This file is indexed.

/usr/include/trilinos/ROL_TrustRegion.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
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
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
// @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_TRUSTREGION_H
#define ROL_TRUSTREGION_H

/** \class ROL::TrustRegion
    \brief Provides interface for and implements trust-region subproblem solvers.
*/

#include "ROL_Types.hpp"
#include "ROL_TrustRegionTypes.hpp"
#include "ROL_TrustRegionModel.hpp"
#include "ROL_ColemanLiModel.hpp"
#include "ROL_KelleySachsModel.hpp"

namespace ROL {

template<class Real>
class TrustRegion {
private:

  Teuchos::RCP<Vector<Real> > prim_, dual_;

  ETrustRegionModel TRmodel_;

  Real delmax_;
  Real eta0_, eta1_, eta2_;
  Real gamma0_, gamma1_, gamma2_;
  Real pRed_;
  Real TRsafe_, eps_;
  Real mu0_;

  std::vector<bool> useInexact_;

  Real ftol_old_;

  Real scale_, omega_, force_;
  int updateIter_, forceFactor_, cnt_;

  unsigned verbosity_;

public:

  virtual ~TrustRegion() {}

  // Constructor
  TrustRegion( Teuchos::ParameterList &parlist )
    : ftol_old_(ROL_OVERFLOW<Real>()), cnt_(0), verbosity_(0) {
    // Trust-Region Parameters
    Teuchos::ParameterList list = parlist.sublist("Step").sublist("Trust Region");
    TRmodel_ = StringToETrustRegionModel(list.get("Subproblem Model", "Kelley-Sachs"));
    delmax_  = list.get("Maximum Radius",                       static_cast<Real>(5000.0));
    eta0_    = list.get("Step Acceptance Threshold",            static_cast<Real>(0.05));
    eta1_    = list.get("Radius Shrinking Threshold",           static_cast<Real>(0.05));
    eta2_    = list.get("Radius Growing Threshold",             static_cast<Real>(0.9));
    gamma0_  = list.get("Radius Shrinking Rate (Negative rho)", static_cast<Real>(0.0625));
    gamma1_  = list.get("Radius Shrinking Rate (Positive rho)", static_cast<Real>(0.25));
    gamma2_  = list.get("Radius Growing Rate",                  static_cast<Real>(2.5));
    mu0_     = list.get("Sufficient Decrease Parameter",        static_cast<Real>(1.e-4));
    TRsafe_  = list.get("Safeguard Size",                       static_cast<Real>(100.0));
    eps_     = TRsafe_*ROL_EPSILON<Real>();
    // General Inexactness Information
    Teuchos::ParameterList &glist = parlist.sublist("General");
    useInexact_.clear();
    useInexact_.push_back(glist.get("Inexact Objective Function",     false));
    useInexact_.push_back(glist.get("Inexact Gradient",               false));
    useInexact_.push_back(glist.get("Inexact Hessian-Times-A-Vector", false));
    // Inexact Function Evaluation Information
    Teuchos::ParameterList &ilist = list.sublist("Inexact").sublist("Value");
    scale_       = ilist.get("Tolerance Scaling",                 static_cast<Real>(1.e-1));
    omega_       = ilist.get("Exponent",                          static_cast<Real>(0.9));
    force_       = ilist.get("Forcing Sequence Initial Value",    static_cast<Real>(1.0));
    updateIter_  = ilist.get("Forcing Sequence Update Frequency", static_cast<int>(10));
    forceFactor_ = ilist.get("Forcing Sequence Reduction Factor", static_cast<Real>(0.1));
    // Get verbosity level
    verbosity_ = glist.get("Print Verbosity", 0);
  }

  virtual void initialize( const Vector<Real> &x, const Vector<Real> &s, const Vector<Real> &g) {
    prim_ = x.clone();
    dual_ = g.clone();
  }

  virtual void update( Vector<Real>           &x,
                       Real                   &fnew,
                       Real                   &del,
                       int                    &nfval,
                       int                    &ngrad,
                       ETrustRegionFlag       &flagTR,
                 const Vector<Real>           &s,
                 const Real                   snorm,
                 const Real                   fold,
                 const Vector<Real>           &g,
                       int                    iter,
                       Objective<Real>        &obj,
                       BoundConstraint<Real>  &bnd,
                       TrustRegionModel<Real> &model ) {
    Real tol = std::sqrt(ROL_EPSILON<Real>());
    const Real one(1), oe4(1.e4), zero(0);

