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

Compute reverse mode Taylor coefficients for op = CExpOp.

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(y) , y , x , w , u , ... ]

where y above represents y_0, y_1, y_2, 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
Basebase type for the operator; i.e., this operation was recorded using AD< Base > and computations by this routine are done using type Base.
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.
parameterFor j = 0, 1, 2, 3, if y_j is a parameter, parameter [ arg[2 + j] ] is its value.
cap_ordernumber of columns in the matrix containing the Taylor coefficients.
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
dis the order of the Taylor coefficient of z that we are computing.
taylorInput: For j = 0, 1, 2, 3 and k = 0 , ... , d, if y_j is a variable then taylor [ arg[2+j] * cap_order + k ] is the k-th order Taylor coefficient corresponding to y_j.
taylor [ i_z * cap_order + k ] for k = 0 , ... , d is the k-th order Taylor coefficient corresponding to z.
nc_partialnumber of columns in the matrix containing the Taylor coefficients.
partialInput: For j = 0, 1, 2, 3 and k = 0 , ... , d, if y_j is a variable then partial [ arg[2+j] * nc_partial + k ] is the partial derivative of G( z , y , x , w , u , ... ) with respect to the k-th order Taylor coefficient corresponding to y_j.
Input: partial [ i_z * cap_order + 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 corresponding to z.
Output: For j = 0, 1, 2, 3 and k = 0 , ... , d, if y_j is a variable then partial [ arg[2+j] * nc_partial + k ] is the partial derivative of H( y , x , w , u , ... ) with respect to the k-th order Taylor coefficient corresponding to y_j.

Definition at line 814 of file cond_op.hpp.

Referenced by reverse_sweep().