CppAD: A C++ Algorithmic Differentiation Package  20171217
template<class Base >
 void CppAD::local::reverse_addvv_op ( size_t d, size_t i_z, const addr_t * arg, const Base * parameter, size_t cap_order, const Base * taylor, size_t nc_partial, Base * partial )
inline

Compute reverse mode partial derivatives for result of op = AddvvOp.

The C++ source code corresponding to this operation is

```     z = x + y
```

In the documentation below, this operations is for the case where both x and y are variables and the argument parameter is not used.

This routine is given the partial derivatives of a function G( z , y , x , w , ... ) and it uses them to compute the partial derivatives of

```     H( y , x , w , u , ... ) = G[ z(x , y) , y , x , w , u , ... ]
```
Template Parameters
 Base base type for the operator; i.e., this operation was recorded using AD< Base > and computations by this routine are done using type Base .
Parameters
 d highest order Taylor coefficient that we are computing the partial derivatives with respect to. i_z variable index corresponding to the result for this operation; i.e. the row index in taylor corresponding to z. arg arg[0] index corresponding to the left operand for this operator; i.e. the index corresponding to x. arg[1] index corresponding to the right operand for this operator; i.e. the index corresponding to y. parameter If x is a parameter, parameter [ arg[0] ] is the value corresponding to x. If y is a parameter, parameter [ arg[1] ] is the value corresponding to y. cap_order maximum number of orders that will fit in the `taylor` array. taylor taylor [ i_z * cap_order + k ] for k = 0 , ... , d is the k-th order Taylor coefficient corresponding to z. If x is a variable, taylor [ arg[0] * cap_order + k ] for k = 0 , ... , d is the k-th order Taylor coefficient corresponding to x. If y is a variable, taylor [ arg[1] * cap_order + k ] for k = 0 , ... , d is the k-th order Taylor coefficient corresponding to y. nc_partial number of colums in the matrix containing all the partial derivatives. partial Input: partial [ i_z * nc_partial + k ] for k = 0 , ... , d is the partial derivative of G( z , y , x , w , u , ... ) with respect to the k-th order Taylor coefficient for z. Input: If x is a variable, partial [ arg[0] * nc_partial + k ] for k = 0 , ... , d is the partial derivative of G( z , y , x , w , u , ... ) with respect to the k-th order Taylor coefficient for x. Input: If y is a variable, partial [ arg[1] * nc_partial + k ] for k = 0 , ... , d is the partial derivative of G( z , x , w , u , ... ) with respect to the k-th order Taylor coefficient for the auxillary variable y. Output: If x is a variable, partial [ arg[0] * nc_partial + k ] for k = 0 , ... , d is the partial derivative of H( y , x , w , u , ... ) with respect to the k-th order Taylor coefficient for x. Output: If y is a variable, partial [ arg[1] * nc_partial + k ] for k = 0 , ... , d is the partial derivative of H( y , x , w , u , ... ) with respect to the k-th order Taylor coefficient for y. Output: partial [ i_z * nc_partial + k ] for k = 0 , ... , d may be used as work space; i.e., may change in an unspecified manner.
Checked Assumptions
• NumArg(op) == 2
• NumRes(op) == 1
• If x is a variable, arg[0] < i_z
• If y is a variable, arg[1] < i_z
• d < cap_order
• d < nc_partial

Definition at line 151 of file add_op.hpp.

Referenced by reverse_sweep().