/usr/include/ITK-4.9/itkGradientDescentLineSearchOptimizerv4.h is in libinsighttoolkit4-dev 4.9.0-4ubuntu1.
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*
* Copyright Insight Software Consortium
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*=========================================================================*/
#ifndef itkGradientDescentLineSearchOptimizerv4_h
#define itkGradientDescentLineSearchOptimizerv4_h
#include "itkGradientDescentOptimizerv4.h"
#include "itkOptimizerParameterScalesEstimator.h"
#include "itkWindowConvergenceMonitoringFunction.h"
namespace itk
{
/** \class GradientDescentLineSearchOptimizerv4Template
* \brief Gradient descent optimizer with a golden section line search.
*
* GradientDescentLineSearchOptimizer implements a simple gradient descent optimizer
* that is followed by a line search to find the best value for the learning rate.
* At each iteration the current position is updated according to
*
* \f[
* p_{n+1} = p_n
* + \mbox{learningRateByGoldenSectionLineSearch}
\, \frac{\partial f(p_n) }{\partial p_n}
* \f]
*
* Options are identical to the superclass's except for:
*
* options Epsilon, LowerLimit and UpperLimit that will guide
* a golden section line search to find the optimal gradient update
* within the range :
*
* [ learningRate * LowerLimit , learningRate * UpperLimit ]
*
* where Epsilon sets the resolution of the search. Smaller values
* lead to additional computation time but better localization of
* the minimum.
*
* By default, this optimizer will return the best value and associated
* parameters that were calculated during the optimization.
* See SetReturnBestParametersAndValue().
*
* \ingroup ITKOptimizersv4
*/
template<typename TInternalComputationValueType>
class GradientDescentLineSearchOptimizerv4Template
: public GradientDescentOptimizerv4Template<TInternalComputationValueType>
{
public:
/** Standard class typedefs. */
typedef GradientDescentLineSearchOptimizerv4Template Self;
typedef GradientDescentOptimizerv4Template<TInternalComputationValueType> Superclass;
typedef SmartPointer< Self > Pointer;
typedef SmartPointer< const Self > ConstPointer;
/** Run-time type information (and related methods). */
itkTypeMacro(GradientDescentLineSearchOptimizerv4Template, Superclass);
/** New macro for creation of through a Smart Pointer */
itkNewMacro(Self);
/** It should be possible to derive the internal computation type from the class object. */
typedef TInternalComputationValueType InternalComputationValueType;
/** Derivative type */
typedef typename Superclass::DerivativeType DerivativeType;
/** Metric type over which this class is templated */
typedef typename Superclass::MeasureType MeasureType;
typedef typename Superclass::ParametersType ParametersType;
/** Type for the convergence checker */
typedef itk::Function::WindowConvergenceMonitoringFunction<TInternalComputationValueType> ConvergenceMonitoringType;
/** The epsilon determines the accuracy of the line search
* i.e. the energy alteration that is considered convergent.
*/
itkSetMacro( Epsilon , TInternalComputationValueType );
itkGetMacro( Epsilon , TInternalComputationValueType );
/** The upper and lower limit below determine the range
* of values over which the learning rate can be adjusted
* by the golden section line search. The update can then
* occur in the range from the smallest change given by :
* NewParams = OldParams + LowerLimit * gradient
* to the largest change given by :
* NewParams = OldParams + UpperLimit * gradient
* Reasonable values might be 0 and 2.
*/
itkSetMacro( LowerLimit , TInternalComputationValueType );
itkGetMacro( LowerLimit , TInternalComputationValueType );
itkSetMacro( UpperLimit , TInternalComputationValueType );
itkGetMacro( UpperLimit , TInternalComputationValueType );
itkSetMacro( MaximumLineSearchIterations , unsigned int );
itkGetMacro( MaximumLineSearchIterations , unsigned int );
protected:
/** Advance one Step following the gradient direction.
* Includes transform update. */
virtual void AdvanceOneStep(void) ITK_OVERRIDE;
/** Default constructor */
GradientDescentLineSearchOptimizerv4Template();
/** Destructor */
virtual ~GradientDescentLineSearchOptimizerv4Template();
virtual void PrintSelf( std::ostream & os, Indent indent ) const ITK_OVERRIDE;
TInternalComputationValueType GoldenSectionSearch( TInternalComputationValueType a, TInternalComputationValueType b, TInternalComputationValueType c );
TInternalComputationValueType m_LowerLimit;
TInternalComputationValueType m_UpperLimit;
TInternalComputationValueType m_Phi;
TInternalComputationValueType m_Resphi;
TInternalComputationValueType m_Epsilon;
/** Controls the maximum recursion depth for the golden section search */
unsigned int m_MaximumLineSearchIterations;
/** Counts the recursion depth for the golden section search */
unsigned int m_LineSearchIterations;
private:
GradientDescentLineSearchOptimizerv4Template( const Self & ) ITK_DELETE_FUNCTION;
void operator=( const Self& ) ITK_DELETE_FUNCTION;
};
/** This helps to meet backward compatibility */
typedef GradientDescentLineSearchOptimizerv4Template<double> GradientDescentLineSearchOptimizerv4;
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
#include "itkGradientDescentLineSearchOptimizerv4.hxx"
#endif
#endif
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