ROL
ROL_MeanVarianceFromTarget.hpp
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43 
44 #ifndef ROL_MEANVARIANCEFROMTARGET_HPP
45 #define ROL_MEANVARIANCEFROMTARGET_HPP
46 
47 #include "ROL_RiskMeasure.hpp"
48 #include "ROL_PositiveFunction.hpp"
49 
50 namespace ROL {
51 
52 template<class Real>
53 class MeanVarianceFromTarget : public RiskMeasure<Real> {
54 private:
55  std::vector<Real> target_;
56  std::vector<Real> order_;
57  std::vector<Real> coeff_;
58  Teuchos::RCP<PositiveFunction<Real> > positiveFunction_;
59 
60 public:
61  MeanVarianceFromTarget( Real target, Real order, Real coeff,
62  Teuchos::RCP<PositiveFunction<Real> > &pf ) : positiveFunction_(pf) {
63  target_.clear();
64  target_.push_back(target);
65  order_.clear();
66  order_.push_back((order < 2.0) ? 2.0 : order);
67  coeff_.clear();
68  coeff_.push_back((coeff < 0.0) ? 1.0 : coeff);
69  }
70  MeanVarianceFromTarget( std::vector<Real> &target, std::vector<Real> &order, std::vector<Real> &coeff,
71  Teuchos::RCP<PositiveFunction<Real> > &pf ) : positiveFunction_(pf) {
72  target_.clear();
73  order_.clear();
74  coeff_.clear();
75  if ( order.size() != target.size() ) {
76  target.resize(order.size(),0.0);
77  }
78  if ( order.size() != coeff.size() ) {
79  coeff.resize(order.size(),1.0);
80  }
81  for ( unsigned i = 0; i < order.size(); i++ ) {
82  target_.push_back(target[i]);
83  order_.push_back((order[i] < 2.0) ? 2.0 : order[i]);
84  coeff_.push_back((coeff[i] < 0.0) ? 1.0 : coeff[i]);
85  }
86  }
87 
88  void reset(Teuchos::RCP<Vector<Real> > &x0, const Vector<Real> &x) {
93  x0 = Teuchos::rcp(&const_cast<Vector<Real> &>(x),false);
94  }
95 
96  void reset(Teuchos::RCP<Vector<Real> > &x0, const Vector<Real> &x,
97  Teuchos::RCP<Vector<Real> > &v0, const Vector<Real> &v) {
102  x0 = Teuchos::rcp(&const_cast<Vector<Real> &>(x),false);
103  v0 = Teuchos::rcp(&const_cast<Vector<Real> &>(v),false);
104  }
105 
106  void update(const Real val, const Real weight) {
107  Real diff = 0.0, pf0 = 0.0;
108  RiskMeasure<Real>::val_ += weight * val;
109  for ( unsigned p = 0; p < this->order_.size(); p++ ) {
110  diff = val-this->target_[p];
111  pf0 = this->positiveFunction_->evaluate(diff,0);
112  RiskMeasure<Real>::val_ += weight * this->coeff_[p] * std::pow(pf0,this->order_[p]);
113  }
114  }
115 
116  void update(const Real val, const Vector<Real> &g, const Real weight) {
117  Real diff = 0.0, pf0 = 0.0, pf1 = 0.0, c = 1.0;
118  for ( unsigned p = 0; p < this->order_.size(); p++ ) {
119  diff = val-this->target_[p];
120  pf0 = this->positiveFunction_->evaluate(diff,0);
121  pf1 = this->positiveFunction_->evaluate(diff,1);
122  c += this->order_[p]*this->coeff_[p]*std::pow(pf0,this->order_[p]-1.0)*pf1;
123  }
124  (RiskMeasure<Real>::g_)->axpy(weight * c,g);
125  }
126 
127  void update(const Real val, const Vector<Real> &g, const Real gv, const Vector<Real> &hv,
128  const Real weight) {
129  Real diff = 0.0, pf0 = 0.0, pf1 = 0.0, pf2 = 0.0, p1 = 0.0, p2 = 0.0, ch = 1.0, cg = 0.0;
130  for ( unsigned p = 0; p < this->order_.size(); p++ ) {
131  diff = val - this->target_[p];
132  pf0 = this->positiveFunction_->evaluate(diff,0);
133  pf1 = this->positiveFunction_->evaluate(diff,1);
134  pf2 = this->positiveFunction_->evaluate(diff,2);
135  //p0 = std::pow(pf0,this->order_[p]);
136  p1 = std::pow(pf0,this->order_[p]-1.0);
137  p2 = std::pow(pf0,this->order_[p]-2.0);
138  cg += this->order_[p]*this->coeff_[p]*gv*( (this->order_[p]-1.0)*p2*pf1*pf1 + p1*pf2 );
139  ch += this->order_[p]*this->coeff_[p]*p1*pf1;
140  }
141  RiskMeasure<Real>::hv_->axpy(weight*cg,g);
142  RiskMeasure<Real>::hv_->axpy(weight*ch,hv);
143  }
144 
146  Real val = RiskMeasure<Real>::val_;
147  Real ev = 0.0;
148  sampler.sumAll(&val,&ev,1);
149  return ev;
150  }
151 
153  sampler.sumAll(*(RiskMeasure<Real>::g_),g);
154  }
155 
157  sampler.sumAll(*(RiskMeasure<Real>::hv_),hv);
158  }
159 };
160 
161 }
162 
163 #endif
void update(const Real val, const Vector< Real > &g, const Real gv, const Vector< Real > &hv, const Real weight)
void update(const Real val, const Vector< Real > &g, const Real weight)
virtual Teuchos::RCP< Vector > clone() const =0
Clone to make a new (uninitialized) vector.
Defines the linear algebra or vector space interface.
Definition: ROL_Vector.hpp:72
void sumAll(Real *input, Real *output, int dim) const
MeanVarianceFromTarget(Real target, Real order, Real coeff, Teuchos::RCP< PositiveFunction< Real > > &pf)
void getGradient(Vector< Real > &g, SampleGenerator< Real > &sampler)
MeanVarianceFromTarget(std::vector< Real > &target, std::vector< Real > &order, std::vector< Real > &coeff, Teuchos::RCP< PositiveFunction< Real > > &pf)
Teuchos::RCP< PositiveFunction< Real > > positiveFunction_
void reset(Teuchos::RCP< Vector< Real > > &x0, const Vector< Real > &x)
void getHessVec(Vector< Real > &hv, SampleGenerator< Real > &sampler)
Real getValue(SampleGenerator< Real > &sampler)
void update(const Real val, const Real weight)
void reset(Teuchos::RCP< Vector< Real > > &x0, const Vector< Real > &x, Teuchos::RCP< Vector< Real > > &v0, const Vector< Real > &v)