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 
57 namespace ROL {
58 
59 template <class Real>
60 class GradientStep : public Step<Real> {
61 private:
62 
63  int verbosity_;
64  const bool computeObj_;
65 
66 public:
67 
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.
Provides the interface to compute optimization steps with the gradient descent method globalized usin...
void update(Vector< Real > &x, const Vector< Real > &s, Objective< Real > &obj, BoundConstraint< Real > &con, AlgorithmState< Real > &algo_state)
Update step, if successful.
std::string print(AlgorithmState< Real > &algo_state, bool print_header=false) const
Print iterate status.
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)
Compute step.
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.
Provides the interface to compute optimization steps.
Definition: ROL_Step.hpp:68
ROL::Ptr< StepState< Real > > getState(void)
Definition: ROL_Step.hpp:73
Defines the linear algebra or vector space interface.
Definition: ROL_Vector.hpp:84
virtual Real norm() const =0
Returns where .
virtual void set(const Vector &x)
Set where .
Definition: ROL_Vector.hpp:209
virtual void scale(const Real alpha)=0
Compute where .
virtual void plus(const Vector &x)=0
Compute , where .
@ DESCENT_STEEPEST
Definition: ROL_Types.hpp:410
std::string EDescentToString(EDescent tr)
Definition: ROL_Types.hpp:418
State for algorithm class. Will be used for restarts.
Definition: ROL_Types.hpp:143
ROL::Ptr< Vector< Real > > iterateVec
Definition: ROL_Types.hpp:157