ROL_MeanVarianceFromTarget.hpp

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#ifndef ROL_MEANVARIANCEFROMTARGET_HPP
#define ROL_MEANVARIANCEFROMTARGET_HPP

#include "ROL_RiskMeasure.hpp"
#include "ROL_PositiveFunction.hpp"
#include "ROL_PlusFunction.hpp"
#include "ROL_AbsoluteValue.hpp"

#include "Teuchos_ParameterList.hpp"
#include "Teuchos_Array.hpp"

namespace ROL {

template<class Real>
class MeanVarianceFromTarget : public RiskMeasure<Real> {
  typedef typename std::vector<Real>::size_type uint;
private:

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

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

  void checkInputs(void) const {
    int oSize = order_.size(), cSize = coeff_.size();
    TEUCHOS_TEST_FOR_EXCEPTION((oSize!=cSize),std::invalid_argument,
      ">>> ERROR (ROL::MeanVarianceFromTarget): Order and coefficient arrays have different sizes!");
    Real zero(0), two(2);
    for (int i = 0; i < oSize; i++) {
      TEUCHOS_TEST_FOR_EXCEPTION((order_[i] < two), std::invalid_argument,
        ">>> ERROR (ROL::MeanVarianceFromTarget): Element of order array out of range!");
      TEUCHOS_TEST_FOR_EXCEPTION((coeff_[i] < zero), std::invalid_argument,
        ">>> ERROR (ROL::MeanVarianceFromTarget): Element of coefficient array out of range!");
    }
    TEUCHOS_TEST_FOR_EXCEPTION(positiveFunction_ == Teuchos::null, std::invalid_argument,
      ">>> ERROR (ROL::MeanVarianceFromTarget): PositiveFunction pointer is null!");
  }

public:
  MeanVarianceFromTarget( const Real target, const Real order, const Real coeff,
                          const Teuchos::RCP<PositiveFunction<Real> > &pf )
    : RiskMeasure<Real>(), positiveFunction_(pf) {
    target_.clear(); target_.push_back(target);
    order_.clear();  order_.push_back(order);
    coeff_.clear();  coeff_.push_back(coeff);
    checkInputs();
    NumMoments_ = order_.size();
  }

  MeanVarianceFromTarget( const std::vector<Real> &target,
                          const std::vector<Real> &order,
                          const std::vector<Real> &coeff, 
                          const Teuchos::RCP<PositiveFunction<Real> > &pf )
    : RiskMeasure<Real>(), positiveFunction_(pf) {
    target_.clear(); order_.clear(); coeff_.clear();
    for ( uint i = 0; i < target.size(); i++ ) {
      target_.push_back(target[i]);
    }
    for ( uint i = 0; i < order.size(); i++ ) {
      order_.push_back(order[i]);
    }
    for ( uint i = 0; i < coeff.size(); i++ ) {
      coeff_.push_back(coeff[i]);
    }
    checkInputs();
    NumMoments_ = order_.size();
  }
  
  MeanVarianceFromTarget( Teuchos::ParameterList &parlist )
    : RiskMeasure<Real>() {
    Teuchos::ParameterList &list
      = parlist.sublist("SOL").sublist("Risk Measure").sublist("Mean Plus Variance From Target");
    // Get data from parameter list
    Teuchos::Array<Real> target
      = Teuchos::getArrayFromStringParameter<double>(list,"Targets");
    target_ = target.toVector();
    Teuchos::Array<Real> order
      = Teuchos::getArrayFromStringParameter<double>(list,"Orders");
    order_ = order.toVector();
    Teuchos::Array<Real> coeff
      = Teuchos::getArrayFromStringParameter<double>(list,"Coefficients");
    coeff_ = coeff.toVector();
    // Build (approximate) positive function
    std::string type = list.get<std::string>("Deviation Type");
    if ( type == "Upper" ) {
      positiveFunction_ = Teuchos::rcp(new PlusFunction<Real>(list));
    }
    else if ( type == "Absolute" ) {
      positiveFunction_ = Teuchos::rcp(new AbsoluteValue<Real>(list));
    }
    else {
      TEUCHOS_TEST_FOR_EXCEPTION(true, std::invalid_argument,
        ">>> (ROL::MeanDeviation): Deviation type is not recoginized!");
    }
    // Check inputs
    checkInputs();
    NumMoments_ = order.size();
  }
  
  void update(const Real val, const Real weight) {
    Real diff(0), pf0(0);
    RiskMeasure<Real>::val_ += weight * val;
    for ( uint p = 0; p < NumMoments_; 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), pf0(0), pf1(0), c(1), one(1);
    for ( uint p = 0; p < NumMoments_; 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]-one)*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), pf0(0), pf1(0), pf2(0), p1(0), p2(0), ch(1), cg(0), one(1), two(2);
    for ( uint p = 0; p < NumMoments_; 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]-one);
      p2   = std::pow(pf0,order_[p]-two);
      cg  += order_[p]*coeff_[p]*gv*( (order_[p]-one)*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