ROL
ROL_GradientStep.hpp
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43
44#ifndef ROL_GRADIENTSTEP_H
45#define ROL_GRADIENTSTEP_H
46
47#include "ROL_Types.hpp"
48#include "ROL_Step.hpp"
49#include "ROL_Secant.hpp"
50
56
57namespace ROL {
58
59template <class Real>
60class GradientStep : public Step<Real> {
61private:
62
64 const bool computeObj_;
65
66public:
67
68 using Step<Real>::initialize;
69 using Step<Real>::compute;
70 using Step<Real>::update;
71
79 GradientStep( ROL::ParameterList &parlist, const bool computeObj = true )
80 : Step<Real>(), verbosity_(0), computeObj_(computeObj) {
81 // Parse ParameterList
82 verbosity_ = parlist.sublist("General").get("Print Verbosity",0);
83 }
84
85 void compute( Vector<Real> &s, const Vector<Real> &x,
87 AlgorithmState<Real> &algo_state ) {
88 Real one(1);
89 ROL::Ptr<StepState<Real> > step_state = Step<Real>::getState();
90
91 // Compute search direction
92 s.set((step_state->gradientVec)->dual());
93 s.scale(-one);
94 }
95
97 AlgorithmState<Real> &algo_state ) {
98 Real tol = std::sqrt(ROL_EPSILON<Real>());
99 ROL::Ptr<StepState<Real> > step_state = Step<Real>::getState();
100
101 // Update iterate and store step
102 algo_state.iter++;
103 x.plus(s);
104 (step_state->descentVec)->set(s);
105 algo_state.snorm = s.norm();
106
107 // Compute new gradient
108 obj.update(x,true,algo_state.iter);
109 if ( computeObj_ ) {
110 algo_state.value = obj.value(x,tol);
111 algo_state.nfval++;
112 }
113 obj.gradient(*(step_state->gradientVec),x,tol);
114 algo_state.ngrad++;
115
116 // Update algorithm state
117 (algo_state.iterateVec)->set(x);
118 algo_state.gnorm = (step_state->gradientVec)->norm();
119 }
120
121 std::string printHeader( void ) const {
122 std::stringstream hist;
123
124 if( verbosity_>0 ) {
125 hist << std::string(109,'-') << "\n";
127 hist << " status output definitions\n\n";
128 hist << " iter - Number of iterates (steps taken) \n";
129 hist << " value - Objective function value \n";
130 hist << " gnorm - Norm of the gradient\n";
131 hist << " snorm - Norm of the step (update to optimization vector)\n";
132 hist << " #fval - Cumulative number of times the objective function was evaluated\n";
133 hist << " #grad - Number of times the gradient was computed\n";
134 hist << std::string(109,'-') << "\n";
135 }
136
137 hist << " ";
138 hist << std::setw(6) << std::left << "iter";
139 hist << std::setw(15) << std::left << "value";
140 hist << std::setw(15) << std::left << "gnorm";
141 hist << std::setw(15) << std::left << "snorm";
142 hist << std::setw(10) << std::left << "#fval";
143 hist << std::setw(10) << std::left << "#grad";
144 hist << "\n";
145 return hist.str();
146 }
147 std::string printName( void ) const {
148 std::stringstream hist;
149 hist << "\n" << EDescentToString(DESCENT_STEEPEST) << "\n";
150 return hist.str();
151 }
152 std::string print( AlgorithmState<Real> &algo_state, bool print_header = false ) const {
153 std::stringstream hist;
154 hist << std::scientific << std::setprecision(6);
155 if ( algo_state.iter == 0 ) {
156 hist << printName();
157 }
158 if ( print_header ) {
159 hist << printHeader();
160 }
161 if ( algo_state.iter == 0 ) {
162 hist << " ";
163 hist << std::setw(6) << std::left << algo_state.iter;
164 hist << std::setw(15) << std::left << algo_state.value;
165 hist << std::setw(15) << std::left << algo_state.gnorm;
166 hist << "\n";
167 }
168 else {
169 hist << " ";
170 hist << std::setw(6) << std::left << algo_state.iter;
171 hist << std::setw(15) << std::left << algo_state.value;
172 hist << std::setw(15) << std::left << algo_state.gnorm;
173 hist << std::setw(15) << std::left << algo_state.snorm;
174 hist << std::setw(10) << std::left << algo_state.nfval;
175 hist << std::setw(10) << std::left << algo_state.ngrad;
176 hist << "\n";
177 }
178 return hist.str();
179 }
180}; // class GradientStep
181
182} // namespace ROL
183#endif
Contains definitions of custom data types in ROL.
Provides the interface to apply upper and lower bound constraints.
void update(Vector< Real > &x, const Vector< Real > &s, Objective< Real > &obj, BoundConstraint< Real > &con, AlgorithmState< Real > &algo_state)
std::string print(AlgorithmState< Real > &algo_state, bool print_header=false) const
const bool computeObj_
Allows line search to compute objective.
void compute(Vector< Real > &s, const Vector< Real > &x, Objective< Real > &obj, BoundConstraint< Real > &bnd, AlgorithmState< Real > &algo_state)
std::string printName(void) const
Print step name.
GradientStep(ROL::ParameterList &parlist, const bool computeObj=true)
Constructor.
std::string printHeader(void) const
Print iterate header.
int verbosity_
Verbosity setting.
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 update(const Vector< Real > &x, UpdateType type, int iter=-1)
Update objective function.
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.
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 set(const Vector &x)
Set where .
virtual void scale(const Real alpha)=0
Compute where .
virtual void plus(const Vector &x)=0
Compute , where .
Real ROL_EPSILON(void)
Platform-dependent machine epsilon.
Definition ROL_Types.hpp:91
@ DESCENT_STEEPEST
std::string EDescentToString(EDescent tr)
State for algorithm class. Will be used for restarts.
ROL::Ptr< Vector< Real > > iterateVec