44 #ifndef ROL_NEWTONSTEP_H 45 #define ROL_NEWTONSTEP_H 78 NewtonStep( ROL::ParameterList &parlist,
const bool computeObj =
true )
81 verbosity_ = parlist.sublist(
"General").get(
"Print Verbosity",0);
88 Real tol = std::sqrt(ROL_EPSILON<Real>()), one(1);
91 obj.
invHessVec(s,*(step_state->gradientVec),x,tol);
97 Real tol = std::sqrt(ROL_EPSILON<Real>());
103 (step_state->descentVec)->
set(s);
112 obj.
gradient(*(step_state->gradientVec),x,tol);
117 algo_state.
gnorm = (step_state->gradientVec)->norm();
121 std::stringstream hist;
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";
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";
147 std::stringstream hist;
152 std::stringstream hist;
153 hist << std::scientific << std::setprecision(6);
154 if ( algo_state.
iter == 0 ) {
157 if ( print_header ) {
160 if ( algo_state.
iter == 0 ) {
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;
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;
Provides the interface to evaluate objective functions.
NewtonStep(ROL::ParameterList &parlist, const bool computeObj=true)
Constructor.
virtual void scale(const Real alpha)=0
Compute where .
virtual void plus(const Vector &x)=0
Compute , where .
std::string print(AlgorithmState< Real > &algo_state, bool print_header=false) const
Print iterate status.
virtual Real value(const Vector< Real > &x, Real &tol)=0
Compute value.
Provides the interface to compute optimization steps.
Contains definitions of custom data types in ROL.
std::string EDescentToString(EDescent tr)
Defines the linear algebra or vector space interface.
virtual void update(const Vector< Real > &x, UpdateType type, int iter=-1)
Update objective function.
Provides the interface to compute optimization steps with Newton's method globalized using line searc...
State for algorithm class. Will be used for restarts.
std::string printName(void) const
Print step name.
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)
void compute(Vector< Real > &s, const Vector< Real > &x, Objective< Real > &obj, BoundConstraint< Real > &bnd, AlgorithmState< Real > &algo_state)
Compute step.
ROL::Ptr< Vector< Real > > iterateVec
virtual void invHessVec(Vector< Real > &hv, const Vector< Real > &v, const Vector< Real > &x, Real &tol)
Apply inverse Hessian approximation to vector.
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 Real norm() const =0
Returns where .