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| Syntax |
autodiff(function f, epsf, x, h) |
| See Also | fordiff , cendiff |
f(y) evaluated at the point
specified by x.
The return value has the same number of rows as f(y)
and its column dimension is equal to the row dimension of x.
f(y) returns a column vector with the same type as x.
In addition, the row dimension of f(y) is the same for all
values of y.
The scalar epsf has the same type as x
and specifies the relative accuracy in the
value of f(y).
The real, double-precision or complex column vector x
is the point at which the derivative of f(y) is approximated.
The column vector h
has the same type and dimension as x
The input value of h specifies the step size for
this approximation of the derivative of f(y).
The output value of h is a suggested step size for
the next time the partials of f(y) is evaluated nearby.
You may modify this value before the next call to autodiff.
(for example, to ensure it is between some maximum and minimum values).
f(y) simulates evaluating this function
to a relative accuracy of 1e-5 by adding a random error.
The autodiff function is called four times in order
to demonstrate the improvement in the derivative approximation
with each call.
Note that the derivative of the exponential function at x = 1
is equal to exp(1).
clear
function f(y) begin
return exp(y) * ( 1d0 + 1d-5 * rand(1, 1))
end
format int "10"
format double "f15.10"
epsf = 1d-5
x = 1d0
h = 1d0
print "exp(1d0) =", exp(1d0)
print align("i, h, autodiff", ",", [10, 16, 16], "right")
for i = 1 to 4 begin
print i, h, autodiff(function f, epsf, x, h)
end
h(j) + x(j)
is equal to x(j), partials of f(y) with respect
to x(j) are not approximated. In this case, zero is placed in
the j-th column of the return value.
In addition, the output value of h(j) is equal to its
input value.
f(y) that has the largest value.
If the components of f(y) have very different
scaling, it may help to evaluate the derivative one component at a time
(maintaining a different value of h for each component).
autodiff,
cendiff
, and
fordiff
can be used to approximate
derivatives for an optimization or zero-finding algorithm.
The function autodiff requires more function
evaluations than the others, but it
should be more accurate.