ROL_MeanSemiDeviationFromTarget.hpp

Go to the documentation of this file.

// @HEADER
// ************************************************************************
//
//               Rapid Optimization Library (ROL) Package
//                 Copyright (2014) Sandia Corporation
//
// Under terms of Contract DE-AC04-94AL85000, there is a non-exclusive
// license for use of this work by or on behalf of the U.S. Government.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are
// met:
//
// 1. Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
//
// 2. Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
//
// 3. Neither the name of the Corporation nor the names of the
// contributors may be used to endorse or promote products derived from
// this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY SANDIA CORPORATION "AS IS" AND ANY
// EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL SANDIA CORPORATION OR THE
// CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
//
// Questions? Contact lead developers:
//              Drew Kouri   (dpkouri@sandia.gov) and
//              Denis Ridzal (dridzal@sandia.gov)
//
// ************************************************************************
// @HEADER

#ifndef ROL_MEANSEMIDEVIATIONFROMTARGET_HPP
#define ROL_MEANSEMIDEVIATIONFROMTARGET_HPP

#include "ROL_RandVarFunctional.hpp"
#include "ROL_PlusFunction.hpp"

namespace ROL {

template<class Real>
class MeanSemiDeviationFromTarget : public RandVarFunctional<Real> {
private:
  Ptr<PlusFunction<Real> > plusFunction_;
  Real coeff_, target_;

  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 {
    const Real zero(0);
    ROL_TEST_FOR_EXCEPTION((coeff_ < zero), std::invalid_argument,
      ">>> ERROR (ROL::MeanPlusSemiDeviationFromTarget): Coefficient must be positive!");
    ROL_TEST_FOR_EXCEPTION(plusFunction_ == nullPtr, std::invalid_argument,
      ">>> ERROR (ROL::MeanSemiDeviation): PlusFunction pointer is null!");
  }

public:

  MeanSemiDeviationFromTarget( const Real coeff, const Real target,
                               const Ptr<PlusFunction<Real>> &pf )
    : RandVarFunctional<Real>(), plusFunction_(pf), coeff_(coeff), target_(target) {
    checkInputs();
  }

  MeanSemiDeviationFromTarget( ROL::ParameterList &parlist )
    : RandVarFunctional<Real>() {
    ROL::ParameterList &list
      = parlist.sublist("SOL").sublist("Risk Measure").sublist("Mean Plus Semi-Deviation From Target");
    // Check inputs
    coeff_  = list.get<Real>("Coefficient");
    target_ = list.get<Real>("Target");
    // Build (approximate) plus function
    plusFunction_ = makePtr<PlusFunction<Real>>(list);
    // Check Inputs
    checkInputs();
  }

  void updateValue(Objective<Real>         &obj,
                   const Vector<Real>      &x,
                   const std::vector<Real> &xstat,
                   Real                    &tol) {
    Real val = computeValue(obj,x,tol);
    Real pf  = plusFunction_->evaluate(val-target_,0);
    val_    += weight_ * (val + coeff_ * pf);
  }

  Real getValue(const Vector<Real>      &x,
                const std::vector<Real> &xstat,
                SampleGenerator<Real>   &sampler) {
    Real ev(0);
    sampler.sumAll(&val_,&ev,1);
    return ev;
  }

  void updateGradient(Objective<Real>         &obj,
                      const Vector<Real>      &x,
                      const std::vector<Real> &xstat,
                      Real                    &tol) {
    const Real one(1);
    Real val = computeValue(obj,x,tol);
    Real pf  = plusFunction_->evaluate(val-target_,1);
    computeGradient(*dualVector_,obj,x,tol);
    g_->axpy(weight_ * (one + coeff_ * pf), *dualVector_);
  }

  void getGradient(Vector<Real>            &g,
                   std::vector<Real>       &gstat,
                   const Vector<Real>      &x,
                   const std::vector<Real> &xstat,
                   SampleGenerator<Real>   &sampler) {
    sampler.sumAll(*g_, g);
  }

  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) {
    const Real one(1);
    Real val = computeValue(obj,x,tol);
    Real pf1 = plusFunction_->evaluate(val-target_,1);
    Real pf2 = plusFunction_->evaluate(val-target_,2);
    Real gv  = computeGradVec(*dualVector_,obj,v,x,tol);
    hv_->axpy(weight_ * coeff_ * pf2 * gv, *dualVector_);
    computeHessVec(*dualVector_,obj,v,x,tol);
    hv_->axpy(weight_ * (one + coeff_ * pf1), *dualVector_);
  }

  void getHessVec(Vector<Real>            &hv,
                  std::vector<Real>       &hvstat,
                  const Vector<Real>      &v,
                  const std::vector<Real> &vstat,
                  const Vector<Real>      &x,
                  const std::vector<Real> &xstat,
                  SampleGenerator<Real>   &sampler) {
    sampler.sumAll(*hv_, hv);
  }
};

}

#endif