SteepestDescent.h

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//===========================================================================
/*!
 * 
 *
 * \brief       SteepestDescent
 * 
 * 
 *
 * \author      O. Krause
 * \date        2010
 *
 *
 * \par Copyright 1995-2017 Shark Development Team
 * 
 * <BR><HR>
 * This file is part of Shark.
 * <https://shark-ml.github.io/Shark/>
 * 
 * Shark is free software: you can redistribute it and/or modify
 * it under the terms of the GNU Lesser General Public License as published 
 * by the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 * 
 * Shark is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU Lesser General Public License for more details.
 * 
 * You should have received a copy of the GNU Lesser General Public License
 * along with Shark.  If not, see <http://www.gnu.org/licenses/>.
 *
 */
//===========================================================================
#ifndef SHARK_ML_OPTIMIZER_STEEPESTDESCENT_H
#define SHARK_ML_OPTIMIZER_STEEPESTDESCENT_H
 
#include <shark/Algorithms/AbstractSingleObjectiveOptimizer.h>
 
namespace shark{
 
///@brief Standard steepest descent.
/// \ingroup gradientopt
template<class SearchPointType = RealVector>
class SteepestDescent : public AbstractSingleObjectiveOptimizer<SearchPointType>
{
public:
    typedef AbstractObjectiveFunction<SearchPointType,double> ObjectiveFunctionType;
    SteepestDescent() {
        this->m_features |= this->REQUIRES_FIRST_DERIVATIVE;
 
        m_learningRate = 0.1;
        m_momentum = 0.0;
    }
 
    /// \brief From INameable: return the class name.
    std::string name() const
    { return "SteepestDescent"; }
 
    void init(ObjectiveFunctionType const& objectiveFunction, SearchPointType const& startingPoint) {
        this->checkFeatures(objectiveFunction);
        SHARK_RUNTIME_CHECK(startingPoint.size() == objectiveFunction.numberOfVariables(), "Initial starting point and dimensionality of function do not agree");
        
        m_path.resize(startingPoint.size());
        m_path.clear();
        this->m_best.point = startingPoint;
        this->m_best.value = objectiveFunction.evalDerivative(this->m_best.point,m_derivative);
    }
    using AbstractSingleObjectiveOptimizer<SearchPointType >::init;
 
    /*!
     *  \brief get learning rate
     */
    double learningRate() const {
        return m_learningRate;
    }
 
    /*!
     *  \brief set learning rate
     */
    void setLearningRate(double learningRate) {
        m_learningRate = learningRate;
    }
 
    /*!
     *  \brief get momentum parameter
     */
    double momentum() const {
        return m_momentum;
    }
 
    /*!
     *  \brief set momentum parameter
     */
    void setMomentum(double momentum) {
        m_momentum = momentum;
    }
    /*!
     *  \brief updates searchdirection and then does simple gradient descent
     */
    void step(ObjectiveFunctionType const& objectiveFunction) {
        m_path = -m_learningRate * m_derivative + m_momentum * m_path;
        this->m_best.point+=m_path;
        this->m_best.value = objectiveFunction.evalDerivative(this->m_best.point,m_derivative);
    }
    virtual void read( InArchive & archive )
    {
        archive>>m_path;
        archive>>m_learningRate;
        archive>>m_momentum;
    }
 
    virtual void write( OutArchive & archive ) const
    {
        archive<<m_path;
        archive<<m_learningRate;
        archive<<m_momentum;
    }
 
private:
    SearchPointType m_path;
    SearchPointType m_derivative;
    double m_learningRate;
    double m_momentum;
};
 
}
#endif