/* $Id: sparse_unary_op.hpp 2625 2012-12-23 14:34:12Z bradbell $ */
# ifndef CPPAD_SPARSE_UNARY_OP_INCLUDED
# define CPPAD_SPARSE_UNARY_OP_INCLUDED
/* --------------------------------------------------------------------------
CppAD: C++ Algorithmic Differentiation: Copyright (C) 2003-12 Bradley M. Bell

CppAD is distributed under multiple licenses. This distribution is under
the terms of the 
                    Eclipse Public License Version 1.0.

A copy of this license is included in the COPYING file of this distribution.
Please visit http://www.coin-or.org/CppAD/ for information on other licenses.
-------------------------------------------------------------------------- */

CPPAD_BEGIN_NAMESPACE
/*!
\defgroup sparse_unary_op_hpp sparse_unary_op.hpp
\{
\file sparse_unary_op.hpp
Forward and reverse mode sparsity patterns for unary operators.
*/


/*!
Forward mode Jacobian sparsity pattern for all unary operators. 

The C++ source code corresponding to a unary operation has the form
\verbatim
	z = fun(x)
\endverbatim
where fun is a C++ unary function, or it has the form
\verbatim
	z = x op p
\endverbatim
where op is a C++ binary unary operator and p is a parameter.

\tparam Vector_set
is the type used for vectors of sets. It can be either
\c sparse_pack, \c sparse_set or \c sparse_list.

\param i_z
variable index corresponding to the result for this operation; 
i.e., z. 

\param i_x
variable index corresponding to the argument for this operator;
i.e., x.


\param sparsity
\b Input: The set with index \a arg[0] in \a sparsity
is the sparsity bit pattern for x.
This identifies which of the independent variables the variable x
depends on. 
\n
\n
\b Output: The set with index \a i_z in \a sparsity
is the sparsity bit pattern for z.
This identifies which of the independent variables the variable z
depends on. 
\n

\par Checked Assertions:
\li \a i_x < \a i_z 
*/

template <class Vector_set>
inline void forward_sparse_jacobian_unary_op(
	size_t            i_z           ,
	size_t            i_x           ,
	Vector_set&       sparsity      )
{	
	// check assumptions
	CPPAD_ASSERT_UNKNOWN( i_x < i_z );

	sparsity.assignment(i_z, i_x, sparsity);
}	

/*!
Reverse mode Jacobian sparsity pattern for all unary operators. 

The C++ source code corresponding to a unary operation has the form
\verbatim
	z = fun(x)
\endverbatim
where fun is a C++ unary function, or it has the form
\verbatim
	z = x op p
\endverbatim
where op is a C++ bianry operator and p is a parameter.

This routine is given the sparsity patterns
for a function G(z, y, ... )
and it uses them to compute the sparsity patterns for 
\verbatim
	H( x , w , u , ... ) = G[ z(x) , x , w , u , ... ]
\endverbatim

\tparam Vector_set
is the type used for vectors of sets. It can be either
\c sparse_pack, \c sparse_set, or \c sparse_list.


\param i_z
variable index corresponding to the result for this operation; 
i.e. the row index in sparsity corresponding to z. 

\param i_x
variable index corresponding to the argument for this operator;
i.e. the row index in sparsity corresponding to x.

\param sparsity
\b Input: 
The set with index \a i_z in \a sparsity 
is the sparsity bit pattern for G with respect to the variable z. 
\n
\b Input: 
The set with index \a i_x in \a sparsity
is the sparsity bit pattern for G with respect to the variable x. 
\n
\b Output: 
The set with index \a i_x in \a sparsity 
is the sparsity bit pattern for H with respect to the variable x.

\par Checked Assertions:
\li \a i_x < \a i_z 
*/

template <class Vector_set>
inline void reverse_sparse_jacobian_unary_op(
	size_t     i_z                     ,
	size_t     i_x                     ,
	Vector_set&            sparsity    )
{	
	// check assumptions
	CPPAD_ASSERT_UNKNOWN( i_x < i_z );

	sparsity.binary_union(i_x, i_x, i_z, sparsity);

	return;
}	

/*!
Reverse mode Hessian sparsity pattern for linear unary operators.

The C++ source code corresponding to this operation is
\verbatim
        z = fun(x)
\endverbatim
where fun is a linear functions; e.g. abs, or
\verbatim
	z = x op p
\endverbatim
where op is a C++ binary operator and p is a parameter.

\copydetails reverse_sparse_hessian_unary_op
*/
template <class Vector_set>
inline void reverse_sparse_hessian_linear_unary_op(
	size_t              i_z               ,
	size_t              i_x               ,
	bool*               rev_jacobian      ,
	Vector_set&         for_jac_sparsity  ,
	Vector_set&         rev_hes_sparsity  )
{	
	// check assumptions
	CPPAD_ASSERT_UNKNOWN( i_x < i_z );

	rev_hes_sparsity.binary_union(i_x, i_x, i_z, rev_hes_sparsity);

	rev_jacobian[i_x] |= rev_jacobian[i_z];
	return;
}

/*!
Reverse mode Hessian sparsity pattern for non-linear unary operators.

The C++ source code corresponding to this operation is
\verbatim
        z = fun(x)
\endverbatim
where fun is a non-linear functions; e.g. sin. or
\verbatim
	z = p / x
\endverbatim
where p is a parameter.


\copydetails reverse_sparse_hessian_unary_op
*/
template <class Vector_set>
inline void reverse_sparse_hessian_nonlinear_unary_op(
	size_t              i_z               ,
	size_t              i_x               ,
	bool*               rev_jacobian      ,
	Vector_set&         for_jac_sparsity  ,
	Vector_set&         rev_hes_sparsity  )
{	
	// check assumptions
	CPPAD_ASSERT_UNKNOWN( i_x < i_z );

	rev_hes_sparsity.binary_union(i_x, i_x, i_z, rev_hes_sparsity);
	if( rev_jacobian[i_z] )
		rev_hes_sparsity.binary_union(i_x, i_x, i_x, for_jac_sparsity);

	rev_jacobian[i_x] |= rev_jacobian[i_z];
	return;
}

/*! \} */
CPPAD_END_NAMESPACE
# endif
