template<class Base , class Vector_set >

void CppAD::local::rev_jac_sweep	(	const local::player< Base > *	play,
		bool	dependency,
		size_t	n,
		size_t	numvar,
		Vector_set &	var_sparsity
	)

Given the sparsity pattern for the dependent variables, RevJacSweep computes the sparsity pattern for all the independent variables.

Template Parameters

Base	this operation sequence was recorded using AD<Base>.
Vector_set	is the type used for vectors of sets. It can be either sparse_pack or sparse_list.

Parameters

dependency	Are the derivatives with respect to left and right of the expression below considered to be non-zero: CondExpRel(left, right, if_true, if_false) This is used by the optimizer to obtain the correct dependency relations.
n	is the number of independent variables on the tape.
numvar	is the total number of variables on the tape; i.e., play->num_var_rec(). This is also the number of rows in the entire sparsity pattern RevJac.
play	The information stored in play is a recording of the operations corresponding to a function $F : {\bf R}^n \rightarrow {\bf R}^m$ where is the number of independent variables and is the number of dependent variables.
var_sparsity	For i = 0 , ... , numvar - 1, (all the variables on the tape) the forward Jacobian sparsity pattern for variable i corresponds to the set with index i in var_sparsity. Input: For i = 0 , ... , numvar - 1, the forward Jacobian sparsity pattern for variable i is an input if i corresponds to a dependent variable. Otherwise the sparsity patten is empty. Output: For j = 1 , ... , n, the sparsity pattern for the dependent variable with index (j-1) is given by the set with index index j in var_sparsity.

Definition at line 85 of file rev_jac_sweep.hpp.