AbstractSingleObjectiveOptimizer.h

Go to the documentation of this file.

//===========================================================================
/*!
 * 
 *
 * \brief       AbstractSingleObjectiveOptimizer
 * 
 * 
 *
 * \author      T.Voss, T. Glasmachers, O.Krause
 * \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_ABSTRACTSINGLEOBJECTIVEOPTIMIZER_H
#define SHARK_OBJECTIVEFUNCTIONS_ABSTRACTSINGLEOBJECTIVEOPTIMIZER_H
 
#include <shark/Algorithms/AbstractOptimizer.h>
#include <shark/Core/ResultSets.h>
 
namespace shark {
    
    ///\defgroup gradientopt Gradient-based Single objective optimizers
    ///\ingroup optimizers
    /// Gradient-Based optimizers use the gradient of an objective function to find a local minimum in the search space.
    /// If a function is not convex this local optimum might not be the global one.
    
    ///\defgroup singledirect Single-objective Direct-Search optimizers
    ///\ingroup optimizers
    /// Group of optimization algorithms that find the local optimum of a function without using gradient information, only
    /// the function values are used.
    
    ///\brief Base class for all single objective optimizer
    ///
    /// This class is a spezialization of the AbstractOptimizer itnerface for the class of single objective optimizers. A single objective optimizer is an optimizer
    /// which can only optimize functions with a single objective. This is the default case for most optimisation problems. 
    /// the class requires the ObjectiveFunction to provide a feasible starting point. If this is not possible, a second version of init is provided where the starting point can be 
    /// explicitely defined. 
    /// The Return type of an SingleObjectiveOptimizer is the SingleObjectiveResultSet which is a struct returning the best value of the function and together with it's point.
    /// \ingroup optimizers
    template<class PointType>
    class AbstractSingleObjectiveOptimizer: public AbstractOptimizer<PointType,double,SingleObjectiveResultSet<PointType> >{
    private:
        typedef AbstractOptimizer<PointType,double,SingleObjectiveResultSet<PointType> > base_type;
    public:
        typedef typename base_type::SearchPointType SearchPointType;
        typedef typename base_type::SolutionType SolutionType;
        typedef typename base_type::ResultType ResultType;
        typedef typename base_type::ObjectiveFunctionType ObjectiveFunctionType;
 
        ///\brief By default most single objective optimizers only require a single point
        std::size_t numInitPoints() const{
            return 1;
        }
        
        using base_type::init;
        
        /// \brief Initialize the optimizer for the supplied objective function using a set of initialisation points
        ///
        /// The default implementation picks either the first point in the set, or if it is enmpty, trys
        /// to generate one from the function.
        ///
        /// Be aware that function.init() has to be called before calling this function!
        ///
        /// \param [in] function The objective function to initialize for.
        /// \param [in] initPoints points used for initialisation. Should be at least numInitPoints().
        virtual void init( ObjectiveFunctionType const& function, std::vector<SearchPointType> const& initPoints ){
            if(initPoints.empty())
                init(function);
            else
                init(function,initPoints[0]);
        }
        
        ///initializes the optimizer using a predefined starting point
        virtual void init(ObjectiveFunctionType const& function, SearchPointType const& startingPoint)=0;
        ///returns the current solution of the optimizer
        virtual const SolutionType& solution() const{
            return m_best;
        }
 
    protected:
        
        SolutionType m_best; ///<Current solution of the optimizer
    };
 
}
#endif