CppAD: A C++ Algorithmic Differentiation Package 20110419
template<class Vector_set >
void reverse_sparse_hessian_csum_op ( size_t  i_z,
const size_t *  arg,
bool *  rev_jacobian,
Vector_set &  rev_hes_sparsity 
) [inline]

Reverse mode Hessian sparsity pattern for CSumOp operator.

This operation is

	z = p + x(1) + ... + x(m) - y(1) - ... - y(n).
	H(y, x, w, ...) = G[ z(x, y), y, x, w, ... ] 
Template Parameters:
Vector_setis the type used for vectors of sets. It can be either sparse_pack or sparse_set.
Parameters:
i_zvariable index corresponding to the result for this operation; i.e. the index in sparsity corresponding to z.
argarg[0] is the number of addition variables in this cummulative summation; i.e., m + n.
arg[1] is the number of subtraction variables in this cummulative summation; i.e., m.
parameter[ arg[2] ] is the parameter value p in this cummunative summation.
arg[2+i] for i = 1 , ... , m is the value x(i).
arg[2+arg[0]+i] for i = 1 , ... , n is the value y(i).
rev_jacobianrev_jacobian[i_z] is all false (true) if the Jabobian of G with respect to z must be zero (may be non-zero).

For i = 1 , ... , m rev_jacobian[ arg[2+i] ] is all false (true) if the Jacobian with respect to x(i) is zero (may be non-zero). On input, it corresponds to the function G, and on output it corresponds to the function H.

For i = 1 , ... , n rev_jacobian[ arg[2+arg[0]+i] ] is all false (true) if the Jacobian with respect to y(i) is zero (may be non-zero). On input, it corresponds to the function G, and on output it corresponds to the function H.
rev_hes_sparsityThe set with index i_z in in rev_hes_sparsity is the Hessian sparsity pattern for the fucntion G where one of the partials derivative is with respect to z.

For i = 1 , ... , m The set with index arg[2+i] in rev_hes_sparsity is the Hessian sparsity pattern where one of the partials derivative is with respect to x(i). On input, it corresponds to the function G, and on output it corresponds to the function H.

For i = 1 , ... , n The set with index arg[2+arg[0]+i] in rev_hes_sparsity is the Hessian sparsity pattern where one of the partials derivative is with respect to y(i). On input, it corresponds to the function G, and on output it corresponds to the function H.

Definition at line 472 of file csum_op.hpp.

Referenced by RevHesSweep().