/*=========================================================================
 *
 *  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 itkConjugateGradientLineSearchOptimizerv4_h
#define itkConjugateGradientLineSearchOptimizerv4_h

#include "itkGradientDescentLineSearchOptimizerv4.h"
#include "itkOptimizerParameterScalesEstimator.h"
#include "itkWindowConvergenceMonitoringFunction.h"

namespace itk
{
  /** \class ConjugateGradientLineSearchOptimizerv4Template
   *  \brief Conjugate gradient descent optimizer with a golden section line search for nonlinear optimization.
   *
   * ConjugateGradientLineSearchOptimizer implements a conjugate 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}
   *                 \, d
   * \f]
   *
   * where d is defined as the Polak-Ribiere conjugate gradient.
   *
   * Options are identical to the superclass's.
   *
   * \ingroup ITKOptimizersv4
   */
template<typename TInternalComputationValueType>
class ITK_TEMPLATE_EXPORT ConjugateGradientLineSearchOptimizerv4Template
: public GradientDescentLineSearchOptimizerv4Template<TInternalComputationValueType>
{
public:
  ITK_DISALLOW_COPY_AND_ASSIGN(ConjugateGradientLineSearchOptimizerv4Template);

  /** Standard class type aliases. */
  using Self = ConjugateGradientLineSearchOptimizerv4Template;
  using Superclass = GradientDescentLineSearchOptimizerv4Template<TInternalComputationValueType>;
  using Pointer = SmartPointer< Self >;
  using ConstPointer = SmartPointer< const Self >;

  /** Run-time type information (and related methods). */
  itkTypeMacro(ConjugateGradientLineSearchOptimizerv4Template, 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. */
  using InternalComputationValueType = TInternalComputationValueType;

  /** Derivative type */
  using DerivativeType = typename Superclass::DerivativeType;

  /** Metric type over which this class is templated */
  using MeasureType = typename Superclass::MeasureType;

  /** Type for the convergence checker */
  using ConvergenceMonitoringType = itk::Function::WindowConvergenceMonitoringFunction<TInternalComputationValueType>;

  void StartOptimization( bool doOnlyInitialization = false ) override;

protected:

  /** Advance one Step following the gradient direction.
   * Includes transform update. */
  void AdvanceOneStep() override;

  /** Default constructor */
  ConjugateGradientLineSearchOptimizerv4Template() = default;

  /** Destructor */
  ~ConjugateGradientLineSearchOptimizerv4Template() override = default;

  void PrintSelf( std::ostream & os, Indent indent ) const override;

private:

  DerivativeType               m_LastGradient;
  DerivativeType               m_ConjugateGradient;

};

/** This helps to meet backward compatibility */
using ConjugateGradientLineSearchOptimizerv4 = ConjugateGradientLineSearchOptimizerv4Template<double>;

} // end namespace itk

#ifndef ITK_MANUAL_INSTANTIATION
#include "itkConjugateGradientLineSearchOptimizerv4.hxx"
#endif

#endif
