ROL
ROL_LinearCombinationObjective.hpp
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43 
44 #ifndef ROL_LINEARCOMBINATIONOBJECTIVE_H
45 #define ROL_LINEARCOMBINATIONOBJECTIVE_H
46 
47 #include "ROL_Objective.hpp"
48 #include "ROL_Ptr.hpp"
49 
50 namespace ROL {
51 
52 template <class Real>
53 class LinearCombinationObjective : public Objective<Real> {
54 private:
55  const std::vector<ROL::Ptr<Objective<Real> > > obj_;
56  std::vector<Real> weights_;
57  size_t size_;
58 
59  ROL::Ptr<Vector<Real> > xdual_;
61 
62 public:
63  LinearCombinationObjective(const std::vector<ROL::Ptr<Objective<Real> > > &obj)
64  : Objective<Real>(), obj_(obj),
65  xdual_(ROL::nullPtr), initialized_(false) {
66  size_ = obj_.size();
67  weights_.clear(); weights_.assign(size_,static_cast<Real>(1));
68  }
69 
70  LinearCombinationObjective(const std::vector<Real> &weights,
71  const std::vector<ROL::Ptr<Objective<Real> > > &obj)
72  : Objective<Real>(), obj_(obj), weights_(weights), size_(weights.size()),
73  xdual_(ROL::nullPtr), initialized_(false) {}
74 
75  void update(const Vector<Real> &x, bool flag = true, int iter = -1) {
76  for (size_t i=0; i<size_; ++i) {
77  obj_[i]->update(x,flag,iter);
78  }
79  }
80 
81  Real value( const Vector<Real> &x, Real &tol ) {
82  Real val = 0.;
83  for (size_t i = 0; i < size_; i++) {
84  val += weights_[i]*obj_[i]->value(x,tol);
85  }
86  return val;
87  }
88 
89  void gradient( Vector<Real> &g, const Vector<Real> &x, Real &tol ) {
90  if (!initialized_) {
91  xdual_ = g.clone();
92  initialized_ = true;
93  }
94  g.zero();
95  for (size_t i = 0; i < size_; i++) {
96  obj_[i]->gradient(*xdual_,x,tol);
97  g.axpy(weights_[i],*xdual_);
98  }
99  }
100 
101  void hessVec( Vector<Real> &hv, const Vector<Real> &v, const Vector<Real> &x, Real &tol ) {
102  if (!initialized_) {
103  xdual_ = hv.clone();
104  initialized_ = true;
105  }
106  hv.zero();
107  for (size_t i = 0; i < size_; i++) {
108  obj_[i]->hessVec(*xdual_,v,x,tol);
109  hv.axpy(weights_[i],*xdual_);
110  }
111  }
112 
113 // Definitions for parametrized (stochastic) objective functions
114 public:
115  void setParameter(const std::vector<Real> &param) {
117  for (size_t i = 0; i < size_; ++i) {
118  obj_[i]->setParameter(param);
119  }
120  }
121 }; // class LinearCombinationObjective
122 
123 } // namespace ROL
124 
125 #endif
Provides the interface to evaluate objective functions.
LinearCombinationObjective(const std::vector< Real > &weights, const std::vector< ROL::Ptr< Objective< Real > > > &obj)
virtual ROL::Ptr< Vector > clone() const =0
Clone to make a new (uninitialized) vector.
const double weights[4][5]
Definition: ROL_Types.hpp:866
virtual void axpy(const Real alpha, const Vector &x)
Compute where .
Definition: ROL_Vector.hpp:153
void gradient(Vector< Real > &g, const Vector< Real > &x, Real &tol)
Compute gradient.
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
LinearCombinationObjective(const std::vector< ROL::Ptr< Objective< Real > > > &obj)
virtual void setParameter(const std::vector< Real > &param)
void update(const Vector< Real > &x, bool flag=true, int iter=-1)
Update objective function.
void setParameter(const std::vector< Real > &param)
Real value(const Vector< Real > &x, Real &tol)
Compute value.
const std::vector< ROL::Ptr< Objective< Real > > > obj_
void hessVec(Vector< Real > &hv, const Vector< Real > &v, const Vector< Real > &x, Real &tol)
Apply Hessian approximation to vector.