/usr/include/trilinos/ROL_LinearCombinationObjective_SimOpt.hpp is in libtrilinos-rol-dev 12.12.1-5.
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// ************************************************************************
//
// Rapid Optimization Library (ROL) Package
// Copyright (2014) Sandia Corporation
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#ifndef ROL_LINEARCOMBINATIONOBJECTIVE_SIMOPT_H
#define ROL_LINEARCOMBINATIONOBJECTIVE_SIMOPT_H
#include "ROL_Objective_SimOpt.hpp"
#include "Teuchos_RCP.hpp"
namespace ROL {
template <class Real>
class LinearCombinationObjective_SimOpt : public Objective_SimOpt<Real> {
private:
const std::vector<Teuchos::RCP<Objective_SimOpt<Real> > > obj_;
std::vector<Real> weights_;
size_t size_;
Teuchos::RCP<Vector<Real> > udual_, zdual_;
bool uinitialized_, zinitialized_;
public:
LinearCombinationObjective_SimOpt(const std::vector<Teuchos::RCP<Objective_SimOpt<Real> > > &obj)
: Objective_SimOpt<Real>(), obj_(obj),
udual_(Teuchos::null), zdual_(Teuchos::null),
uinitialized_(false), zinitialized_(false) {
size_ = obj_.size();
weights_.clear(); weights_.assign(size_,static_cast<Real>(1));
}
LinearCombinationObjective_SimOpt(const std::vector<Real> &weights,
const std::vector<Teuchos::RCP<Objective_SimOpt<Real> > > &obj)
: Objective_SimOpt<Real>(), obj_(obj),
weights_(weights), size_(weights.size()),
udual_(Teuchos::null), zdual_(Teuchos::null),
uinitialized_(false), zinitialized_(false) {}
void update(const Vector<Real> &u, const Vector<Real> &z, bool flag = true, int iter = -1) {
for (size_t i=0; i<size_; ++i) {
obj_[i]->update(u,z,flag,iter);
}
}
Real value( const Vector<Real> &u, const Vector<Real> &z, Real &tol ) {
Real val(0);
for (size_t i = 0; i < size_; ++i) {
val += weights_[i]*obj_[i]->value(u,z,tol);
}
return val;
}
void gradient_1( Vector<Real> &g, const Vector<Real> &u, const Vector<Real> &z, Real &tol ) {
if (!uinitialized_) {
udual_ = g.clone();
uinitialized_ = true;
}
g.zero();
for (size_t i = 0; i < size_; ++i) {
obj_[i]->gradient_1(*udual_,u,z,tol);
g.axpy(weights_[i],*udual_);
}
}
void gradient_2( Vector<Real> &g, const Vector<Real> &u, const Vector<Real> &z, Real &tol ) {
if (!zinitialized_) {
zdual_ = g.clone();
zinitialized_ = true;
}
g.zero();
for (size_t i = 0; i < size_; ++i) {
obj_[i]->gradient_2(*zdual_,u,z,tol);
g.axpy(weights_[i],*zdual_);
}
}
void hessVec_11( Vector<Real> &hv, const Vector<Real> &v, const Vector<Real> &u, const Vector<Real> &z, Real &tol ) {
if (!uinitialized_) {
udual_ = hv.clone();
uinitialized_ = true;
}
hv.zero();
for (size_t i = 0; i < size_; ++i) {
obj_[i]->hessVec_11(*udual_,v,u,z,tol);
hv.axpy(weights_[i],*udual_);
}
}
void hessVec_12( Vector<Real> &hv, const Vector<Real> &v, const Vector<Real> &u, const Vector<Real> &z, Real &tol ) {
if (!uinitialized_) {
udual_ = hv.clone();
uinitialized_ = true;
}
hv.zero();
for (size_t i = 0; i < size_; ++i) {
obj_[i]->hessVec_12(*udual_,v,u,z,tol);
hv.axpy(weights_[i],*udual_);
}
}
void hessVec_21( Vector<Real> &hv, const Vector<Real> &v, const Vector<Real> &u, const Vector<Real> &z, Real &tol ) {
if (!zinitialized_) {
zdual_ = hv.clone();
zinitialized_ = true;
}
hv.zero();
for (size_t i = 0; i < size_; ++i) {
obj_[i]->hessVec_21(*zdual_,v,u,z,tol);
hv.axpy(weights_[i],*zdual_);
}
}
void hessVec_22( Vector<Real> &hv, const Vector<Real> &v, const Vector<Real> &u, const Vector<Real> &z, Real &tol ) {
if (!zinitialized_) {
zdual_ = hv.clone();
zinitialized_ = true;
}
hv.zero();
for (size_t i = 0; i < size_; ++i) {
obj_[i]->hessVec_22(*zdual_,v,u,z,tol);
hv.axpy(weights_[i],*zdual_);
}
}
// Definitions for parametrized (stochastic) objective functions
public:
void setParameter(const std::vector<Real> ¶m) {
Objective_SimOpt<Real>::setParameter(param);
for (size_t i = 0; i < size_; ++i) {
obj_[i]->setParameter(param);
}
}
}; // class LinearCombinationObjective
} // namespace ROL
#endif
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