ROL
ROL_ProjectedNewtonStep.hpp
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43
44#ifndef ROL_PROJECTEDNEWTONSTEP_H
45#define ROL_PROJECTEDNEWTONSTEP_H
46
47#include "ROL_Types.hpp"
48#include "ROL_Step.hpp"
49
56namespace ROL {
57
58template <class Real>
59class ProjectedNewtonStep : public Step<Real> {
60private:
61
62 ROL::Ptr<Vector<Real> > gp_;
63 ROL::Ptr<Vector<Real> > d_;
65 const bool computeObj_;
67
68public:
69
70 using Step<Real>::initialize;
71 using Step<Real>::compute;
72 using Step<Real>::update;
73
81 ProjectedNewtonStep( ROL::ParameterList &parlist, const bool computeObj = true )
82 : Step<Real>(), gp_(ROL::nullPtr), d_(ROL::nullPtr),
83 verbosity_(0), computeObj_(computeObj), useProjectedGrad_(false) {
84 // Parse ParameterList
85 ROL::ParameterList& Glist = parlist.sublist("General");
86 useProjectedGrad_ = Glist.get("Projected Gradient Criticality Measure", false);
87 verbosity_ = parlist.sublist("General").get("Print Verbosity",0);
88 }
89
90 void initialize( Vector<Real> &x, const Vector<Real> &s, const Vector<Real> &g,
92 AlgorithmState<Real> &algo_state ) {
93 Step<Real>::initialize(x,s,g,obj,bnd,algo_state);
94 gp_ = g.clone();
95 d_ = s.clone();
96 }
97
98 void compute( Vector<Real> &s, const Vector<Real> &x,
100 AlgorithmState<Real> &algo_state ) {
101 Real tol = std::sqrt(ROL_EPSILON<Real>()), one(1);
102 ROL::Ptr<StepState<Real> > step_state = Step<Real>::getState();
103
104 // Compute projected Newton step
105 // ---> Apply inactive-inactive block of inverse hessian to gradient
106 gp_->set(*(step_state->gradientVec));
107 bnd.pruneActive(*gp_,*(step_state->gradientVec),x,algo_state.gnorm);
108 obj.invHessVec(s,*gp_,x,tol);
109 bnd.pruneActive(s,*(step_state->gradientVec),x,algo_state.gnorm);
110 // ---> Add in active gradient components
111 gp_->set(*(step_state->gradientVec));
112 bnd.pruneInactive(*gp_,*(step_state->gradientVec),x,algo_state.gnorm);
113 s.plus(gp_->dual());
114 s.scale(-one);
115 }
116
117 void update( Vector<Real> &x, const Vector<Real> &s,
119 AlgorithmState<Real> &algo_state ) {
120 Real tol = std::sqrt(ROL_EPSILON<Real>()), one(1);
121 ROL::Ptr<StepState<Real> > step_state = Step<Real>::getState();
122
123 // Update iterate and store previous step
124 algo_state.iter++;
125 d_->set(x);
126 x.plus(s);
127 bnd.project(x);
128 (step_state->descentVec)->set(x);
129 (step_state->descentVec)->axpy(-one,*d_);
130 algo_state.snorm = s.norm();
131
132 // Compute new gradient
133 obj.update(x,true,algo_state.iter);
134 if ( computeObj_ ) {
135 algo_state.value = obj.value(x,tol);
136 algo_state.nfval++;
137 }
138 obj.gradient(*(step_state->gradientVec),x,tol);
139 algo_state.ngrad++;
140
141 // Update algorithm state
142 (algo_state.iterateVec)->set(x);
143 if ( useProjectedGrad_ ) {
144 gp_->set(*(step_state->gradientVec));
145 bnd.computeProjectedGradient( *gp_, x );
146 algo_state.gnorm = gp_->norm();
147 }
148 else {
149 d_->set(x);
150 d_->axpy(-one,(step_state->gradientVec)->dual());
151 bnd.project(*d_);
152 d_->axpy(-one,x);
153 algo_state.gnorm = d_->norm();
154 }
155 }
156
157 std::string printHeader( void ) const {
158 std::stringstream hist;
159
160 if( verbosity_>0 ) {
161 hist << std::string(109,'-') << "\n";
163 hist << " status output definitions\n\n";
164 hist << " iter - Number of iterates (steps taken) \n";
165 hist << " value - Objective function value \n";
166 hist << " gnorm - Norm of the gradient\n";
167 hist << " snorm - Norm of the step (update to optimization vector)\n";
168 hist << " #fval - Cumulative number of times the objective function was evaluated\n";
169 hist << " #grad - Number of times the gradient was computed\n";
170 hist << std::string(109,'-') << "\n";
171 }
172
173 hist << " ";
174 hist << std::setw(6) << std::left << "iter";
175 hist << std::setw(15) << std::left << "value";
176 hist << std::setw(15) << std::left << "gnorm";
177 hist << std::setw(15) << std::left << "snorm";
178 hist << std::setw(10) << std::left << "#fval";
179 hist << std::setw(10) << std::left << "#grad";
180 hist << "\n";
181 return hist.str();
182 }
183 std::string printName( void ) const {
184 std::stringstream hist;
