CppAD: A C++ Algorithmic Differentiation Package  20171217
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template<class Base >
template<class SizeVector >
void CppAD::ADFun< Base >::for_jac_sparsity ( const sparse_rc< SizeVector > &  pattern_in,
bool  transpose,
bool  dependency,
bool  internal_bool,
sparse_rc< SizeVector > &  pattern_out 

Forward Jacobian sparsity patterns.

Template Parameters
Baseis the base type for this recording.
SizeVectoris the simple vector with elements of type size_t that is used for row, column index sparsity patterns.
pattern_inis the sparsity pattern for for R or R^T depending on transpose.
transposeIs the input and returned sparsity pattern transposed.
dependencyAre 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.
internal_boolIf this is true, calculations are done with sets represented by a vector of boolean values. Othewise, a vector of standard sets is used.
pattern_outThe value of transpose is false (true), the return value is a sparsity pattern for J(x) ( J(x)^T ) where

\[ J(x) = F^{(1)} (x) * R \]

Here F is the function corresponding to the operation sequence and x is any argument value.

Definition at line 208 of file for_jac_sparsity.hpp.