CppAD: A C++ Algorithmic Differentiation Package  20171217
 All Classes Namespaces Files Functions Variables Typedefs Enumerations Enumerator Friends Macros
template<class Vector_set >
void CppAD::local::forward_sparse_jacobian_cond_op ( bool  dependency,
size_t  i_z,
const addr_t *  arg,
size_t  num_par,
Vector_set &  sparsity 
)
inline

Compute forward Jacobian sparsity patterns for op = CExpOp.

The C++ source code coresponding to this operation is 

     z = CondExpRel(y_0, y_1, y_2, y_3)

where Rel is one of the following: Lt, Le, Eq, Ge, Gt.

Template Parameters
Vector_setis the type used for vectors of sets. It can be either sparse_pack or sparse_list.
Parameters
i_zis the AD variable index corresponding to the variable z.
arg
arg[0] is static cast to size_t from the enum type 
     enum CompareOp {
          CompareLt,
          CompareLe,
          CompareEq,
          CompareGe,
          CompareGt,
          CompareNe
     }
for this operation. Note that arg[0] cannot be equal to CompareNe.

arg[1] & 1
If this is zero, y_0 is a parameter. Otherwise it is a variable.

arg[1] & 2
If this is zero, y_1 is a parameter. Otherwise it is a variable.

arg[1] & 4
If this is zero, y_2 is a parameter. Otherwise it is a variable.

arg[1] & 8
If this is zero, y_3 is a parameter. Otherwise it is a variable.

arg[2 + j ] for j = 0, 1, 2, 3
is the index corresponding to y_j.
num_paris the total number of values in the vector parameter.
Checked Assertions
  • NumArg(CExpOp) == 6
  • NumRes(CExpOp) == 1
  • arg[0] < static_cast<size_t> ( CompareNe )
  • arg[1] != 0; i.e., not all of y_0, y_1, y_2, y_3 are parameters.
  • For j = 0, 1, 2, 3 if y_j is a parameter, arg[2+j] < num_par.
Parameters
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.
sparsityInput: if y_2 is a variable, the set with index t is the sparsity pattern corresponding to y_2. This identifies which of the independent variables the variable y_2 depends on.
Input: if y_3 is a variable, the set with index t is the sparsity pattern corresponding to y_3. This identifies which of the independent variables the variable y_3 depends on.
Output: The set with index T is the sparsity pattern corresponding to z. This identifies which of the independent variables the variable z depends on.

Definition at line 981 of file cond_op.hpp.

Referenced by for_jac_sweep().