ROL
ROL_LinearCombinationObjective_SimOpt.hpp
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43 
44 #ifndef ROL_LINEARCOMBINATIONOBJECTIVE_SIMOPT_H
45 #define ROL_LINEARCOMBINATIONOBJECTIVE_SIMOPT_H
46 
47 #include "ROL_Objective_SimOpt.hpp"
48 #include "ROL_Ptr.hpp"
49 
50 namespace ROL {
51 
52 template <class Real>
54 private:
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 
62 public:
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
171 public:
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
void hessVec_12(Vector< Real > &hv, const Vector< Real > &v, const Vector< Real > &u, const Vector< Real > &z, Real &tol)
Provides the interface to evaluate simulation-based objective functions.
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...
virtual ROL::Ptr< Vector > clone() const =0
Clone to make a new (uninitialized) vector.
LinearCombinationObjective_SimOpt(const std::vector< ROL::Ptr< Objective_SimOpt< Real > > > &obj)
const double weights[4][5]
Definition: ROL_Types.hpp:868
Real value(const Vector< Real > &u, const Vector< Real > &z, Real &tol)
Compute value.
virtual void axpy(const Real alpha, const Vector &x)
Compute where .
Definition: ROL_Vector.hpp:153
const std::vector< ROL::Ptr< Objective_SimOpt< Real > > > obj_
void hessVec_22(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, UpdateType type, int iter=-1)
virtual void zero()
Set to zero vector.
Definition: ROL_Vector.hpp:167
Defines the linear algebra or vector space interface.
Definition: ROL_Vector.hpp:80
void gradient_1(Vector< Real > &g, const Vector< Real > &u, const Vector< Real > &z, Real &tol)
Compute gradient with respect to first component.
LinearCombinationObjective_SimOpt(const std::vector< Real > &weights, const std::vector< ROL::Ptr< Objective_SimOpt< Real > > > &obj)
void hessVec_21(Vector< Real > &hv, const Vector< Real > &v, const Vector< Real > &u, const Vector< Real > &z, Real &tol)
void gradient_2(Vector< Real > &g, const Vector< Real > &u, const Vector< Real > &z, Real &tol)
Compute gradient with respect to second component.
virtual void setParameter(const std::vector< Real > &param)
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.