#include <admfem.hpp>

Public Types
typedef codi::RealForwardGen< double >	ADFType

typedef TAutoDiffVector< ADFType >	ADFVector

typedef TAutoDiffDenseMatrix< ADFType >	ADFDenseMatrix

typedef codi::RealForwardGen< ADFType >	ADSType

typedef TAutoDiffVector< ADSType >	ADSVector

typedef TAutoDiffDenseMatrix< ADSType >	ADSDenseMatrix

typedef codi::RealForwardGen< double >	ADFType

typedef TAutoDiffVector< ADFType >	ADFVector

typedef TAutoDiffDenseMatrix< ADFType >	ADFDenseMatrix

typedef codi::RealReverseGen< ADFType >	ADSType

typedef TAutoDiffVector< ADSType >	ADSVector

typedef TAutoDiffDenseMatrix< ADSType >	ADSDenseMatrix

Public Member Functions
double	Eval (const mfem::Vector &vparam, mfem::Vector &uu)

void	VectorFunc (const mfem::Vector &vparam, mfem::Vector &uu, mfem::Vector &rr)
	Provides the same functionality as Grad. More...

void	Grad (const mfem::Vector &vparam, mfem::Vector &uu, mfem::Vector &rr)

void	Jacobian (mfem::Vector &vparam, mfem::Vector &uu, mfem::DenseMatrix &jac)
	Provides same functionality as Hessian. More...

void	Hessian (mfem::Vector &vparam, mfem::Vector &uu, mfem::DenseMatrix &jac)

double	Eval (const Vector &vparam, Vector &uu)

void	VectorFunc (const Vector &vparam, Vector &uu, Vector &rr)
	Provides the same functionality as Grad. More...

void	Grad (const Vector &vparam, Vector &uu, Vector &rr)

void	Jacobian (mfem::Vector &vparam, mfem::Vector &uu, mfem::DenseMatrix &jac)
	Provides same functionality as Hessian. More...

void	Hessian (mfem::Vector &vparam, mfem::Vector &uu, mfem::DenseMatrix &jac)

Detailed Description

template<template< typename, typename, typename, int, int > class TFunctor, int state_size = 1, int param_size = 0>
class mfem::QFunctionAutoDiff< TFunctor, state_size, param_size >

The class provides an evaluation of the first derivatives and the Hessian of a templated scalar function provided as a functor TFunctor. Both the first and the second derivatives are evaluated with the help of automatic differentiation (AD). The template parameters specify the size of the input vector (state_size) and the size of the parameters supplied to the function. The TFunctor functor is a template class with parameters [Float data type], [Vector type for the additional parameters], [Vector type for the state vector and the return residual]. The integer template parameters are the same ones passed to QFunctionAutoDiff.

The class provides an evaluation of the first derivatives and the Hessian of a templated scalar function provided as a functor TFunctor. Both the first and the second derivatives are evaluated with the help of automatic differentiation (AD). The template parameters specify the size of the input vector (state_size) and the size of the parameters supplied to the function. The TFunctor functor is a template class with parameters [Float data type], [Vector type for the additional parameters], [Vector type for the state vector and the return residual]. The integer template parameters are the same ones passed to QFunctionAutoDiff. The class duplicates Grad and Hessian, i.e., VectorFunc calls Grad, and Jacobian calls Hessian. The main reason is to provide the same interface as the QVectorFuncAutoDiff class used to differentiate vector functions. Such compatibility allows users to start implementation of their problem based only on some energy or a weak form. The gradients, computed with Grad/VectorFunc, of the function will contribute to the FE residual. Computed with Hessian/Jacobian, the Hessian will contribute to the tangent matrix in Newton's iterations. Once the implementation is complete and tested, the users can start improving the performance by replacing Grad/VectorFunc with a hand-coded version. The gradient is a vector function and can be differentiated with the functionality implemented in QVectorFuncAutoDiff. Thus, the user can directly employ AD for computing the contributions to the global tangent matrix. The main code will not require changes as the names Grad/VectorFunc and Hessian/Jacobian are mirrored.

Definition at line 242 of file admfem.hpp.