ROL
ROL_LinearCombinationObjective_SimOpt.hpp
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43
44#ifndef ROL_LINEARCOMBINATIONOBJECTIVE_SIMOPT_H
45#define ROL_LINEARCOMBINATIONOBJECTIVE_SIMOPT_H
46
48#include "ROL_Ptr.hpp"
49
50namespace ROL {
51
52template <class Real>
54private:
55 const std::vector<ROL::Ptr<Objective_SimOpt<Real> > > obj_;
56 std::vector<Real> weights_;
57 size_t size_;
58
59 ROL::Ptr<Vector<Real> > udual_, zdual_;
61
62public:
63 LinearCombinationObjective_SimOpt(const std::vector<ROL::Ptr<Objective_SimOpt<Real> > > &obj)
64 : Objective_SimOpt<Real>(), obj_(obj),
65 udual_(ROL::nullPtr), zdual_(ROL::nullPtr),
66 uinitialized_(false), zinitialized_(false) {
67 size_ = obj_.size();
68 weights_.clear(); weights_.assign(size_,static_cast<Real>(1));
69 }
70
71 LinearCombinationObjective_SimOpt(const std::vector<Real> &weights,
72 const std::vector<ROL::Ptr<Objective_SimOpt<Real> > > &obj)
73 : Objective_SimOpt<Real>(), obj_(obj),
74 weights_(weights), size_(weights.size()),
75 udual_(ROL::nullPtr), zdual_(ROL::nullPtr),
76 uinitialized_(false), zinitialized_(false) {}
77
78 void update(const Vector<Real> &u, const Vector<Real> &z, UpdateType type, int iter = -1) {
79 for (size_t i=0; i<size_; ++i) {
80 obj_[i]->update(u,z,type,iter);
81 }
82 }
83
84 void update(const Vector<Real> &u, const Vector<Real> &z, bool flag = true, int iter = -1) {
85 for (size_t i=0; i<size_; ++i) {
86 obj_[i]->update(u,z,flag,iter);
87 }
88 }
89
90 Real value( const Vector<Real> &u, const Vector<Real> &z, Real &tol ) {
91 Real val(0);
92 for (size_t i = 0; i < size_; ++i) {
93 val += weights_[i]*obj_[i]->value(u,z,tol);
94 }
95 return val;
96 }
97
98 void gradient_1( Vector<Real> &g, const Vector<Real> &u, const Vector<Real> &z, Real &tol ) {
99 if (!uinitialized_) {
100 udual_ = g.clone();
101 uinitialized_ = true;
102 }
103 g.zero();
104 for (size_t i = 0; i < size_; ++i) {
105 obj_[i]->gradient_1(*udual_,u,z,tol);
106 g.axpy(weights_[i],*udual_);
107 }
108 }
109
110 void gradient_2( Vector<Real> &g, const Vector<Real> &u, const Vector<Real> &z, Real &tol ) {
111 if (!zinitialized_) {
112 zdual_ = g.clone();
113 zinitialized_ = true;
114 }
115 g.zero();
116 for (size_t i = 0; i < size_; ++i) {
117 obj_[i]->gradient_2(*zdual_,u,z,tol);
118 g.axpy(weights_[i],*zdual_);
119 }
120 }
121
122 void hessVec_11( Vector<Real> &hv, const Vector<Real> &v, const Vector<Real> &u, const Vector<Real> &z, Real &tol ) {
123 if (!uinitialized_) {
124 udual_ = hv.clone();
125 uinitialized_ = true;
126 }
127 hv.zero();
128 for (size_t i = 0; i < size_; ++i) {
129 obj_[i]->hessVec_11(*udual_,v,u,z,tol);
130 hv.axpy(weights_[i],*udual_);
131 }
132 }
133
134 void hessVec_12( Vector<Real> &hv, const Vector<Real> &v, const Vector<Real> &u, const Vector<Real> &z, Real &tol ) {
135 if (!uinitialized_) {
136 udual_ = hv.clone();
137 uinitialized_ = true;
138 }
139 hv.zero();
140 for (size_t i = 0; i < size_; ++i) {
141 obj_[i]->hessVec_12(*udual_,v,u,z,tol);
142 hv.axpy(weights_[i],*udual_);
