CppAD: A C++ Algorithmic Differentiation Package  20171217
template<class Vector_set >
 void CppAD::local::reverse_sparse_hessian_cond_op ( size_t i_z, const addr_t * arg, size_t num_par, bool * jac_reverse, Vector_set & hes_sparsity )
inline

Compute reverse Hessian sparsity patterns for op = CExpOp.

This routine is given the sparsity patterns for a function G(z, y, x, ... ) and it uses them to compute the sparsity patterns for

```     H( y, x, w , u , ... ) = G[ z(x,y) , y , x , w , u , ... ]
```

where y represents the combination of y_0, y_1, y_2, and y_3.

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_set is the type used for vectors of sets. It can be either sparse_pack or sparse_list.
Parameters
 i_z is 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_par is 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
 jac_reverse jac_reverse[i_z] is false (true) if the Jacobian of G with respect to z is always zero (may be non-zero). jac_reverse[ arg[4] ] If y_2 is a variable, jac_reverse[ arg[4] ] is false (true) if the Jacobian with respect to y_2 is always zero (may be non-zero). On input, it corresponds to the function G, and on output it corresponds to the function H. jac_reverse[ arg[5] ] If y_3 is a variable, jac_reverse[ arg[5] ] is false (true) if the Jacobian with respect to y_3 is always zero (may be non-zero). On input, it corresponds to the function G, and on output it corresponds to the function H. hes_sparsity The set with index i_z in hes_sparsity is the Hessian sparsity pattern for the function G where one of the partials is with respect to z. If y_2 is a variable, the set with index arg[4] in hes_sparsity is the Hessian sparsity pattern where one of the partials is with respect to y_2. On input, this pattern corresponds to the function G. On output, this pattern corresponds to the function H. If y_3 is a variable, the set with index arg[5] in hes_sparsity is the Hessian sparsity pattern where one of the partials is with respect to y_3. On input, this pattern corresponds to the function G. On output, this pattern corresponds to the function H.

Definition at line 1279 of file cond_op.hpp.

Referenced by rev_hes_sweep().