SteepestDescent.h
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1 //===========================================================================
2 /*!
3  *
4  *
5  * \brief SteepestDescent
6  *
7  *
8  *
9  * \author O. Krause
10  * \date 2010
11  *
12  *
13  * \par Copyright 1995-2017 Shark Development Team
14  *
15  * <BR><HR>
16  * This file is part of Shark.
17  * <http://shark-ml.org/>
18  *
19  * Shark is free software: you can redistribute it and/or modify
20  * it under the terms of the GNU Lesser General Public License as published
21  * by the Free Software Foundation, either version 3 of the License, or
22  * (at your option) any later version.
23  *
24  * Shark is distributed in the hope that it will be useful,
25  * but WITHOUT ANY WARRANTY; without even the implied warranty of
26  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
27  * GNU Lesser General Public License for more details.
28  *
29  * You should have received a copy of the GNU Lesser General Public License
30  * along with Shark. If not, see <http://www.gnu.org/licenses/>.
31  *
32  */
33 //===========================================================================
34 #ifndef SHARK_ML_OPTIMIZER_STEEPESTDESCENT_H
35 #define SHARK_ML_OPTIMIZER_STEEPESTDESCENT_H
36 
37 #include <shark/Algorithms/AbstractSingleObjectiveOptimizer.h>
38 
39 namespace shark{
40 
41 ///@brief Standard steepest descent.
42 class SteepestDescent : public AbstractSingleObjectiveOptimizer<RealVector >
43 {
44 public:
45  SteepestDescent() {
46  m_features |= REQUIRES_FIRST_DERIVATIVE;
47 
48  m_learningRate = 0.1;
49  m_momentum = 0.0;
50  }
51 
52  /// \brief From INameable: return the class name.
53  std::string name() const
54  { return "SteepestDescent"; }
55 
56  void init(ObjectiveFunctionType const& objectiveFunction, SearchPointType const& startingPoint) {
57  checkFeatures(objectiveFunction);
58  SHARK_RUNTIME_CHECK(startingPoint.size() == objectiveFunction.numberOfVariables(), "Initial starting point and dimensionality of function do not agree");
59 
60  m_path.resize(startingPoint.size());
61  m_path.clear();
62  m_best.point = startingPoint;
63  m_best.value = objectiveFunction.evalDerivative(m_best.point,m_derivative);
64  }
65  using AbstractSingleObjectiveOptimizer<RealVector >::init;
66 
67  /*!
68  * \brief get learning rate
69  */
70  double learningRate() const {
71  return m_learningRate;
72  }
73 
74  /*!
75  * \brief set learning rate
76  */
77  void setLearningRate(double learningRate) {
78  m_learningRate = learningRate;
79  }
80 
81  /*!
82  * \brief get momentum parameter
83  */
84  double momentum() const {
85  return m_momentum;
86  }
87 
88  /*!
89  * \brief set momentum parameter
90  */
91  void setMomentum(double momentum) {
92  m_momentum = momentum;
93  }
94  /*!
95  * \brief updates searchdirection and then does simple gradient descent
96  */
97  void step(ObjectiveFunctionType const& objectiveFunction) {
98  m_path = -m_learningRate * m_derivative + m_momentum * m_path;
99  m_best.point+=m_path;
100  m_best.value = objectiveFunction.evalDerivative(m_best.point,m_derivative);
101  }
102  virtual void read( InArchive & archive )
103  {
104  archive>>m_path;
105  archive>>m_learningRate;
106  archive>>m_momentum;
107  }
108 
109  virtual void write( OutArchive & archive ) const
110  {
111  archive<<m_path;
112  archive<<m_learningRate;
113  archive<<m_momentum;
114  }
115 
116 private:
117  RealVector m_path;
118  ObjectiveFunctionType::FirstOrderDerivative m_derivative;
119  double m_learningRate;
120  double m_momentum;
121 };
122 
123 }
124 #endif
125