10 #ifndef ROL_GRADIENTSTEP_H
11 #define ROL_GRADIENTSTEP_H
45 GradientStep( ROL::ParameterList &parlist,
const bool computeObj =
true )
48 verbosity_ = parlist.sublist(
"General").get(
"Print Verbosity",0);
58 s.
set((step_state->gradientVec)->dual());
64 Real tol = std::sqrt(ROL_EPSILON<Real>());
70 (step_state->descentVec)->set(s);
79 obj.
gradient(*(step_state->gradientVec),x,tol);
84 algo_state.
gnorm = (step_state->gradientVec)->norm();
88 std::stringstream hist;
91 hist << std::string(109,
'-') <<
"\n";
93 hist <<
" status output definitions\n\n";
94 hist <<
" iter - Number of iterates (steps taken) \n";
95 hist <<
" value - Objective function value \n";
96 hist <<
" gnorm - Norm of the gradient\n";
97 hist <<
" snorm - Norm of the step (update to optimization vector)\n";
98 hist <<
" #fval - Cumulative number of times the objective function was evaluated\n";
99 hist <<
" #grad - Number of times the gradient was computed\n";
100 hist << std::string(109,
'-') <<
"\n";
104 hist << std::setw(6) << std::left <<
"iter";
105 hist << std::setw(15) << std::left <<
"value";
106 hist << std::setw(15) << std::left <<
"gnorm";
107 hist << std::setw(15) << std::left <<
"snorm";
108 hist << std::setw(10) << std::left <<
"#fval";
109 hist << std::setw(10) << std::left <<
"#grad";
114 std::stringstream hist;
119 std::stringstream hist;
120 hist << std::scientific << std::setprecision(6);
121 if ( algo_state.
iter == 0 ) {
124 if ( print_header ) {
127 if ( algo_state.
iter == 0 ) {
129 hist << std::setw(6) << std::left << algo_state.
iter;
130 hist << std::setw(15) << std::left << algo_state.
value;
131 hist << std::setw(15) << std::left << algo_state.
gnorm;
136 hist << std::setw(6) << std::left << algo_state.
iter;
137 hist << std::setw(15) << std::left << algo_state.
value;
138 hist << std::setw(15) << std::left << algo_state.
gnorm;
139 hist << std::setw(15) << std::left << algo_state.
snorm;
140 hist << std::setw(10) << std::left << algo_state.
nfval;
141 hist << std::setw(10) << std::left << algo_state.
ngrad;
Provides the interface to evaluate objective functions.
virtual void scale(const Real alpha)=0
Compute where .
virtual void plus(const Vector &x)=0
Compute , where .
virtual Real value(const Vector< Real > &x, Real &tol)=0
Compute value.
Provides the interface to compute optimization steps.
std::string print(AlgorithmState< Real > &algo_state, bool print_header=false) const
Print iterate status.
Contains definitions of custom data types in ROL.
void compute(Vector< Real > &s, const Vector< Real > &x, Objective< Real > &obj, BoundConstraint< Real > &bnd, AlgorithmState< Real > &algo_state)
Compute step.
std::string EDescentToString(EDescent tr)
GradientStep(ROL::ParameterList &parlist, const bool computeObj=true)
Constructor.
Defines the linear algebra or vector space interface.
virtual void update(const Vector< Real > &x, UpdateType type, int iter=-1)
Update objective function.
State for algorithm class. Will be used for restarts.
virtual void gradient(Vector< Real > &g, const Vector< Real > &x, Real &tol)
Compute gradient.
std::string printHeader(void) const
Print iterate header.
ROL::Ptr< StepState< Real > > getState(void)
ROL::Ptr< Vector< Real > > iterateVec
int verbosity_
Verbosity setting.
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)
Update step, if successful.
virtual void set(const Vector &x)
Set where .
virtual Real norm() const =0
Returns where .
Provides the interface to compute optimization steps with the gradient descent method globalized usin...
std::string printName(void) const
Print step name.
const bool computeObj_
Allows line search to compute objective.