44 #ifndef ROL_RISKAVERSEOBJECTIVE_HPP 45 #define ROL_RISKAVERSEOBJECTIVE_HPP 47 #include "Teuchos_RCP.hpp" 73 Teuchos::RCP<Vector<Real> >
x_;
74 Teuchos::RCP<Vector<Real> >
v_;
75 Teuchos::RCP<Vector<Real> >
g_;
76 Teuchos::RCP<Vector<Real> >
hv_;
80 const std::vector<Real> ¶m, Real &tol) {
81 if ( storage_ && value_storage_.count(param) ) {
82 val = value_storage_[param];
85 ParametrizedObjective_->setParameter(param);
86 val = ParametrizedObjective_->value(x,tol);
88 value_storage_.insert(std::pair<std::vector<Real>,Real>(param,val));
98 const std::vector<Real> ¶m, Real &tol) {
99 if ( storage_ && gradient_storage_.count(param) ) {
100 g.
set(*(gradient_storage_[param]));
103 ParametrizedObjective_->setParameter(param);
104 ParametrizedObjective_->gradient(g,x,tol);
106 Teuchos::RCP<Vector<Real> > tmp = g.
clone();
107 gradient_storage_.insert(std::pair<std::vector<Real>,Teuchos::RCP<
Vector<Real> > >(param,tmp));
108 gradient_storage_[param]->set(g);
118 const std::vector<Real> ¶m, Real &tol) {
119 ParametrizedObjective_->setParameter(param);
120 ParametrizedObjective_->hessVec(hv,v,x,tol);
131 const bool storage =
true )
132 : ParametrizedObjective_(pObj), RiskMeasure_(rm),
133 ValueSampler_(vsampler), GradientSampler_(gsampler), HessianSampler_(hsampler),
134 firstUpdate_(true), storage_(storage) {
135 value_storage_.clear();
136 gradient_storage_.clear();
143 const bool storage =
true )
144 : ParametrizedObjective_(pObj), RiskMeasure_(rm),
145 ValueSampler_(vsampler), GradientSampler_(gsampler), HessianSampler_(gsampler),
146 firstUpdate_(true), storage_(storage) {
147 value_storage_.clear();
148 gradient_storage_.clear();
154 const bool storage =
true )
155 : ParametrizedObjective_(pObj), RiskMeasure_(rm),
156 ValueSampler_(sampler), GradientSampler_(sampler), HessianSampler_(sampler),
157 firstUpdate_(true), storage_(storage) {
158 value_storage_.clear();
159 gradient_storage_.clear();
163 Teuchos::ParameterList &parlist,
167 : ParametrizedObjective_(pObj),
168 ValueSampler_(vsampler), GradientSampler_(gsampler), HessianSampler_(hsampler),
170 RiskMeasure_ = RiskMeasureFactory<Real>(parlist);
171 storage_ = parlist.sublist(
"SOL").get(
"Store Sampled Value and Gradient",
true);
172 value_storage_.clear();
173 gradient_storage_.clear();
177 Teuchos::ParameterList &parlist,
180 : ParametrizedObjective_(pObj),
181 ValueSampler_(vsampler), GradientSampler_(gsampler), HessianSampler_(gsampler),
183 RiskMeasure_ = RiskMeasureFactory<Real>(parlist);
184 storage_ = parlist.sublist(
"SOL").get(
"Store Sampled Value and Gradient",
true);
185 value_storage_.clear();
186 gradient_storage_.clear();
190 Teuchos::ParameterList &parlist,
192 : ParametrizedObjective_(pObj),
193 ValueSampler_(sampler), GradientSampler_(sampler), HessianSampler_(sampler),
195 RiskMeasure_ = RiskMeasureFactory<Real>(parlist);
196 storage_ = parlist.sublist(
"SOL").get(
"Store Sampled Value and Gradient",
true);
197 value_storage_.clear();
198 gradient_storage_.clear();
202 if ( firstUpdate_ ) {
203 RiskMeasure_->reset(x_,x);
204 g_ = (x_->dual()).clone();
205 hv_ = (x_->dual()).clone();
206 firstUpdate_ =
false;
208 ParametrizedObjective_->update(x,flag,iter);
209 ValueSampler_->update(x);
211 value_storage_.clear();
214 GradientSampler_->update(x);
215 HessianSampler_->update(x);
217 gradient_storage_.clear();
224 RiskMeasure_->reset(x_,x);
225 for (
int i = 0; i < ValueSampler_->numMySamples(); i++ ) {
226 getValue(val,*x_,ValueSampler_->getMyPoint(i),tol);
227 RiskMeasure_->update(val,ValueSampler_->getMyWeight(i));
229 return RiskMeasure_->getValue(*ValueSampler_);
235 RiskMeasure_->reset(x_,x);
236 for (
int i = 0; i < GradientSampler_->numMySamples(); i++ ) {
237 getValue(val,*x_,GradientSampler_->getMyPoint(i),tol);
238 getGradient(*g_,*x_,GradientSampler_->getMyPoint(i),tol);
239 RiskMeasure_->update(val,*g_,GradientSampler_->getMyWeight(i));
241 RiskMeasure_->getGradient(g,*GradientSampler_);
246 Real val = 0.0, gv = 0.0;
248 RiskMeasure_->reset(x_,x,v_,v);
249 for (
int i = 0; i < HessianSampler_->numMySamples(); i++ ) {
250 getValue(val,*x_,HessianSampler_->getMyPoint(i),tol);
251 getGradient(*g_,*x_,HessianSampler_->getMyPoint(i),tol);
252 getHessVec(*hv_,*v_,*x_,HessianSampler_->getMyPoint(i),tol);
253 gv = g_->dot(v_->dual());
254 RiskMeasure_->update(val,*g_,gv,*hv_,HessianSampler_->getMyWeight(i));
256 RiskMeasure_->getHessVec(hv,*HessianSampler_);
virtual void gradient(Vector< Real > &g, const Vector< Real > &x, Real &tol)
Compute gradient.
