CG.h
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1 //===========================================================================
2 /*!
3  *
4  *
5  * \brief CG
6  *
7  * Conjugate-gradient method for unconstraint optimization.
8  *
9  *
10  *
11  * \author O. Krause
12  * \date 2010
13  *
14  *
15  * \par Copyright 1995-2017 Shark Development Team
16  *
17  * <BR><HR>
18  * This file is part of Shark.
19  * <http://shark-ml.org/>
20  *
21  * Shark is free software: you can redistribute it and/or modify
22  * it under the terms of the GNU Lesser General Public License as published
23  * by the Free Software Foundation, either version 3 of the License, or
24  * (at your option) any later version.
25  *
26  * Shark is distributed in the hope that it will be useful,
27  * but WITHOUT ANY WARRANTY; without even the implied warranty of
28  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
29  * GNU Lesser General Public License for more details.
30  *
31  * You should have received a copy of the GNU Lesser General Public License
32  * along with Shark. If not, see <http://www.gnu.org/licenses/>.
33  *
34  */
35 //===========================================================================
36 
37 #ifndef SHARK_ML_OPTIMIZER_CG_H
38 #define SHARK_ML_OPTIMIZER_CG_H
39 
40 #include <shark/Core/DLLSupport.h>
41 #include <shark/Algorithms/GradientDescent/AbstractLineSearchOptimizer.h>
42 
43 namespace shark {
44 /// \brief Conjugate-gradient method for unconstrained optimization
45 ///
46 /// The next CG search Direction p_{k+1} is computed using the current gradient g_k by
47 /// \f$ p_{k+1} = \beta p_k - g_k \f$
48 /// where beta can be computed using different formulas
49 /// well known is the Fletcher - Reeves method:
50 /// \f$ \beta = ||g_k||2/ ||g_{k-1}||^2 \f$
51 /// we use
52 /// \f$ \beta = ||g_k||^2 /<p_k,g_k-g_{k-1}> \f$
53 /// which is formula 5.49 in Nocedal, Wright - Numerical Optimization.
54 /// This formula has better numerical properties than Fletcher-Reeves for non-quadratic functions
55 /// while ensuring a descent direction.
56 ///
57 /// We implement restarting to ensure quadratic convergence near the optimum as well as numerical stability
58 class CG : public AbstractLineSearchOptimizer{
59 protected:
60  SHARK_EXPORT_SYMBOL void initModel();
61  SHARK_EXPORT_SYMBOL void computeSearchDirection(ObjectiveFunctionType const& objectiveFunction);
62 public:
63  std::string name() const
64  { return "CG"; }
65 
66  //from ISerializable
67  SHARK_EXPORT_SYMBOL void read( InArchive & archive );
68  SHARK_EXPORT_SYMBOL void write( OutArchive & archive ) const;
69 protected:
70  unsigned m_count;
71 };
72 
73 }
74 
75 #endif