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/*=========================================================================
*
* Copyright NumFOCUS
*
* 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
*
* https://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 ITK_TEMPLATE_EXPORT GradientDescentLineSearchOptimizerv4Template
: public GradientDescentOptimizerv4Template<TInternalComputationValueType>
{
public:
ITK_DISALLOW_COPY_AND_MOVE(GradientDescentLineSearchOptimizerv4Template);
/** Standard class type aliases. */
using Self = GradientDescentLineSearchOptimizerv4Template;
using Superclass = GradientDescentOptimizerv4Template<TInternalComputationValueType>;
using Pointer = SmartPointer<Self>;
using ConstPointer = SmartPointer<const Self>;
/** \see LightObject::GetNameOfClass() */
itkOverrideGetNameOfClassMacro(GradientDescentLineSearchOptimizerv4Template);
/** 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. */
using InternalComputationValueType = TInternalComputationValueType;
/** Derivative type */
using typename Superclass::DerivativeType;
/** Metric type over which this class is templated */
using typename Superclass::MeasureType;
using typename Superclass::ParametersType;
/** Type for the convergence checker */
using ConvergenceMonitoringType = itk::Function::WindowConvergenceMonitoringFunction<TInternalComputationValueType>;
/** 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. */
void
AdvanceOneStep() override;
/** Default constructor */
GradientDescentLineSearchOptimizerv4Template();
/** Destructor */
~GradientDescentLineSearchOptimizerv4Template() override = default;
void
PrintSelf(std::ostream & os, Indent indent) const override;
/** Search the golden section.
*
* \p a and \p c are the current bounds; the minimum is between them.
* \p b is a center point.
* \c f(x) is some mathematical function elsewhere defined.
* \p a corresponds to \c x1; \p b corresponds to \c x2; \p c corresponds to \c x3.
* \c x corresponds to \c x4.
*/
TInternalComputationValueType
GoldenSectionSearch(TInternalComputationValueType a,
TInternalComputationValueType b,
TInternalComputationValueType c,
TInternalComputationValueType metricb = NumericTraits<TInternalComputationValueType>::max());
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{};
};
/** This helps to meet backward compatibility */
using GradientDescentLineSearchOptimizerv4 = GradientDescentLineSearchOptimizerv4Template<double>;
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
# include "itkGradientDescentLineSearchOptimizerv4.hxx"
#endif
#endif
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