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dw =
f.Reverse(1,
w)
F : B^n \rightarrow B^m
to denote the
AD function
corresponding to f.
The function
W : B^n \rightarrow B
is defined by
\[
W(x) = w_0 * F_0 ( x ) + \cdots + w_{m-1} * F_{m-1} (x)
\]
The result of this operation is the derivative
dw = W^{(1)} (x)
; i.e.,
\[
dw = w_0 * F_0^{(1)} ( x ) + \cdots + w_{m-1} * F_{m-1}^{(1)} (x)
\]
Note that if
w
is the i-th
elementary vector
,
dw = F_i^{(1)} (x)
.
const ADFun<
Base>
f
Before this call to Reverse
, the value returned by
f.size_taylor()
must be greater than or equal one (see size_taylor
).
f.Forward(0,
x)
If there is no previous call with the first argument zero,
the value of the independent
variables
during the recording of the AD sequence of operations is used
for x.
const
Vector &
w
(see Vector
below)
and its size
must be equal to m, the dimension of the
range
space for f.
Vector
dw
(see Vector
below)
and its value is the derivative
W^{(1)} (x)
.
The size of dw
is equal to n, the dimension of the
domain
space for f.