ConstrainedSphere.h

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/*!
 * 
 *
 * \brief       Convex quadratic benchmark function.
 * 
 *
 * \author      T. Voss
 * \date        2010-2011
 *
 *
 * \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_BENCHMARK_CONSTRAINEDSPHERE_H
#define SHARK_OBJECTIVEFUNCTIONS_BENCHMARK_CONSTRAINEDSPHERE_H

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

namespace shark {
/**
 * \brief Constrained Sphere function
 *
 * This is a simple sphere function minimizing \f$ f(x) = \sum_i^N x_i^2-m \f$ under the constraints that
 *  \f$ x_i \geq 1\f$ for \f$ i = 1,\dots,m \f$. The minimum is at \f$ x_1=\dots = x_m = 1\f$ and 
 * \f$ x_{m+1}=\dots = x_N = 0 \f$ with function value 0.
 *
 * This is a simple benchmark for evolutionary algorithms as, the closer the algorithm is to the optimu
 */
struct ConstrainedSphere : public SingleObjectiveFunction {
    
    ConstrainedSphere(std::size_t numberOfVariables = 5, std::size_t m = 1)
    :m_numberOfVariables(numberOfVariables), m_constraints(m) {
        m_features |= CAN_PROPOSE_STARTING_POINT;
        m_features |= IS_CONSTRAINED_FEATURE;
        m_features |= IS_THREAD_SAFE;
    }

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

    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 < m_constraints; i++) {
            x(i) = std::abs(random::gauss(*mep_rng, 0, 1))+1;
        }
        for (std::size_t i = m_constraints; i < x.size(); i++) {
            x(i) = random::gauss(0, 1);
        }
        return x;
    }
    
    bool isFeasible( SearchPointType const& input) const {
        for (std::size_t i = 0; i < m_constraints; i++) {
            if(input(i) < 1) return false;
        }
        return true;
    }

    double eval(const SearchPointType &p) const {
        m_evaluationCounter++;
        return norm_sqr(p)-m_constraints;
    }
private:
    std::size_t m_numberOfVariables;
    std::size_t m_constraints;
};

}

#endif