Teuchos::RCP< Vector< Real > > g_
Provides the interface to evaluate objective functions.
virtual const Vector & dual() const
Return dual representation of , for example, the result of applying a Riesz map, or change of basis...
RiskAverseObjective(const Teuchos::RCP< ParametrizedObjective< Real > > &pObj, const Teuchos::RCP< RiskMeasure< Real > > &rm, const Teuchos::RCP< SampleGenerator< Real > > &vsampler, const Teuchos::RCP< SampleGenerator< Real > > &gsampler, const Teuchos::RCP< SampleGenerator< Real > > &hsampler, const bool storage=true)
virtual void update(const Vector< Real > &x, bool flag=true, int iter=-1)
Update objective function.
Teuchos::RCP< ParametrizedObjective< Real > > ParametrizedObjective_
void getValue(Real &val, const Vector< Real > &x, const std::vector< Real > ¶m, Real &tol)
virtual Teuchos::RCP< Vector > clone() const =0
Clone to make a new (uninitialized) vector.
virtual void zero()
Set to zero vector.
Teuchos::RCP< SampleGenerator< Real > > GradientSampler_
Defines the linear algebra or vector space interface.
Teuchos::RCP< Vector< Real > > hv_
virtual void precond(Vector< Real > &Pv, const Vector< Real > &v, const Vector< Real > &x, Real &tol)
Apply preconditioner to vector.
RiskAverseObjective(const Teuchos::RCP< ParametrizedObjective< Real > > &pObj, const Teuchos::RCP< RiskMeasure< Real > > &rm, const Teuchos::RCP< SampleGenerator< Real > > &vsampler, const Teuchos::RCP< SampleGenerator< Real > > &gsampler, const bool storage=true)
Teuchos::RCP< Vector< Real > > x_
RiskAverseObjective(const Teuchos::RCP< ParametrizedObjective< Real > > &pObj, Teuchos::ParameterList &parlist, const Teuchos::RCP< SampleGenerator< Real > > &sampler)
void getHessVec(Vector< Real > &hv, const Vector< Real > &v, const Vector< Real > &x, const std::vector< Real > ¶m, Real &tol)
RiskAverseObjective(const Teuchos::RCP< ParametrizedObjective< Real > > &pObj, Teuchos::ParameterList &parlist, const Teuchos::RCP< SampleGenerator< Real > > &vsampler, const Teuchos::RCP< SampleGenerator< Real > > &gsampler, const Teuchos::RCP< SampleGenerator< Real > > &hsampler)
RiskAverseObjective(const Teuchos::RCP< ParametrizedObjective< Real > > &pObj, Teuchos::ParameterList &parlist, const Teuchos::RCP< SampleGenerator< Real > > &vsampler, const Teuchos::RCP< SampleGenerator< Real > > &gsampler)
std::map< std::vector< Real >, Teuchos::RCP< Vector< Real > > > gradient_storage_
void getGradient(Vector< Real > &g, const Vector< Real > &x, const std::vector< Real > ¶m, Real &tol)
std::map< std::vector< Real >, Real > value_storage_
Teuchos::RCP< Vector< Real > > v_
Teuchos::RCP< RiskMeasure< Real > > RiskMeasure_
virtual void set(const Vector &x)
Set where .
virtual void hessVec(Vector< Real > &hv, const Vector< Real > &v, const Vector< Real > &x, Real &tol)
Apply Hessian approximation to vector.
RiskAverseObjective(const Teuchos::RCP< ParametrizedObjective< Real > > &pObj, const Teuchos::RCP< RiskMeasure< Real > > &rm, const Teuchos::RCP< SampleGenerator< Real > > &sampler, const bool storage=true)
virtual ~RiskAverseObjective()
Teuchos::RCP< SampleGenerator< Real > > HessianSampler_
Teuchos::RCP< SampleGenerator< Real > > ValueSampler_
virtual Real value(const Vector< Real > &x, Real &tol)
Compute value.