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@(@\newcommand{\B}[1]{{\bf #1}} \newcommand{\R}[1]{{\rm #1}}@)@
Create an AD Function Object

f = adfun(a_xa_y)

The function object f will store the type( a_x[0] ) operation sequence that mapped the independent variable vector a_x to the dependent variable vector a_y .

The argument a_x is the numpy.array returned by the previous call to independent . Neither the size of a_x , or the value it its elements, may change between calling
a_x = independent(x)
f = adfun(a_xa_y)
The length of the vector a_x determines the domain size @(@ n @)@ for the function @(@ y = F(x) @)@ below.

The argument a_y specifies the dependent variables. It must be a numpy.array with one dimension (i.e., a vector) and with the same type of elements as a_x . The object f stores the type( a_x[0] ) operations that mapped the vector a_x to the vector a_y . The length of the vector a_y determines the range size @(@ m @)@ for the function @(@ y = F(x) @)@ below.

The return value f can be used to evaluate the function @[@ F : \B{R}^n \rightarrow \B{R}^m @]@ and its derivatives, where @(@ y = F(x) @)@ corresponds to the operation sequence mentioned above.

The range size @(@ m @)@ is equal to the length of the vector a_y .

The domain size @(@ n @)@ is equal to the length of the vector a_x .

The ad level for the object f is one less than the AD level for the arguments a_x and a_y ; i.e., if type( a_x[0] ) is a_float (a2float) the corresponding AD level for f is zero (one).

The file contains an example and test of this function.
Input File: pycppad/