    /***************************************************************************************************/
    // BEGIN INEXACT OBJECTIVE FUNCTION COMPUTATION
    /***************************************************************************************************/
    // Update inexact objective function
    Real fold1 = fold, ftol = tol, TOL(1.e-2);
    if ( useInexact_[0] ) {
      if ( !(cnt_%updateIter_) && (cnt_ != 0) ) {
        force_ *= forceFactor_;
      }
      Real c = scale_*std::max(TOL,std::min(one,oe4*std::max(pRed_,std::sqrt(ROL_EPSILON<Real>()))));
      ftol   = c*std::pow(std::min(eta1_,one-eta2_)
                *std::min(std::max(pRed_,std::sqrt(ROL_EPSILON<Real>())),force_),one/omega_);
      if ( ftol_old_ > ftol || cnt_ == 0 ) {
        ftol_old_ = ftol;
        fold1 = obj.value(x,ftol_old_);
      }
      cnt_++;
    }
    // Evaluate objective function at new iterate
    prim_->set(x); prim_->plus(s);
    obj.update(*prim_);
    fnew = obj.value(*prim_,ftol);

    nfval = 1;
    Real aRed = fold1 - fnew;
    /***************************************************************************************************/
    // FINISH INEXACT OBJECTIVE FUNCTION COMPUTATION
    /***************************************************************************************************/

    /***************************************************************************************************/
    // BEGIN COMPUTE RATIO OF ACTUAL AND PREDICTED REDUCTION
    /***************************************************************************************************/
    // Modify Actual and Predicted Reduction According to Model
    model.updateActualReduction(aRed,s);
    model.updatePredictedReduction(pRed_,s);

    if ( verbosity_ > 0 ) {
      std::cout << std::endl;
      std::cout << "  Computation of actual and predicted reduction" << std::endl;
      std::cout << "    Current objective function value:        "   << fold1 << std::endl;
      std::cout << "    New objective function value:            "   << fnew  << std::endl;
      std::cout << "    Actual reduction:                        "   << aRed  << std::endl;
      std::cout << "    Predicted reduction:                     "   << pRed_ << std::endl;
    }

    // Compute Ratio of Actual and Predicted Reduction
    Real EPS = eps_*((one > std::abs(fold1)) ? one : std::abs(fold1));
    Real aRed_safe = aRed + EPS, pRed_safe = pRed_ + EPS;
    Real rho(0);
    if (((std::abs(aRed_safe) < eps_) && (std::abs(pRed_safe) < eps_)) || aRed == pRed_) {
      rho = one;
      flagTR = TRUSTREGION_FLAG_SUCCESS;
    }
    else if ( std::isnan(aRed_safe) || std::isnan(pRed_safe) ) {
      rho = -one;
      flagTR = TRUSTREGION_FLAG_NAN;
    }
    else {
      rho = aRed_safe/pRed_safe;
      if (pRed_safe < zero && aRed_safe > zero) {
        flagTR = TRUSTREGION_FLAG_POSPREDNEG;
      }
      else if (aRed_safe <= zero && pRed_safe > zero) {
        flagTR = TRUSTREGION_FLAG_NPOSPREDPOS;
      }
      else if (aRed_safe <= zero && pRed_safe < zero) {
        flagTR = TRUSTREGION_FLAG_NPOSPREDNEG;
      }
      else {
        flagTR = TRUSTREGION_FLAG_SUCCESS;
      }
    }

    if ( verbosity_ > 0 ) {
      std::cout << "    Safeguard:                               " << eps_      << std::endl;
      std::cout << "    Actual reduction with safeguard:         " << aRed_safe << std::endl;
      std::cout << "    Predicted reduction with safeguard:      " << pRed_safe << std::endl;
      std::cout << "    Ratio of actual and predicted reduction: " << rho       << std::endl;
      std::cout << "    Trust-region flag:                       " << flagTR    << std::endl;
    }
    /***************************************************************************************************/
    // FINISH COMPUTE RATIO OF ACTUAL AND PREDICTED REDUCTION
    /***************************************************************************************************/


