SimplexDownhill.h

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//===========================================================================
/*!
 *
 *
 * \brief       Nelder-Mead Simplex Downhill Method
 *
 *
 *
 * \author      T. Glasmachers
 * \date        2015
 *
 *
 * \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_ALGORITHMS_SIMPLEXDOWNHILL_H
#define SHARK_ALGORITHMS_SIMPLEXDOWNHILL_H
 
 
#include <shark/Algorithms/AbstractSingleObjectiveOptimizer.h>
#include <boost/serialization/vector.hpp>
#include <vector>
 
 
namespace shark {
 
///
/// \brief Simplex Downhill Method
///
/// \par
/// The Nelder-Mead Simplex Downhill Method is a deterministic direct
/// search method. It is known to perform quite well in low dimensions,
/// at least for local search.
///
/// \par
/// The implementation of the algorithm is along the lines of the
/// Wikipedia article
/// https://en.wikipedia.org/wiki/Nelder%E2%80%93Mead_method
/// \ingroup singledirect
class SimplexDownhill : public AbstractSingleObjectiveOptimizer<RealVector >
{
public:
    /// \brief Default Constructor.
    SimplexDownhill()
    { }
 
    /// \brief From INameable: return the class name.
    std::string name() const
    { return "SimplexDownhill"; }
 
    //from ISerializable
    virtual void read( InArchive & archive )
    {
        archive >> m_simplex;
        archive >> m_best.point;
        archive >> m_best.value;
    }
 
    virtual void write( OutArchive & archive ) const
    {
        archive << m_simplex;
        archive << m_best.point;
        archive << m_best.value;
    }
 
    /// \brief Initialization of the optimizer.
    ///
    /// The initial simplex is created is a distance of about one around the proposed starting point.
    ///
    virtual void init(ObjectiveFunctionType const& objectiveFunction, SearchPointType const& startingPoint)
    {
        checkFeatures(objectiveFunction);
        size_t dim = startingPoint.size();
 
        // create the initial simplex
        m_best.value = 1e100;
        m_simplex = std::vector<SolutionType>(dim + 1);
        for (size_t j=0; j<=dim; j++)
        {
            RealVector p(dim);
            for (size_t i=0; i<dim; i++) p(i) = startingPoint(i) + ((i == j) ? 1.0 : -0.5);
            m_simplex[j].point = p;
            m_simplex[j].value = objectiveFunction.eval(p);
            if (m_simplex[j].value < m_best.value) m_best = m_simplex[j];
        }
    }
    using AbstractSingleObjectiveOptimizer<RealVector>::init;
 
    /// \brief Step of the simplex algorithm.
    void step(ObjectiveFunctionType const& objectiveFunction)
    {
        size_t dim = m_simplex.size() - 1;
 
        // step of the simplex algorithm
        sort(m_simplex.begin(), m_simplex.end());
        SolutionType& best = m_simplex[0];
        SolutionType& worst = m_simplex[dim];
 
        // compute centroid
        RealVector x0(dim, 0.0);
        for (size_t j=0; j<dim; j++) x0 += m_simplex[j].point;
        x0 /= (double)dim;
 
        // reflection
        SolutionType xr;
        xr.point = 2.0 * x0 - worst.point;
        xr.value = objectiveFunction(xr.point);
        if (xr.value < m_best.value) m_best = xr;   // keep track of best point
        if (best.value <= xr.value && xr.value < m_simplex[dim-1].value)
        {
            // replace worst point with reflected point
            worst = xr;
        }
        else if (xr.value < best.value)
        {
            // expansion
            SolutionType xe;
            xe.point = 3.0 * x0 - 2.0 * worst.point;
            xe.value = objectiveFunction(xe.point);
            if (xe.value < m_best.value) m_best = xe;   // keep track of best point
            if (xe.value < xr.value)
            {
                // replace worst point with expanded point
                worst = xe;
            }
            else
            {
                // replace worst point with reflected point
                worst = xr;
            }
        }
        else
        {
            // contraction
            SolutionType xc;
            xc.point = 0.5 * x0 + 0.5 * worst.point;
            xc.value = objectiveFunction(xc.point);
            if (xc.value < m_best.value) m_best = xc;   // keep track of best point
            if (xc.value < worst.value)
            {
                // replace worst point with contracted point
                worst = xc;
            }
            else
            {
                // reduction
                for (size_t j=1; j<=dim; j++)
                {
                    m_simplex[j].point = 0.5 * best.point + 0.5 * m_simplex[j].point;
                    m_simplex[j].value = objectiveFunction(m_simplex[j].point);
                    if (m_simplex[j].value < m_best.value) m_best = m_simplex[j];   // keep track of best point
                }
            }
        }
    }
 
    /// \brief Read access to the current simplex.
    std::vector<SolutionType> const& simplex()
    { return m_simplex; }
 
protected:
    std::vector<SolutionType> m_simplex;       ///< \brief Current simplex (algorithm state).
};
 
 
}
#endif