/usr/include/trilinos/ROL_NonlinearCGStep.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 | // @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_NONLINEARCGSTEP_H
#define ROL_NONLINEARCGSTEP_H
#include "ROL_Types.hpp"
#include "ROL_Step.hpp"
#include "ROL_NonlinearCG.hpp"
/** @ingroup step_group
\class ROL::NonlinearCGStep
\brief Provides the interface to compute optimization steps
with nonlinear CG.
*/
namespace ROL {
template <class Real>
class NonlinearCGStep : public Step<Real> {
private:
Teuchos::RCP<NonlinearCG<Real> > nlcg_; ///< NonlinearCG object (used for quasi-Newton)
ENonlinearCG enlcg_;
int verbosity_; ///< Verbosity setting
const bool computeObj_;
public:
using Step<Real>::initialize;
using Step<Real>::compute;
using Step<Real>::update;
/** \brief Constructor.
Constructor to build a NonlinearCGStep object with a user-defined
nonlinear CG object. Algorithmic specifications are passed in through
a Teuchos::ParameterList.
@param[in] parlist is a parameter list containing algorithmic specifications
@param[in] nlcg is a user-defined NonlinearCG object
*/
NonlinearCGStep( Teuchos::ParameterList &parlist,
const Teuchos::RCP<NonlinearCG<Real> > &nlcg = Teuchos::null,
const bool computeObj = true )
: Step<Real>(), nlcg_(nlcg), enlcg_(NONLINEARCG_USERDEFINED),
verbosity_(0), computeObj_(computeObj) {
// Parse ParameterList
verbosity_ = parlist.sublist("General").get("Print Verbosity",0);
// Initialize secant object
Teuchos::ParameterList& Llist = parlist.sublist("Step").sublist("Line Search");
if ( nlcg == Teuchos::null ) {
enlcg_
= StringToENonlinearCG(Llist.sublist("Descent Method").get("Nonlinear CG Type","Oren-Luenberger"));
nlcg_ = Teuchos::rcp(new NonlinearCG<Real>(enlcg_));
}
}
void compute( Vector<Real> &s, const Vector<Real> &x,
Objective<Real> &obj, BoundConstraint<Real> &bnd,
AlgorithmState<Real> &algo_state ) {
Teuchos::RCP<StepState<Real> > step_state = Step<Real>::getState();
Real one(1);
// Compute search direction
nlcg_->run(s,*(step_state->gradientVec),x,obj);
s.scale(-one);
}
void update( Vector<Real> &x, const Vector<Real> &s, Objective<Real> &obj, BoundConstraint<Real> &con,
AlgorithmState<Real> &algo_state ) {
Real tol = std::sqrt(ROL_EPSILON<Real>());
Teuchos::RCP<StepState<Real> > step_state = Step<Real>::getState();
// Update iterate
algo_state.iter++;
x.plus(s);
(step_state->descentVec)->set(s);
algo_state.snorm = s.norm();
// Compute new gradient
obj.update(x,true,algo_state.iter);
if ( computeObj_ ) {
algo_state.value = obj.value(x,tol);
algo_state.nfval++;
}
obj.gradient(*(step_state->gradientVec),x,tol);
algo_state.ngrad++;
// Update algorithm state
(algo_state.iterateVec)->set(x);
algo_state.gnorm = (step_state->gradientVec)->norm();
}
std::string printHeader( void ) const {
std::stringstream hist;
if( verbosity_>0 ) {
hist << std::string(109,'-') << "\n";
hist << EDescentToString(DESCENT_NONLINEARCG);
hist << " status output definitions\n\n";
hist << " iter - Number of iterates (steps taken) \n";
hist << " value - Objective function value \n";
hist << " gnorm - Norm of the gradient\n";
hist << " snorm - Norm of the step (update to optimization vector)\n";
hist << " #fval - Cumulative number of times the objective function was evaluated\n";
hist << " #grad - Number of times the gradient was computed\n";
hist << std::string(109,'-') << "\n";
}
hist << " ";
hist << std::setw(6) << std::left << "iter";
hist << std::setw(15) << std::left << "value";
hist << std::setw(15) << std::left << "gnorm";
hist << std::setw(15) << std::left << "snorm";
hist << std::setw(10) << std::left << "#fval";
hist << std::setw(10) << std::left << "#grad";
hist << "\n";
return hist.str();
}
std::string printName( void ) const {
std::stringstream hist;
hist << "\n" << ENonlinearCGToString(enlcg_) << " "
<< EDescentToString(DESCENT_NONLINEARCG) << "\n";
return hist.str();
}
std::string print( AlgorithmState<Real> &algo_state, bool print_header = false ) const {
std::stringstream hist;
hist << std::scientific << std::setprecision(6);
if ( algo_state.iter == 0 ) {
hist << printName();
}
if ( print_header ) {
hist << printHeader();
}
if ( algo_state.iter == 0 ) {
hist << " ";
hist << std::setw(6) << std::left << algo_state.iter;
hist << std::setw(15) << std::left << algo_state.value;
hist << std::setw(15) << std::left << algo_state.gnorm;
hist << "\n";
}
else {
hist << " ";
hist << std::setw(6) << std::left << algo_state.iter;
hist << std::setw(15) << std::left << algo_state.value;
hist << std::setw(15) << std::left << algo_state.gnorm;
hist << std::setw(15) << std::left << algo_state.snorm;
hist << std::setw(10) << std::left << algo_state.nfval;
hist << std::setw(10) << std::left << algo_state.ngrad;
hist << "\n";
}
return hist.str();
}
}; // class NonlinearCGStep
} // namespace ROL
#endif
|