    /***************************************************************************************************/
    // BEGIN CHECK SUFFICIENT DECREASE FOR BOUND CONSTRAINED PROBLEMS
    /***************************************************************************************************/
    bool decr = true;
    if ( bnd.isActivated() && TRmodel_ == TRUSTREGION_MODEL_KELLEYSACHS ) {
      if ( rho >= eta0_ && (std::abs(aRed_safe) > eps_) ) {
        // Compute Criticality Measure || x - P( x - g ) ||
        prim_->set(x);
        prim_->axpy(-one,g.dual());
        bnd.project(*prim_);
        prim_->scale(-one);
        prim_->plus(x);
        Real pgnorm = prim_->norm();
        // Compute Scaled Measure || x - P( x - lam * PI(g) ) ||
        prim_->set(g.dual());
        bnd.pruneActive(*prim_,g,x);
        Real lam = std::min(one, del/prim_->norm());
        prim_->scale(-lam);
        prim_->plus(x);
        bnd.project(*prim_);
        prim_->scale(-one);
        prim_->plus(x);
        pgnorm *= prim_->norm();
        // Sufficient decrease?
        decr = ( aRed_safe >= mu0_*eta0_*pgnorm );
        flagTR = (!decr ? TRUSTREGION_FLAG_QMINSUFDEC : flagTR);

        if ( verbosity_ > 0 ) {
          std::cout << "    Decrease lower bound (constraints):      " << 0.1*eta0_*pgnorm  << std::endl;
          std::cout << "    Trust-region flag (constraints):         " << flagTR            << std::endl;
          std::cout << "    Is step feasible:                        " << bnd.isFeasible(x) << std::endl;
        }
      }
    }
    /***************************************************************************************************/
    // FINISH CHECK SUFFICIENT DECREASE FOR BOUND CONSTRAINED PROBLEMS
    /***************************************************************************************************/

    /***************************************************************************************************/
    // BEGIN STEP ACCEPTANCE AND TRUST REGION RADIUS UPDATE
    /***************************************************************************************************/
    if ( verbosity_ > 0 ) {
      std::cout << "    Norm of step:                            " << snorm << std::endl;
      std::cout << "    Trust-region radius before update:       " << del   << std::endl;
    }
    if ((rho < eta0_ && flagTR == TRUSTREGION_FLAG_SUCCESS) || flagTR >= 2 || !decr ) { // Step Rejected
      fnew = fold1;
      if (rho < zero) { // Negative reduction, interpolate to find new trust-region radius
        Real gs(0);
        if ( bnd.isActivated() ) {
          model.dualTransform(*dual_, *model.getGradient());
          gs = dual_->dot(s.dual());
        }
        else {
          gs = g.dot(s.dual());
        }
        Real modelVal = model.value(s,tol);
        modelVal += fold1;
        Real theta = (one-eta2_)*gs/((one-eta2_)*(fold1+gs)+eta2_*modelVal-fnew);
        del = std::min(gamma1_*std::min(snorm,del),std::max(gamma0_,theta)*del);
      }
      else { // Shrink trust-region radius
        del = gamma1_*std::min(snorm,del);
      }
      obj.update(x,true,iter);
    }
    else if ((rho >= eta0_ && flagTR != TRUSTREGION_FLAG_NPOSPREDNEG) ||
             (flagTR == TRUSTREGION_FLAG_POSPREDNEG)) { // Step Accepted
      x.plus(s);
      obj.update(x,true,iter);
      if (rho >= eta2_) { // Increase trust-region radius
        del = std::min(gamma2_*del,delmax_);
      }
    }
    if ( verbosity_ > 0 ) {
      std::cout << "    Trust-region radius after update:        " << del << std::endl;
      std::cout << std::endl;
    }
    /***************************************************************************************************/
    // FINISH STEP ACCEPTANCE AND TRUST REGION RADIUS UPDATE
    /***************************************************************************************************/
  }

  virtual void run( Vector<Real>           &s,           // Step (to be computed)
                    Real                   &snorm,       // Step norm (to be computed)
                    int                    &iflag,       // Exit flag (to be computed)
                    int                    &iter,        // Iteration count (to be computed)
                    const Real              del,         // Trust-region radius
                    TrustRegionModel<Real> &model ) = 0; // Trust-region model

  void setPredictedReduction(const Real pRed) {
    pRed_ = pRed;
  }

  Real getPredictedReduction(void) const {
    return pRed_;
  }
};

}

#include "ROL_TrustRegionFactory.hpp"

#endif