185 hist << "\n" << EDescentToString(DESCENT_NEWTON) << "\n";
186 return hist.str();
187 }
188 std::string print( AlgorithmState<Real> &algo_state, bool print_header = false ) const {
189 std::stringstream hist;
190 hist << std::scientific << std::setprecision(6);
191 if ( algo_state.iter == 0 ) {
192 hist << printName();
193 }
194 if ( print_header ) {
195 hist << printHeader();
196 }
197 if ( algo_state.iter == 0 ) {
198 hist << " ";
199 hist << std::setw(6) << std::left << algo_state.iter;
200 hist << std::setw(15) << std::left << algo_state.value;
201 hist << std::setw(15) << std::left << algo_state.gnorm;
202 hist << "\n";
203 }
204 else {
205 hist << " ";
206 hist << std::setw(6) << std::left << algo_state.iter;
207 hist << std::setw(15) << std::left << algo_state.value;
208 hist << std::setw(15) << std::left << algo_state.gnorm;
209 hist << std::setw(15) << std::left << algo_state.snorm;
210 hist << std::setw(10) << std::left << algo_state.nfval;
211 hist << std::setw(10) << std::left << algo_state.ngrad;
212 hist << "\n";
213 }
214 return hist.str();
215 }
216}; // class ProjectedNewtonStep
217
218} // namespace ROL
219
220#endif
Contains definitions of custom data types in ROL.
Provides the interface to apply upper and lower bound constraints.
void pruneInactive(Vector< Real > &v, const Vector< Real > &x, Real eps=Real(0))
Set variables to zero if they correspond to the -inactive set.
void pruneActive(Vector< Real > &v, const Vector< Real > &x, Real eps=Real(0))
Set variables to zero if they correspond to the -active set.
void computeProjectedGradient(Vector< Real > &g, const Vector< Real > &x)
Compute projected gradient.
virtual void project(Vector< Real > &x)
Project optimization variables onto the bounds.
Provides the interface to evaluate objective functions.
virtual void gradient(Vector< Real > &g, const Vector< Real > &x, Real &tol)
Compute gradient.
virtual Real value(const Vector< Real > &x, Real &tol)=0
Compute value.
virtual void invHessVec(Vector< Real > &hv, const Vector< Real > &v, const Vector< Real > &x, Real &tol)
Apply inverse Hessian approximation to vector.
virtual void update(const Vector< Real > &x, UpdateType type, int iter=-1)
Update objective function.
Provides the interface to compute optimization steps with projected Newton's method using line search...
ROL::Ptr< Vector< Real > > gp_
Additional vector storage.
std::string printHeader(void) const
Print iterate header.
void update(Vector< Real > &x, const Vector< Real > &s, Objective< Real > &obj, BoundConstraint< Real > &bnd, AlgorithmState< Real > &algo_state)
Update step, if successful.
std::string print(AlgorithmState< Real > &algo_state, bool print_header=false) const
Print iterate status.
ROL::Ptr< Vector< Real > > d_
Additional vector storage.
ProjectedNewtonStep(ROL::ParameterList &parlist, const bool computeObj=true)
Constructor.
void compute(Vector< Real > &s, const Vector< Real > &x, Objective< Real > &obj, BoundConstraint< Real > &bnd, AlgorithmState< Real > &algo_state)
Compute step.
void initialize(Vector< Real > &x, const Vector< Real > &s, const Vector< Real > &g, Objective< Real > &obj, BoundConstraint< Real > &bnd, AlgorithmState< Real > &algo_state)
Initialize step with bound constraint.
std::string printName(void) const
Print step name.
bool useProjectedGrad_
Whether or not to use to the projected gradient criticality measure.
Provides the interface to compute optimization steps.
Definition ROL_Step.hpp:68
virtual void initialize(Vector< Real > &x, const Vector< Real > &g, Objective< Real > &obj, BoundConstraint< Real > &con, AlgorithmState< Real > &algo_state)
Initialize step with bound constraint.
Definition ROL_Step.hpp:88
ROL::Ptr< StepState< Real > > getState(void)
Definition ROL_Step.hpp:73
Defines the linear algebra or vector space interface.
virtual Real norm() const =0
Returns where .
virtual void scale(const Real alpha)=0
Compute where .
virtual void plus(const Vector &x)=0
Compute , where .
virtual ROL::Ptr< Vector > clone() const =0
Clone to make a new (uninitialized) vector.
@ DESCENT_NEWTON
std::string EDescentToString(EDescent tr)
State for algorithm class. Will be used for restarts.
ROL::Ptr< Vector< Real > > iterateVec