ROL_CVaR.hpp

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// @HEADER
// *****************************************************************************
//               Rapid Optimization Library (ROL) Package
//
// Copyright 2014 NTESS and the ROL contributors.
// SPDX-License-Identifier: BSD-3-Clause
// *****************************************************************************
// @HEADER
 
#ifndef ROL_CVAR_HPP
#define ROL_CVAR_HPP
 
#include "ROL_RandVarFunctional.hpp"
#include "ROL_PlusFunction.hpp"
 
namespace ROL {
 
template<class Real>
class CVaR : public RandVarFunctional<Real> {
private:
  Ptr<PlusFunction<Real> > plusFunction_;
  Real prob_;
  Real coeff_;
 
  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 {
    Real zero(0), one(1);
    ROL_TEST_FOR_EXCEPTION((prob_ <= zero) || (prob_ >= one), std::invalid_argument,
      ">>> ERROR (ROL::CVaR): Confidence level must be between 0 and 1!");
    ROL_TEST_FOR_EXCEPTION((coeff_ < zero) || (coeff_ > one), std::invalid_argument,
      ">>> ERROR (ROL::CVaR): Convex combination parameter must be positive!");
    ROL_TEST_FOR_EXCEPTION(plusFunction_ == nullPtr, std::invalid_argument,
      ">>> ERROR (ROL::CVaR): PlusFunction pointer is null!");
  }
 
public:
 
  CVaR( const Real prob, const Real coeff,
        const Ptr<PlusFunction<Real> > &pf )
    : RandVarFunctional<Real>(), plusFunction_(pf), prob_(prob), coeff_(coeff) {
    checkInputs();
  }
 
  CVaR( ROL::ParameterList &parlist )
    : RandVarFunctional<Real>() {
    ROL::ParameterList &list
      = parlist.sublist("SOL").sublist("Risk Measure").sublist("CVaR");
    // Check CVaR inputs
    prob_  = list.get<Real>("Confidence Level");
    coeff_ = list.get<Real>("Convex Combination Parameter");
    // 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 one(1);
    Real val = computeValue(obj,x,tol);
    Real pf  = plusFunction_->evaluate(val-xstat[0],0);
    val_ += weight_*((one-coeff_)*val + coeff_/(one-prob_)*pf);
  }
 
  void updateGradient(Objective<Real>         &obj,
                      const Vector<Real>      &x,
                      const std::vector<Real> &xstat,
                      Real                    &tol) {
    Real one(1);
    Real val = computeValue(obj,x,tol);
    Real pf  = plusFunction_->evaluate(val-xstat[0],1);
    val_    += weight_*pf;
    Real c   = (one-coeff_) + coeff_/(one-prob_)*pf;
    if (std::abs(c) >= ROL_EPSILON<Real>()) {
      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 one(1);
    Real val = computeValue(obj,x,tol);
    Real pf1 = plusFunction_->evaluate(val-xstat[0],1);
    Real pf2 = plusFunction_->evaluate(val-xstat[0],2);
    Real c(0);
    if (std::abs(pf2) >= ROL_EPSILON<Real>()) {
      Real gv = computeGradVec(*dualVector_,obj,v,x,tol);
      val_   += weight_*pf2*(vstat[0]-gv);
      c       = pf2*coeff_/(one-prob_)*(gv-vstat[0]);
      hv_->axpy(weight_*c,*dualVector_);
    }
    c = (one-coeff_) + coeff_/(one-prob_)*pf1;
    if (std::abs(c) >= ROL_EPSILON<Real>()) {
      computeHessVec(*dualVector_,obj,v,x,tol);
      hv_->axpy(weight_*c,*dualVector_);
    }
  }
 
  Real getValue(const Vector<Real>      &x,
                const std::vector<Real> &xstat,
                SampleGenerator<Real>   &sampler) {
    Real cvar(0);
    sampler.sumAll(&val_,&cvar,1);
    cvar += coeff_*xstat[0];
    return cvar;
  }
 
  void getGradient(Vector<Real>            &g,
                   std::vector<Real>       &gstat,
                   const Vector<Real>      &x,
                   const std::vector<Real> &xstat,
                   SampleGenerator<Real>   &sampler) {
    Real var(0), one(1);
    sampler.sumAll(&val_,&var,1);
    var *= -coeff_/(one-prob_);
    var += coeff_;
    gstat[0] = var;
    sampler.sumAll(*g_,g);
  }
 
  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) {
    Real var(0), one(1);
    sampler.sumAll(&val_,&var,1);
    var *= coeff_/(one-prob_);
    hvstat[0] = var;
    sampler.sumAll(*hv_,hv);
  }
};
 
}
 
#endif