Sphere.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.
 * <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_OBJECTIVEFUNCTIONS_BENCHMARK_SPHERE_H
#define SHARK_OBJECTIVEFUNCTIONS_BENCHMARK_SPHERE_H
 
#include <shark/ObjectiveFunctions/AbstractObjectiveFunction.h>
#include <shark/Core/Random.h>
 
namespace shark {namespace benchmarks{
/**
 * \brief Convex quadratic benchmark function.
*  \ingroup benchmarks
 */
struct Sphere : public SingleObjectiveFunction {
    
    Sphere(std::size_t numberOfVariables = 5):m_numberOfVariables(numberOfVariables) {
        m_features |= CAN_PROPOSE_STARTING_POINT;
        m_features |= HAS_FIRST_DERIVATIVE;
    }
 
    /// \brief From INameable: return the class name.
    std::string name() const
    { return "Sphere"; }
 
    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::gauss(*mep_rng, 0,1);
        }
        return x;
    }
 
    double eval(SearchPointType const& x) const {
        SIZE_CHECK(x.size() == numberOfVariables());
        m_evaluationCounter++;
        return norm_sqr(x);
    }
    
    double evalDerivative(SearchPointType const& x, FirstOrderDerivative& derivative) const {
        SIZE_CHECK(x.size() == numberOfVariables());
        m_evaluationCounter++;
        derivative.resize(x.size());
        noalias(derivative) = 2*x;
        return norm_sqr(x);
    }
private:
    std::size_t m_numberOfVariables;
};
 
}}
 
#endif