143 }
144 }
145
146 void hessVec_21( Vector<Real> &hv, const Vector<Real> &v, const Vector<Real> &u, const Vector<Real> &z, Real &tol ) {
147 if (!zinitialized_) {
148 zdual_ = hv.clone();
149 zinitialized_ = true;
150 }
151 hv.zero();
152 for (size_t i = 0; i < size_; ++i) {
153 obj_[i]->hessVec_21(*zdual_,v,u,z,tol);
154 hv.axpy(weights_[i],*zdual_);
155 }
156 }
157
158 void hessVec_22( Vector<Real> &hv, const Vector<Real> &v, const Vector<Real> &u, const Vector<Real> &z, Real &tol ) {
159 if (!zinitialized_) {
160 zdual_ = hv.clone();
161 zinitialized_ = true;
162 }
163 hv.zero();
164 for (size_t i = 0; i < size_; ++i) {
165 obj_[i]->hessVec_22(*zdual_,v,u,z,tol);
166 hv.axpy(weights_[i],*zdual_);
167 }
168 }
169
170// Definitions for parametrized (stochastic) objective functions
171public:
172 void setParameter(const std::vector<Real> &param) {
174 for (size_t i = 0; i < size_; ++i) {
175 obj_[i]->setParameter(param);
176 }
177 }
178}; // class LinearCombinationObjective
179
180} // namespace ROL
181
182#endif
LinearCombinationObjective_SimOpt(const std::vector< Real > &weights, const std::vector< ROL::Ptr< Objective_SimOpt< Real > > > &obj)
void gradient_1(Vector< Real > &g, const Vector< Real > &u, const Vector< Real > &z, Real &tol)
Compute gradient with respect to first component.
Real value(const Vector< Real > &u, const Vector< Real > &z, Real &tol)
Compute value.
void update(const Vector< Real > &u, const Vector< Real > &z, UpdateType type, int iter=-1)
void gradient_2(Vector< Real > &g, const Vector< Real > &u, const Vector< Real > &z, Real &tol)
Compute gradient with respect to second component.
void hessVec_22(Vector< Real > &hv, const Vector< Real > &v, const Vector< Real > &u, const Vector< Real > &z, Real &tol)
void hessVec_11(Vector< Real > &hv, const Vector< Real > &v, const Vector< Real > &u, const Vector< Real > &z, Real &tol)
Apply Hessian approximation to vector.
const std::vector< ROL::Ptr< Objective_SimOpt< Real > > > obj_
void hessVec_12(Vector< Real > &hv, const Vector< Real > &v, const Vector< Real > &u, const Vector< Real > &z, Real &tol)
void update(const Vector< Real > &u, const Vector< Real > &z, bool flag=true, int iter=-1)
Update objective function. u is an iterate, z is an iterate, flag = true if the iterate has changed...
void hessVec_21(Vector< Real > &hv, const Vector< Real > &v, const Vector< Real > &u, const Vector< Real > &z, Real &tol)
LinearCombinationObjective_SimOpt(const std::vector< ROL::Ptr< Objective_SimOpt< Real > > > &obj)
Provides the interface to evaluate simulation-based objective functions.
virtual void setParameter(const std::vector< Real > &param)
Defines the linear algebra or vector space interface.
Definition: ROL_Vector.hpp:84
virtual void zero()
Set to zero vector.
Definition: ROL_Vector.hpp:167
virtual ROL::Ptr< Vector > clone() const =0
Clone to make a new (uninitialized) vector.
virtual void axpy(const Real alpha, const Vector &x)
Compute where .
Definition: ROL_Vector.hpp:153