ROL_LinearCombinationObjective_SimOpt.hpp

Go to the documentation of this file.

// @HEADER
// *****************************************************************************
//               Rapid Optimization Library (ROL) Package
//
// Copyright 2014 NTESS and the ROL contributors.
// SPDX-License-Identifier: BSD-3-Clause
// *****************************************************************************
// @HEADER
 
#ifndef ROL_LINEARCOMBINATIONOBJECTIVE_SIMOPT_H
#define ROL_LINEARCOMBINATIONOBJECTIVE_SIMOPT_H
 
#include "ROL_Objective_SimOpt.hpp"
#include "ROL_Ptr.hpp"
 
namespace ROL {
 
template <class Real>
class LinearCombinationObjective_SimOpt : public Objective_SimOpt<Real> {
private:
  const std::vector<ROL::Ptr<Objective_SimOpt<Real> > > obj_;
  std::vector<Real> weights_;
  size_t size_;
 
  ROL::Ptr<Vector<Real> > udual_, zdual_;
  bool uinitialized_, zinitialized_;
 
public:
  LinearCombinationObjective_SimOpt(const std::vector<ROL::Ptr<Objective_SimOpt<Real> > > &obj)
    : Objective_SimOpt<Real>(), obj_(obj),
      udual_(ROL::nullPtr), zdual_(ROL::nullPtr),
      uinitialized_(false), zinitialized_(false) {
    size_ = obj_.size();
    weights_.clear(); weights_.assign(size_,static_cast<Real>(1));
  }
 
  LinearCombinationObjective_SimOpt(const std::vector<Real> &weights,
                                    const std::vector<ROL::Ptr<Objective_SimOpt<Real> > > &obj)
    : Objective_SimOpt<Real>(), obj_(obj),
      weights_(weights), size_(weights.size()),
      udual_(ROL::nullPtr), zdual_(ROL::nullPtr),
      uinitialized_(false), zinitialized_(false) {}
 
  void update(const Vector<Real> &u, const Vector<Real> &z, UpdateType type, int iter = -1) {
    for (size_t i=0; i<size_; ++i) {
      obj_[i]->update(u,z,type,iter);
    }
  }
 
  void update(const Vector<Real> &u, const Vector<Real> &z, bool flag = true, int iter = -1) {
    for (size_t i=0; i<size_; ++i) {
      obj_[i]->update(u,z,flag,iter);
    }
  }
 
  Real value( const Vector<Real> &u, const Vector<Real> &z, Real &tol ) {
    Real val(0);
    for (size_t i = 0; i < size_; ++i) {
      val += weights_[i]*obj_[i]->value(u,z,tol);
    } 
    return val;
  }
 
  void gradient_1( Vector<Real> &g, const Vector<Real> &u, const Vector<Real> &z, Real &tol ) {
    if (!uinitialized_) {
      udual_ = g.clone();
      uinitialized_ = true;
    }
    g.zero();
    for (size_t i = 0; i < size_; ++i) {
      obj_[i]->gradient_1(*udual_,u,z,tol);
      g.axpy(weights_[i],*udual_);
    }
  }
 
  void gradient_2( Vector<Real> &g, const Vector<Real> &u, const Vector<Real> &z, Real &tol ) {
    if (!zinitialized_) {
      zdual_ = g.clone();
      zinitialized_ = true;
    }
    g.zero();
    for (size_t i = 0; i < size_; ++i) {
      obj_[i]->gradient_2(*zdual_,u,z,tol);
      g.axpy(weights_[i],*zdual_);
    }
  }
 
  void hessVec_11( Vector<Real> &hv, const Vector<Real> &v, const Vector<Real> &u, const Vector<Real> &z, Real &tol ) {
    if (!uinitialized_) {
      udual_ = hv.clone();
      uinitialized_ = true;
    }
    hv.zero();
    for (size_t i = 0; i < size_; ++i) {
      obj_[i]->hessVec_11(*udual_,v,u,z,tol);
      hv.axpy(weights_[i],*udual_);
    }
  }
 
  void hessVec_12( Vector<Real> &hv, const Vector<Real> &v, const Vector<Real> &u, const Vector<Real> &z, Real &tol ) {
    if (!uinitialized_) {
      udual_ = hv.clone();
      uinitialized_ = true;
    }
    hv.zero();
    for (size_t i = 0; i < size_; ++i) {
      obj_[i]->hessVec_12(*udual_,v,u,z,tol);
      hv.axpy(weights_[i],*udual_);
    }
  }
 
  void hessVec_21( Vector<Real> &hv, const Vector<Real> &v, const Vector<Real> &u, const Vector<Real> &z, Real &tol ) {
    if (!zinitialized_) {
      zdual_ = hv.clone();
      zinitialized_ = true;
    }
    hv.zero();
    for (size_t i = 0; i < size_; ++i) {
      obj_[i]->hessVec_21(*zdual_,v,u,z,tol);
      hv.axpy(weights_[i],*zdual_);
    }
  }
 
  void hessVec_22( Vector<Real> &hv, const Vector<Real> &v, const Vector<Real> &u, const Vector<Real> &z, Real &tol ) {
    if (!zinitialized_) {
      zdual_ = hv.clone();
      zinitialized_ = true;
    }
    hv.zero();
    for (size_t i = 0; i < size_; ++i) {
      obj_[i]->hessVec_22(*zdual_,v,u,z,tol);
      hv.axpy(weights_[i],*zdual_);
    }
  }
 
// Definitions for parametrized (stochastic) objective functions
public:
  void setParameter(const std::vector<Real> &param) {
    Objective_SimOpt<Real>::setParameter(param);
    for (size_t i = 0; i < size_; ++i) {
      obj_[i]->setParameter(param);
    }
  }
}; // class LinearCombinationObjective
 
} // namespace ROL
 
#endif