This file is indexed.

/usr/include/trilinos/ROL_AugmentedLagrangian.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
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
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
// @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_AUGMENTEDLAGRANGIAN_H
#define ROL_AUGMENTEDLAGRANGIAN_H

#include "ROL_Objective.hpp"
#include "ROL_EqualityConstraint.hpp"
#include "ROL_QuadraticPenalty.hpp"
#include "ROL_Vector.hpp"
#include "ROL_Types.hpp"
#include "Teuchos_RCP.hpp"
#include <iostream>

/** @ingroup func_group
    \class ROL::AugmentedLagrangian
    \brief Provides the interface to evaluate the augmented Lagrangian.

    This class implements the augmented Lagrangian functional for use with
    ROL::AugmentedLagrangianStep.  Given a function
    \f$f:\mathcal{X}\to\mathbb{R}\f$ and an equality constraint
    \f$c:\mathcal{X}\to\mathcal{C}\f$, the augmented Lagrangian functional is
    \f[
       L_A(x,\lambda,\mu) = f(x) +
           \langle \lambda, c(x)\rangle_{\mathcal{C}^*,\mathcal{C}} +
           \frac{\mu}{2} \langle \mathfrak{R}c(x),c(x)\rangle_{\mathcal{C}^*,\mathcal{C}}
    \f]
    where \f$\lambda\in\mathcal{C}^*\f$ denotes the Lagrange multiplier estimate,
    \f$\mu > 0\f$ is the penalty parameter and
    \f$\mathfrak{R}\in\mathcal{L}(\mathcal{C},\mathcal{C}^*)\f$ is the Riesz operator
    on the constraint space.

    This implementation permits the scaling of \f$L_A\f$ by \f$\mu^{-1}\f$ and also
    permits the Hessian approximation
    \f[
        \nabla^2_x L_A(x,\lambda,\mu)v \approx \nabla^2 f(x) v + \mu c'(x)^*\mathfrak{R} c'(x)v.
    \f]

    ---
*/


namespace ROL {

template <class Real>
class AugmentedLagrangian : public Objective<Real> {
private:
  // Required for Augmented Lagrangian definition
  const Teuchos::RCP<Objective<Real> > obj_;
  Teuchos::RCP<QuadraticPenalty<Real> > pen_;
  Real penaltyParameter_;

  // Auxiliary storage
  Teuchos::RCP<Vector<Real> > dualOptVector_;

  // Objective and constraint evaluations
  Real fval_;
  Teuchos::RCP<Vector<Real> > gradient_;

  // Evaluation counters
  int nfval_;
  int ngval_;

  // User defined options
  bool scaleLagrangian_;

  // Flags to recompute quantities
  bool isValueComputed_;
  bool isGradientComputed_;

public:
  /** \brief Constructor.

      This creates a valid AugmentedLagrangian object.
      @param[in]          obj              is an objective function.
      @param[in]          con              is an equality constraint.
      @param[in]          mulitplier       is a Lagrange multiplier vector.
      @param[in]          penaltyParameter is the penalty parameter.
      @param[in]          optVec           is an optimization space vector.
      @param[in]          conVec           is a constraint space vector.
      @param[in]          parlist          is a parameter list.
  */
  AugmentedLagrangian(const Teuchos::RCP<Objective<Real> > &obj,
                      const Teuchos::RCP<EqualityConstraint<Real> > &con,
                      const Vector<Real> &multiplier,
                      const Real penaltyParameter,
                      const Vector<Real> &optVec,
                      const Vector<Real> &conVec,
                      Teuchos::ParameterList &parlist)
    : obj_(obj), penaltyParameter_(penaltyParameter),
      fval_(0), nfval_(0), ngval_(0), isValueComputed_(false), isGradientComputed_(false) {

    gradient_      = optVec.dual().clone();
    dualOptVector_ = optVec.dual().clone();

    Teuchos::ParameterList& sublist = parlist.sublist("Step").sublist("Augmented Lagrangian");
    scaleLagrangian_  = sublist.get("Use Scaled Augmented Lagrangian", false);
    int HessianApprox = sublist.get("Level of Hessian Approximation",  0);

    pen_ = Teuchos::rcp(new QuadraticPenalty<Real>(con,multiplier,penaltyParameter,optVec,conVec,scaleLagrangian_,HessianApprox));
  }

  /** \brief Null constructor.

