CppAD: A C++ Algorithmic Differentiation Package  20171217
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template<class Base >
template<class SizeVector , class BaseVector >
size_t CppAD::ADFun< Base >::sparse_jac_for ( size_t  group_max,
const BaseVector &  x,
sparse_rcv< SizeVector, BaseVector > &  subset,
const sparse_rc< SizeVector > &  pattern,
const std::string &  coloring,
sparse_jac_work work 

Calculate sparse Jacobains using forward mode.

Template Parameters
Basethe base type for the recording that is stored in the ADFun object.
SizeVectora simple vector class with elements of type size_t.
BaseVectora simple vector class with elements of type Base.
group_maxspecifies the maximum number of colors to group during a single forward sweep. This must be greater than zero and group_max = 1 minimizes memory usage.
xa vector of length n, the number of independent variables in f (this ADFun object).
subsetspecifices the subset of the sparsity pattern where the Jacobian is evaluated. subset.nr() == m, subset.nc() == n.
patternis a sparsity pattern for the Jacobian of f; pattern.nr() == m, pattern.nc() == n, where m is number of dependent variables in f.
coloringdetermines which coloring algorithm is used. This must be cppad or colpack.
workthis structure must be empty, or contain the information stored by a previous call to sparse_jac_for. The previous call must be for the same ADFun object f and the same subset.
This is the number of first order forward sweeps used to compute the Jacobian.

Definition at line 286 of file sparse_jac.hpp.