/usr/include/mia-2.2/mia/core/minimizer.hh is in libmia-2.2-dev 2.2.2-1+b1.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 | /* -*- mia-c++ -*-
*
* This file is part of MIA - a toolbox for medical image analysis
* Copyright (c) Leipzig, Madrid 1999-2014 Gert Wollny
*
* MIA is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* This program 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 General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with MIA; if not, see <http://www.gnu.org/licenses/>.
*
*/
#ifndef mia_core_minimizer_hh
#define mia_core_minimizer_hh
#include <memory>
#include <vector>
#include <mia/core/factory.hh>
#include <mia/core/handler.hh>
#include <mia/core/vector.hh>
NS_MIA_BEGIN
/**
\ingroup basic
\brief A class for generalized minimization problems
This class defined the interface for a generalized minimizer that may use
gradient information. Specific implementations are provided as plug-ins.
*/
class EXPORT_CORE CMinimizer : public CProductBase {
public:
/// plug-in searchpath typedef helper
typedef CMinimizer plugin_data;
/// plug-in searchpath typedef helper
typedef CMinimizer plugin_type;
/// Pointer type of this minimizer
typedef std::shared_ptr<CMinimizer> Pointer;
/// enum to describe whether optimization succeeded
enum EMinimizerResult {failure, /**< optimization failed */
success /**< optimization succeeded */
};
/// plug-in searchpath helper
static const char *const type_descr;
/// plug-in searchpath helper
static const char *const data_descr;
/**
\brief Base class for all optimization problems that can be run
by CMinimizer
This is the abstract base class for all optimization problems that can be run by
CMinimizer. In order to implement a real optimizable problem, the abstract functions
- virtual double do_f(const CDoubleVector& x) = 0;
- virtual void do_df(const CDoubleVector& x, CDoubleVector& g) = 0;
- virtual double do_fdf(const CDoubleVector& x, CDoubleVector& g) = 0;
have to be implemented in the derived class.
These functions must provide the functionality as documented for the public member functions
- double f(size_t n, const double *x);
- void df(size_t n, const double *x, double *g);
- double fdf(size_t n, const double *x, double *g);
*/
class Problem : public CPropertyFlagHolder{
public:
virtual ~Problem();
/**
The function that is called by the optimizer to evaluate the
objective function value.
@param n number of parameters
@param x vector of parameters
@returns function value
*/
double f(size_t n, const double *x);
/**
The function that is called by the optimizer to evaluate the
objective function gradient.
@param n number of parameters
@param x vector of parameters
@param[out] g vector of gradient of the objective function
*/
void df(size_t n, const double *x, double *g);
/**
The function that is called by the optimizer to evaluate the
objective function gradient and the function value.
@param n number of parameters
@param x vector of parameters
@param[out] g vector of gradient of the objective function
@returns objective function value
*/
double fdf(size_t n, const double *x, double *g);
/**
The function that is called by the optimizer to evaluate the
objective function value.
@param x vector of parameters
@returns function value
*/
double f(const std::vector<double>& x);
/**
The function that is called by the optimizer to evaluate the
objective function gradient.
@param x vector of parameters
@param[out] g vector of gradient of the objective function
*/
void df(const std::vector<double>& x, std::vector<double>& g);
/**
The function that is called by the optimizer to evaluate the
objective function gradient and the function value.
@param x vector of parameters
@param[out] g vector of gradient of the objective function
@returns objective function value
*/
double fdf(const std::vector<double>& x, std::vector<double>& g);
/**
The function that is called by the optimizer to evaluate the
objective function value.
@param x vector of parameters
@returns function value
*/
double f(const CDoubleVector& x);
/**
The function that is called by the optimizer to evaluate the
objective function gradient.
@param x vector of parameters
@param[out] g vector of gradient of the objective function
*/
void df(const CDoubleVector& x, CDoubleVector& g);
/**
The function that is called by the optimizer to evaluate the
objective function gradient and the function value.
@param x vector of parameters
@param[out] g vector of gradient of the objective function
@returns objective function value
*/
double fdf(const CDoubleVector& x, CDoubleVector& g);
/// \returns number of parameters to optimize
size_t size() const;
private:
virtual double do_f(const CDoubleVector& x) = 0;
virtual void do_df(const CDoubleVector& x, CDoubleVector& g) = 0;
virtual double do_fdf(const CDoubleVector& x, CDoubleVector& g) = 0;
virtual size_t do_size() const = 0;
};
/// pointer type for the optimization problem
typedef std::shared_ptr<Problem> PProblem;
/**
Construtor of the optimizer.
*/
CMinimizer();
/**
Set the optimization problem
\param x problem to be optimized
*/
void set_problem(PProblem x);
virtual ~CMinimizer();
/**
Run the optimization
\param[in,out] x at entry contains the start point of the optimization at exit the optimized value
\returns returns a status whether the optimization succeeded or why it stopped
*/
int run(CDoubleVector& x);
protected:
/// @returns a raw pointer to the optimization problem
Problem *get_problem_pointer();
/// @returns the size (degrees of freedom) of the optimization problem
size_t size() const;
/// \returns a read/write reference to the current optimization problem
Problem& get_problem();
private:
virtual void do_set_problem();
virtual int do_run(CDoubleVector& x) = 0;
PProblem m_problem;
};
/// Pointer type for the CMinimizer class
typedef CMinimizer::Pointer PMinimizer;
/// Base class for the CMinimizer creator plugins
typedef TFactory<CMinimizer> CMinimizerPlugin;
/// The minimizer plugin handler
typedef THandlerSingleton<TFactoryPluginHandler<CMinimizerPlugin> > CMinimizerPluginHandler;
/// Trait to make the minimizer definition parsable on the command line
FACTORY_TRAIT(CMinimizerPluginHandler);
inline CMinimizer::Problem& CMinimizer::get_problem()
{
return *m_problem;
}
inline
PMinimizer produce_minimizer(const std::string& descr)
{
return CMinimizerPluginHandler::instance().produce(descr);
}
NS_MIA_END
#endif
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