Cigar.h

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/*!
 * 
 *
 * \brief       Convex quadratic benchmark function with single dominant axis

 * 
 *
 * \author      -
 * \date        -
 *
 *
 * \par Copyright 1995-2017 Shark Development Team
 * 
 * <BR><HR>
 * This file is part of Shark.
 * <http://shark-ml.org/>
 * 
 * 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_OBJECTIVEFUNCTIONS_BENCHMARKS_CIGAR_H
#define SHARK_OBJECTIVEFUNCTIONS_BENCHMARKS_CIGAR_H

#include <shark/ObjectiveFunctions/AbstractObjectiveFunction.h>
#include <shark/Core/Random.h>

namespace shark {
/**
 * \brief Convex quadratic benchmark function with single dominant axis
 */
struct Cigar : public SingleObjectiveFunction {

    Cigar(std::size_t numberOfVariables = 5, double alpha=1.E-3) : m_alpha(alpha) {
        m_features |= CAN_PROPOSE_STARTING_POINT;
        m_features |= HAS_FIRST_DERIVATIVE;
        m_numberOfVariables = numberOfVariables;
    }

    /// \brief From INameable: return the class name.
    std::string name() const
    { return "Cigar"; }

    std::size_t numberOfVariables()const{
        return m_numberOfVariables;
    }
    
    bool hasScalableDimensionality()const{
        return true;
    }
    
    void setNumberOfVariables( std::size_t numberOfVariables ){
        m_numberOfVariables = numberOfVariables;
    }

    SearchPointType proposeStartingPoint() const {
        RealVector x(numberOfVariables());

        for (std::size_t i = 0; i < x.size(); i++) {
            x(i) = random::uni(*mep_rng, 0, 1);
        }
        return x;
    }

    double eval(const SearchPointType &p) const {
        m_evaluationCounter++;

        double sum = m_alpha * sqr(p(0));
        for (std::size_t i = 1; i < p.size(); i++)
            sum +=  sqr(p(i));

        return sum;
    }
    double evalDerivative(SearchPointType const& p, FirstOrderDerivative & derivative ) const {
        derivative.resize(p.size());
        noalias(derivative) = 2* p;
        derivative(0) = 2 * m_alpha * p(0);
        return eval(p);
    }

    double alpha() const {
        return m_alpha;
    }

    void setAlpha(double alpha) {
        m_alpha = alpha;
    }

private:
    double m_alpha;
    std::size_t m_numberOfVariables;
};
}

#endif // SHARK_EA_CIGAR_H