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J = f.jacobian(x)
F^{(1)} (x)
where
F : \B{R}^n \rightarrow \B{R}^m
is the
function corresponding to the adfun
object f
.
f
must be an adfun
object.
We use level
for the AD ad
level of
this object.
x
is a numpy.array
with one dimension
(i.e., a vector) with length equal to the domain size n
for the function
f
.
It specifies the argument value at which the derivative is computed.
If the AD level
for
f
is zero,
all the elements of
x
must be either int
or instances
of float
.
If the AD level
for
f
is one,
all the elements of
x
must be a_float
objects.
J
is a numpy.array
with two dimensions
(i.e., a matrix).
The first dimension (row size) is equal to m
(the number of range components in the function
f
).
The second dimension (column size) is equal to n
(the number of domain components in the function
f
).
It is set to the derivative; i.e.,
\[
J = F^{(1)} (x)
\]
If the AD level
for
f
is zero,
all the elements of
J
will be instances of float
.
If the AD level
for
f
is one,
all the elements of
J
will be a_float
objects.