roboptim::GenericNumericLinearFunction< T > Class Template Reference

Build a linear function from a vector and a matrix. More...

#include <roboptim/core/numeric-linear-function.hh>

Inheritance diagram for roboptim::GenericNumericLinearFunction< T >:
roboptim::GenericLinearFunction< T > roboptim::GenericQuadraticFunction< T > roboptim::GenericTwiceDifferentiableFunction< T > roboptim::GenericDifferentiableFunction< T > roboptim::GenericFunction< T >

List of all members.

Public Member Functions

 ROBOPTIM_TWICE_DIFFERENTIABLE_FUNCTION_FWD_TYPEDEFS_ (GenericLinearFunction< T >)
 GenericNumericLinearFunction (const matrix_t &A, const vector_t &b)
 Build a linear function from a matrix and a vector.
 GenericNumericLinearFunction (const GenericLinearFunction< T > &)
 Build a linear function from another one.
 ~GenericNumericLinearFunction ()
virtual std::ostream & print (std::ostream &) const
 Display the function on the specified output stream.
const matrix_t & A () const
const vector_t & b () const
matrix_t & A ()
vector_t & b ()
void impl_compute (result_ref, const_argument_ref) const
 Function evaluation.
void impl_gradient (gradient_ref, const_argument_ref, size_type=0) const
 Gradient evaluation.
void impl_jacobian (jacobian_ref, const_argument_ref) const
 Jacobian evaluation.
template<>
void impl_gradient (gradient_ref gradient, const_argument_ref, size_type idFunction) const
 Gradient evaluation.

Detailed Description

template<typename T>
class roboptim::GenericNumericLinearFunction< T >

Build a linear function from a vector and a matrix.

Implement a linear function using the general formula:

\[f(x) = A x + b\]

where $A$ and $b$ are set when the class is instantiated.

Examples:
numeric-linear-function.cc.

Constructor & Destructor Documentation

template<typename T >
roboptim::GenericNumericLinearFunction< T >::GenericNumericLinearFunction ( const matrix_t &  A,
const vector_t &  b 
)

Build a linear function from a matrix and a vector.

See class documentation for A and b definition.

Parameters:
AA matrix
bb vector
template<typename T >
roboptim::GenericNumericLinearFunction< T >::GenericNumericLinearFunction ( const GenericLinearFunction< T > &  function)

Build a linear function from another one.


Member Function Documentation

template<typename T>
const matrix_t& roboptim::GenericNumericLinearFunction< T >::A ( ) const [inline]
template<typename T>
matrix_t& roboptim::GenericNumericLinearFunction< T >::A ( ) [inline]
template<typename T>
const vector_t& roboptim::GenericNumericLinearFunction< T >::b ( ) const [inline]
template<typename T>
vector_t& roboptim::GenericNumericLinearFunction< T >::b ( ) [inline]
template<typename T >
void roboptim::GenericNumericLinearFunction< T >::impl_compute ( result_ref  result,
const_argument_ref  argument 
) const [virtual]

Function evaluation.

Evaluate the function, has to be implemented in concrete classes.

Warning:
Do not call this function directly, call operator()(result_ref, const_argument_ref) const instead.
Parameters:
resultresult will be stored in this vector
argumentpoint at which the function will be evaluated

Implements roboptim::GenericFunction< T >.

template<typename T >
void roboptim::GenericNumericLinearFunction< T >::impl_gradient ( gradient_ref  gradient,
const_argument_ref  argument,
size_type  functionId = 0 
) const [virtual]

Gradient evaluation.

Compute the gradient, has to be implemented in concrete classes. The gradient is computed for a specific sub-function which id is passed through the functionId argument.

Warning:
Do not call this function directly, call gradient instead.
Parameters:
gradientgradient will be store in this argument
argumentpoint where the gradient will be computed
functionIdevaluated function id in the split representation

Implements roboptim::GenericDifferentiableFunction< T >.

template<>
void roboptim::GenericNumericLinearFunction< EigenMatrixSparse >::impl_gradient ( gradient_ref  gradient,
const_argument_ref  argument,
size_type  functionId 
) const [inline, virtual]

Gradient evaluation.

Compute the gradient, has to be implemented in concrete classes. The gradient is computed for a specific sub-function which id is passed through the functionId argument.

Warning:
Do not call this function directly, call gradient instead.
Parameters:
gradientgradient will be store in this argument
argumentpoint where the gradient will be computed
functionIdevaluated function id in the split representation

Implements roboptim::GenericDifferentiableFunction< T >.

template<typename T >
void roboptim::GenericNumericLinearFunction< T >::impl_jacobian ( jacobian_ref  jacobian,
const_argument_ref  arg 
) const [virtual]

Jacobian evaluation.

Computes the jacobian, can be overridden by concrete classes. The default behavior is to compute the jacobian from the gradient.

Warning:
Do not call this function directly, call jacobian instead.
Parameters:
jacobianjacobian will be store in this argument
argpoint where the jacobian will be computed

ROBOPTIM_DO_NOT_CHECK_ALLOCATION

Reimplemented from roboptim::GenericDifferentiableFunction< T >.

template<typename T >
std::ostream & roboptim::GenericNumericLinearFunction< T >::print ( std::ostream &  o) const [virtual]

Display the function on the specified output stream.

Parameters:
ooutput stream used for display
Returns:
output stream

Reimplemented from roboptim::GenericLinearFunction< T >.

References roboptim::decindent(), roboptim::iendl(), and roboptim::incindent().

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