ROL_MeanVarianceFromTarget.hpp

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#ifndef ROL_MEANVARIANCEFROMTARGET_HPP
#define ROL_MEANVARIANCEFROMTARGET_HPP

#include "ROL_RiskMeasure.hpp"
#include "ROL_PositiveFunction.hpp"

namespace ROL {

template<class Real>
class MeanVarianceFromTarget : public RiskMeasure<Real> {
private:

  Teuchos::RCP<PositiveFunction<Real> > positiveFunction_;

  std::vector<Real> target_;
  std::vector<Real> order_;
  std::vector<Real> coeff_;

public:

  MeanVarianceFromTarget( Real target, Real order, Real coeff,
                          Teuchos::RCP<PositiveFunction<Real> > &pf )
    : RiskMeasure<Real>(), positiveFunction_(pf) {
    target_.clear(); order_.clear(); coeff_.clear();
    target_.push_back(target);
    order_.push_back((order < 2.0) ? 2.0 : order);
    coeff_.push_back((coeff < 0.0) ? 1.0 : coeff);
  }

  MeanVarianceFromTarget( std::vector<Real> &target, std::vector<Real> &order, std::vector<Real> &coeff, 
                          Teuchos::RCP<PositiveFunction<Real> > &pf )
    : RiskMeasure<Real>(), positiveFunction_(pf) {
    target_.clear(); order_.clear(); coeff_.clear();
    if ( order.size() != target.size() ) {
      target.resize(order.size(),0.0);
    }
    if ( order.size() != coeff.size() ) {
      coeff.resize(order.size(),1.0);
    }
    for ( unsigned i = 0; i < order.size(); i++ ) {
      target_.push_back(target[i]);
      order_.push_back((order[i] < 2.0) ? 2.0 : order[i]);
      coeff_.push_back((coeff[i] < 0.0) ? 1.0 : coeff[i]);
    }
  }
  
  void update(const Real val, const Real weight) {
    Real diff = 0.0, pf0 = 0.0;
    RiskMeasure<Real>::val_ += weight * val;
    for ( unsigned p = 0; p < order_.size(); p++ ) {
      diff = val-target_[p];
      pf0  = positiveFunction_->evaluate(diff,0);
      RiskMeasure<Real>::val_ += weight * coeff_[p] * std::pow(pf0,order_[p]);
    }
  }

  void update(const Real val, const Vector<Real> &g, const Real weight) {
    Real diff = 0.0, pf0 = 0.0, pf1 = 0.0, c = 1.0;
    for ( unsigned p = 0; p < order_.size(); p++ ) {
      diff = val-target_[p];
      pf0  = positiveFunction_->evaluate(diff,0);
      pf1  = positiveFunction_->evaluate(diff,1);
      c   += order_[p]*coeff_[p]*std::pow(pf0,order_[p]-1.0)*pf1;
    }
    (RiskMeasure<Real>::g_)->axpy(weight * c,g);
  }

  void update(const Real val, const Vector<Real> &g, const Real gv, const Vector<Real> &hv,
              const Real weight) {
    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;
    for ( unsigned p = 0; p < order_.size(); p++ ) {
      diff = val - target_[p];
      pf0  = positiveFunction_->evaluate(diff,0);
      pf1  = positiveFunction_->evaluate(diff,1);
      pf2  = positiveFunction_->evaluate(diff,2);
      //p0   = std::pow(pf0,order_[p]);
      p1   = std::pow(pf0,order_[p]-1.0);
      p2   = std::pow(pf0,order_[p]-2.0);
      cg  += order_[p]*coeff_[p]*gv*( (order_[p]-1.0)*p2*pf1*pf1 + p1*pf2 );
      ch  += order_[p]*coeff_[p]*p1*pf1;
    }
    RiskMeasure<Real>::hv_->axpy(weight*cg,g);
    RiskMeasure<Real>::hv_->axpy(weight*ch,hv);
  }
};

}

#endif