hvec = x.clone();
    Teuchos::RCP<Vector<Real> > Bvec = x.clone();
    Real one(1);
  
    // Print BHv -> Should be v
    vec->set(s);
    applyH(*Hvec,*vec);
    applyB(*Bvec,*Hvec);
    vec->axpy(-one,*Bvec);
    std::cout << " ||BHv-v|| = " << vec->norm() << "\n";
  
    // Print HBv -> Should be v
    vec->set(s);
    applyB(*Bvec,*vec);
    applyH(*Hvec,*Bvec);
    vec->axpy(-one,*Hvec);
    std::cout << " ||HBv-v|| = " << vec->norm() << "\n";
  }

  void apply(Vector<Real> &Hv, const Vector<Real> &v, Real &tol) const {
    applyB(Hv,v);
  }

  void applyInverse(Vector<Real> &Hv, const Vector<Real> &v, Real &tol) const {
    applyH(Hv,v);
  }

};

}

#include "ROL_SecantFactory.hpp"

#endif