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