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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 itkConjugateGradientLineSearchOptimizerv4_hxx
#define itkConjugateGradientLineSearchOptimizerv4_hxx
namespace itk
{
/**
*PrintSelf
*/
template <typename TInternalComputationValueType>
void
ConjugateGradientLineSearchOptimizerv4Template<TInternalComputationValueType>::PrintSelf(std::ostream & os,
Indent indent) const
{
Superclass::PrintSelf(os, indent);
}
template <typename TInternalComputationValueType>
void
ConjugateGradientLineSearchOptimizerv4Template<TInternalComputationValueType>::StartOptimization(
bool doOnlyInitialization)
{
this->m_ConjugateGradient.SetSize(this->m_Metric->GetNumberOfParameters());
this->m_ConjugateGradient.Fill(TInternalComputationValueType{});
this->m_LastGradient.SetSize(this->m_Metric->GetNumberOfParameters());
this->m_LastGradient.Fill(TInternalComputationValueType{});
Superclass::StartOptimization(doOnlyInitialization);
}
/**
* Advance one Step following the gradient direction
*/
template <typename TInternalComputationValueType>
void
ConjugateGradientLineSearchOptimizerv4Template<TInternalComputationValueType>::AdvanceOneStep()
{
itkDebugMacro("AdvanceOneStep");
this->ModifyGradientByScales();
if (this->m_CurrentIteration == 0)
{
this->EstimateLearningRate();
}
TInternalComputationValueType gamma{};
TInternalComputationValueType gammaDenom = inner_product(this->m_LastGradient, this->m_LastGradient);
if (gammaDenom > itk::NumericTraits<TInternalComputationValueType>::epsilon())
{
gamma = inner_product(this->m_Gradient - this->m_LastGradient, this->m_Gradient) / gammaDenom;
}
/** Modified Polak-Ribiere restart conditions */
if (gamma < 0 || gamma > 5)
{
gamma = 0;
}
this->m_LastGradient = this->m_Gradient;
this->m_ConjugateGradient = this->m_Gradient + this->m_ConjugateGradient * gamma;
this->m_Gradient = this->m_ConjugateGradient;
/* Estimate a learning rate for this step */
this->m_LineSearchIterations = 0;
this->m_LearningRate = this->GoldenSectionSearch(
this->m_LearningRate * this->m_LowerLimit, this->m_LearningRate, this->m_LearningRate * this->m_UpperLimit);
/* Begin threaded gradient modification of m_Gradient variable. */
this->ModifyGradientByLearningRate();
try
{
/* Pass gradient to transform and let it do its own updating. */
this->m_Metric->UpdateTransformParameters(this->m_Gradient);
}
catch (const ExceptionObject &)
{
this->m_StopCondition = StopConditionObjectToObjectOptimizerEnum::UPDATE_PARAMETERS_ERROR;
this->m_StopConditionDescription << "UpdateTransformParameters error";
this->StopOptimization();
// Pass exception to caller
throw;
}
this->InvokeEvent(IterationEvent());
}
} // namespace itk
#endif
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