      This constructor is only used for inheritance and does not create a
      valid AugmentedLagrangian object.  Do not use.
  */
  AugmentedLagrangian()
   : obj_(Teuchos::null), pen_(Teuchos::null), dualOptVector_(Teuchos::null),
     fval_(0), gradient_(Teuchos::null), nfval_(0), ngval_(0),
     scaleLagrangian_(false), isValueComputed_(false), isGradientComputed_(false) {}

  virtual void update( const Vector<Real> &x, bool flag = true, int iter = -1 ) {
    obj_->update(x,flag,iter);
    pen_->update(x,flag,iter);
    isValueComputed_ = (flag ? false : isValueComputed_);
    isGradientComputed_ = (flag ? false : isGradientComputed_);
  }

  virtual Real value( const Vector<Real> &x, Real &tol ) {
    // Compute objective function value
    if ( !isValueComputed_ ) {
      fval_ = obj_->value(x,tol); nfval_++;
      isValueComputed_ = true;
    }
    // Compute penalty term
    Real pval = pen_->value(x,tol);
    // Compute augmented Lagrangian
    Real val = fval_;
    if (scaleLagrangian_) {
      val /= penaltyParameter_;
    }
    return val + pval;
  }

  virtual void gradient( Vector<Real> &g, const Vector<Real> &x, Real &tol ) {
    // Compute objective function gradient
    if ( !isGradientComputed_ ) {
      obj_->gradient(*gradient_,x,tol); ngval_++;
      isGradientComputed_ = true;
    }
    g.set(*gradient_);
    // Compute gradient of penalty
    pen_->gradient(*dualOptVector_,x,tol);
    // Compute gradient of Augmented Lagrangian
    if ( scaleLagrangian_ ) {
      g.scale(static_cast<Real>(1)/penaltyParameter_);
    }
    g.plus(*dualOptVector_);
  }

  virtual void hessVec( Vector<Real> &hv, const Vector<Real> &v, const Vector<Real> &x, Real &tol ) {
    // Apply objective Hessian to a vector
    obj_->hessVec(hv,v,x,tol);
    // Apply penalty Hessian to a vector
    pen_->hessVec(*dualOptVector_,v,x,tol);
    // Build hessVec of Augmented Lagrangian
    if ( scaleLagrangian_ ) {
      hv.scale(static_cast<Real>(1)/penaltyParameter_);
    }
    hv.plus(*dualOptVector_);
  }

  // Return objective function value
  virtual Real getObjectiveValue(const Vector<Real> &x) {
    Real tol = std::sqrt(ROL_EPSILON<Real>());
    // Evaluate objective function value
    if ( !isValueComputed_ ) {
      fval_ = obj_->value(x,tol); nfval_++;
      isValueComputed_ = true;
    }
    return fval_;
  }

  // Return constraint value
  virtual void getConstraintVec(Vector<Real> &c, const Vector<Real> &x) {
    pen_->getConstraintVec(c,x);
  }

  // Return total number of constraint evaluations
  virtual int getNumberConstraintEvaluations(void) const {
    return pen_->getNumberConstraintEvaluations();
  }

  // Return total number of objective evaluations
  virtual int getNumberFunctionEvaluations(void) const {
    return nfval_;
  }

  // Return total number of gradient evaluations
  virtual int getNumberGradientEvaluations(void) const {
    return ngval_;
  }

  // Reset with upated penalty parameter
  virtual void reset(const Vector<Real> &multiplier, const Real penaltyParameter) {
    nfval_ = 0; ngval_ = 0;
    pen_->reset(multiplier,penaltyParameter);
  }
}; // class AugmentedLagrangian

} // namespace ROL

#endif