ROL_ProbabilityVector.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_PROBABILITYVECTOR_H
#define ROL_PROBABILITYVECTOR_H

#include "ROL_BatchStdVector.hpp"

namespace ROL {

template <class Real>
class PrimalProbabilityVector;

template <class Real>
class DualProbabilityVector;

template <class Real>
class ProbabilityVector : public BatchStdVector<Real> {
  typedef typename std::vector<Real>::size_type uint;
public:
  ProbabilityVector(const ROL::Ptr<std::vector<Real> > &vec,
                    const ROL::Ptr<BatchManager<Real> > &bman)
   : BatchStdVector<Real>(vec,bman) {}

  const Real getProbability(const int i) const {
    const std::vector<Real> &yval = *(StdVector<Real>::getVector());
    int numMySamples = static_cast<int>(yval.size());
    ROL_TEST_FOR_EXCEPTION((i < 0 || i >= numMySamples), std::invalid_argument,
      ">>> ERROR (ROL::ProbabilityVector): index out of bounds in getProbability!");
    return yval[i];
  }

  void setProbability(const int i, const Real wt) {
    std::vector<Real> &yval = *(StdVector<Real>::getVector());
    int numMySamples = static_cast<int>(yval.size());
    ROL_TEST_FOR_EXCEPTION((i < 0 || i >= numMySamples), std::invalid_argument,
      ">>> ERROR (ROL::ProbabilityVector): index out of bounds in setProbability!");
    yval[i] = wt;
  }

  int getNumMyAtoms(void) const {
    int numMySamples = static_cast<int>(StdVector<Real>::getVector()->size());
    return numMySamples;
  }
};

template<class Real>
class PrimalProbabilityVector : public ProbabilityVector<Real> {
  typedef typename std::vector<Real>::size_type uint;
private:
  ROL::Ptr<std::vector<Real> > scale_;
  mutable ROL::Ptr<DualProbabilityVector<Real> > dual_vec_;
  mutable bool isDualInitialized_;

public:
  PrimalProbabilityVector(const ROL::Ptr<std::vector<Real> > &vec,
                          const ROL::Ptr<BatchManager<Real> > &bman,
                          const ROL::Ptr<std::vector<Real> > &scale)
    : ProbabilityVector<Real>(vec,bman), scale_(scale),
      isDualInitialized_(false) {}

  Real dot(const Vector<Real> &x) const {
    const std::vector<Real> &xval = *(dynamic_cast<const StdVector<Real>&>(x).getVector());
    const std::vector<Real> &yval = *(StdVector<Real>::getVector());
    uint numMySamples = static_cast<uint>(yval.size());
    ROL_TEST_FOR_EXCEPTION( xval.size() != numMySamples, std::invalid_argument,
      "Error: Vectors must have the same dimension." );
    Real val(0), sum_val(0);
    for (uint i = 0; i < numMySamples; i++) {
      val += xval[i] * (*scale_)[i] * yval[i];
    }
    // Global sum
    BatchStdVector<Real>::getBatchManager()->sumAll(&val,&sum_val,1);
    return sum_val;
  }

  ROL::Ptr<Vector<Real> > clone(void) const {
    uint numMySamples = static_cast<uint>(StdVector<Real>::getVector()->size());
    return ROL::makePtr<PrimalProbabilityVector>(
           ROL::makePtr<std::vector<Real>>(numMySamples),
           BatchStdVector<Real>::getBatchManager(),scale_);
  }

  const Vector<Real> & dual(void) const {
    uint numMySamples = static_cast<uint>(StdVector<Real>::getVector()->size());
    if ( !isDualInitialized_ ) {
      dual_vec_ = ROL::makePtr<DualProbabilityVector<Real>>(
                  ROL::makePtr<std::vector<Real>>(numMySamples),
                  BatchStdVector<Real>::getBatchManager(),scale_);
      isDualInitialized_ = true;
    }
    for (uint i = 0; i < numMySamples; ++i) {
      (*(dual_vec_->getVector()))[i]
        = (*scale_)[i]*(*StdVector<Real>::getVector())[i];
    }
    return *dual_vec_;
  }
};

template<class Real>
class DualProbabilityVector : public ProbabilityVector<Real> {
  typedef typename std::vector<Real>::size_type uint;
private:
  ROL::Ptr<std::vector<Real> > scale_;
  mutable ROL::Ptr<PrimalProbabilityVector<Real> > primal_vec_;
  mutable bool isDualInitialized_;

public:
  DualProbabilityVector(const ROL::Ptr<std::vector<Real> > &vec,
                        const ROL::Ptr<BatchManager<Real> > &bman,
                        const ROL::Ptr<std::vector<Real> > &scale)
    : ProbabilityVector<Real>(vec,bman), scale_(scale),
      isDualInitialized_(false) {}

  Real dot(const Vector<Real> &x) const {
    const std::vector<Real> &xval = *(dynamic_cast<const StdVector<Real>&>(x).getVector());
    const std::vector<Real> &yval = *(StdVector<Real>::getVector());
    uint numMySamples = static_cast<uint>(yval.size());
    ROL_TEST_FOR_EXCEPTION( xval.size() != numMySamples, std::invalid_argument,
      "Error: Vectors must have the same dimension." );
    Real val(0), sum_val(0);
    for (uint i = 0; i < numMySamples; i++) {
      val += xval[i] * yval[i] / (*scale_)[i];
    }
    // Global sum
    BatchStdVector<Real>::getBatchManager()->sumAll(&val,&sum_val,1);
    return sum_val;
  }

  ROL::Ptr<Vector<Real> > clone(void) const {
    uint numMySamples = static_cast<uint>(StdVector<Real>::getVector()->size());
    return ROL::makePtr<DualProbabilityVector>(
           ROL::makePtr<std::vector<Real>>(numMySamples),
           BatchStdVector<Real>::getBatchManager(),scale_);
  }

  const Vector<Real> & dual(void) const {
    uint numMySamples = static_cast<uint>(StdVector<Real>::getVector()->size());
    if ( !isDualInitialized_ ) {
      primal_vec_ = ROL::makePtr<PrimalProbabilityVector<Real>>(
                    ROL::makePtr<std::vector<Real>>(numMySamples),
                    BatchStdVector<Real>::getBatchManager(),scale_);
      isDualInitialized_ = true;
    }
    for (uint i = 0; i < numMySamples; i++) {
      (*(primal_vec_->getVector()))[i]
        = (*StdVector<Real>::getVector())[i]/(*scale_)[i];
    }
    return *primal_vec_;
  }
};

} // namespace ROL

#endif