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// Copyright (C) 2016 EDF
// All Rights Reserved
// This code is published under the GNU Lesser General Public License (GNU LGPL)
#ifndef OPTIMIZERBASE_H
#define OPTIMIZERBASE_H
#include <Eigen/Dense>
/** \file OptimizerBase.h
* \brief Define an abstract class for Dynamic Programming problems solved by Monte Carlo methods
* \author Xavier Warin
*/
namespace StOpt
{
/// \class OptimizerBase OptimizerBase.h
/// Base class for optimizer for Dynamic Programming with and without regression methods
class OptimizerBase
{
public :
OptimizerBase() {}
virtual ~OptimizerBase() {}
/// \brief defines the dimension to split for MPI parallelism
/// For each dimension return true is the direction can be split
virtual Eigen::Array< bool, Eigen::Dynamic, 1> getDimensionToSplit() const = 0 ;
/// \brief defines the diffusion cone for parallelism
/// \param p_regionByProcessor region (min max) treated by the processor for the different regimes treated
/// \return returns in each dimension the min max values in the stock that can be reached from the grid p_gridByProcessor for each regime
virtual std::vector< std::array< double, 2> > getCone(const std::vector< std::array< double, 2> > &p_regionByProcessor) const = 0;
/// \brief Get the number of regimes allowed for the asset to be reached at the current time step
virtual int getNbRegime() const = 0 ;
/// \brief get back the dimension of the control
virtual int getNbControl() const = 0 ;
/// \brief get size of the function to follow in simulation
virtual int getSimuFuncSize() const = 0;
};
}
#endif /* OPTIMIZERBASE_H */
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