@(@\newcommand{\W}[1]{ \; #1 \; }
\newcommand{\R}[1]{ {\rm #1} }
\newcommand{\B}[1]{ {\bf #1} }
\newcommand{\D}[2]{ \frac{\partial #1}{\partial #2} }
\newcommand{\DD}[3]{ \frac{\partial^2 #1}{\partial #2 \partial #3} }
\newcommand{\Dpow}[2]{ \frac{\partial^{#1}}{\partial {#2}^{#1}} }
\newcommand{\dpow}[2]{ \frac{ {\rm d}^{#1}}{{\rm d}\, {#2}^{#1}} }@)@
This is cppad-20221105 documentation. Here is a link to its
current documentation
.
Power Function Forward Mode Theory
We consider the operation @(@
F(x) = x^y
@)@ where @(@
x
@)@
is a variable and @(@
y
@)@ is a parameter.
Derivatives
The corresponding derivative satisfies the equation
@[@
x * F^{(1)} (x) - y F(x) = 0
@]@
This is the
standard math function differential equation
,
where
@(@
A(x) = y
@)@,
@(@
B(x) = x
@)@,
and @(@
D(x) = 0
@)@.
We use @(@
a
@)@, @(@
b
@)@, @(@
d
@)@,
and @(@
z
@)@ to denote the
Taylor coefficients for
@(@
A [ X (t) ]
@)@,
@(@
B [ X (t) ]
@)@,
@(@
D [ X (t) ]
@)@,
and @(@
F [ X(t) ]
@)@ respectively.
It follows that
@(@
b^j = x^j
@)@, @(@
d^j = 0
@)@,
@[@
a^{(j)} = \left\{ \begin{array}{ll}
y & \R{if} \; j = 0
\\
0 & \R{otherwise}
\end{array} \right.
@]@