ROL_MeanVarianceFromTarget.hpp

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#ifndef ROL_MEANVARIANCEFROMTARGET_HPP
#define ROL_MEANVARIANCEFROMTARGET_HPP

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

#include "ROL_ParameterList.hpp"

namespace ROL {

template<class Real>
class MeanVarianceFromTarget : public RandVarFunctional<Real> {
  typedef typename std::vector<Real>::size_type uint;
private:
  ROL::Ptr<PositiveFunction<Real> > positiveFunction_;
  std::vector<Real> target_;
  std::vector<Real> order_;
  std::vector<Real> coeff_;
  uint NumMoments_;

  using RandVarFunctional<Real>::val_;
  using RandVarFunctional<Real>::gv_;
  using RandVarFunctional<Real>::g_;
  using RandVarFunctional<Real>::hv_;
  using RandVarFunctional<Real>::dualVector_;

  using RandVarFunctional<Real>::point_;
  using RandVarFunctional<Real>::weight_;

  using RandVarFunctional<Real>::computeValue;
  using RandVarFunctional<Real>::computeGradient;
  using RandVarFunctional<Real>::computeGradVec;
  using RandVarFunctional<Real>::computeHessVec;

  void checkInputs(void) const {
    int oSize = order_.size(), cSize = coeff_.size();
    ROL_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++) {
      ROL_TEST_FOR_EXCEPTION((order_[i] < two), std::invalid_argument,
        ">>> ERROR (ROL::MeanVarianceFromTarget): Element of order array out of range!");
      ROL_TEST_FOR_EXCEPTION((coeff_[i] < zero), std::invalid_argument,
        ">>> ERROR (ROL::MeanVarianceFromTarget): Element of coefficient array out of range!");
    }
    ROL_TEST_FOR_EXCEPTION(positiveFunction_ == ROL::nullPtr, std::invalid_argument,
      ">>> ERROR (ROL::MeanVarianceFromTarget): PositiveFunction pointer is null!");
  }

public:
  MeanVarianceFromTarget( const Real target, const Real order, const Real coeff,
                          const ROL::Ptr<PositiveFunction<Real> > &pf )
    : RandVarFunctional<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 ROL::Ptr<PositiveFunction<Real> > &pf )
    : RandVarFunctional<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( ROL::ParameterList &parlist )
    : RandVarFunctional<Real>() {
    ROL::ParameterList &list
      = parlist.sublist("SOL").sublist("Risk Measure").sublist("Mean Plus Variance From Target");
    // Get data from parameter list
    target_ = ROL::getArrayFromStringParameter<double>(list,"Targets");
    order_  = ROL::getArrayFromStringParameter<double>(list,"Orders");
    coeff_  = ROL::getArrayFromStringParameter<double>(list,"Coefficients");

    // Build (approximate) positive function
    std::string type = list.get<std::string>("Deviation Type");
    if ( type == "Upper" ) {
      positiveFunction_ = ROL::makePtr<PlusFunction<Real>>(list);
    }
    else if ( type == "Absolute" ) {
      positiveFunction_ = ROL::makePtr<AbsoluteValue<Real>>(list);
    }
    else {
      ROL_TEST_FOR_EXCEPTION(true, std::invalid_argument,
        ">>> (ROL::MeanDeviation): Deviation type is not recoginized!");
    }
    // Check inputs
    checkInputs();
    NumMoments_ = order_.size();
  }
  
  void updateValue(Objective<Real>         &obj,
                   const Vector<Real>      &x,
                   const std::vector<Real> &xstat,
                   Real                    &tol) {
    Real diff(0), pf0(0);
    Real val = computeValue(obj,x,tol);
    val_    += weight_ * val;
    for ( uint p = 0; p < NumMoments_; p++ ) {
      diff = val-target_[p];
      pf0  = positiveFunction_->evaluate(diff,0);
      val_ += weight_ * coeff_[p] * std::pow(pf0,order_[p]);
    }
  }

  void updateGradient(Objective<Real>         &obj,
                      const Vector<Real>      &x,
                      const std::vector<Real> &xstat,
                      Real                    &tol) {
    Real diff(0), pf0(0), pf1(0), c(1), one(1);
    Real val = computeValue(obj,x,tol);
    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;
    }
    computeGradient(*dualVector_,obj,x,tol);
    g_->axpy(weight_ * c,*dualVector_);
  }

  void updateHessVec(Objective<Real>         &obj,
                     const Vector<Real>      &v,
                     const std::vector<Real> &vstat,
                     const Vector<Real>      &x,
                     const std::vector<Real> &xstat,
                     Real                    &tol) {
    Real diff(0), pf0(0), pf1(0), pf2(0), p1(0), p2(0), ch(1), cg(0), one(1), two(2);
    Real val = computeValue(obj,x,tol);
    Real gv  = computeGradVec(*dualVector_,obj,v,x,tol);
    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;
    }
    hv_->axpy(weight_*cg,*dualVector_);
    computeHessVec(*dualVector_,obj,v,x,tol);
    hv_->axpy(weight_*ch,*dualVector_);
  }
};

}

#endif