\end{itemstep}
\end{slide}}
-\overlays{3}{
-\begin{slide}{warning}
+\overlays{4}{
+\begin{slide}{warning - machine accuracy $\epsilon_m$}
\begin{itemstep}
\item numerical precision of 64-bit floating point \\
- {\small ieee floating point format:} $v = -1^s 2^{-e} m$
- \[
+ ieee floating point format: $v = -1^s 2^{-e} m$
+ \[
\begin{array}{lll}
s: & \textrm{signe} & \textrm{1 bit} \\
m: & \textrm{mantissa} & \textrm{52 bit} \\
e: & \textrm{exponent} & \textrm{11 bit} \\
\end{array}
\]
- \item foo
- \item bar
+ \item $\epsilon_m$: smallest floating point with $1 + \epsilon_m \neq 1$ \\
+ $\epsilon_m \approx 2 \times 10^{-18}$ \hspace{2pt} (roundoff error)
+ \item $N$ arithmetic operations $\Rightarrow$ error of order $N \epsilon_m$
+ \item subtraction of very nearly equal numbers\\
+ (difference in few significant low-order bits)
\end{itemstep}
\end{slide}}
-\begin{slide}{}
+\overlays{6}{
+\begin{slide}{warning - truncation error}
+ \begin{itemstep}
+ \item discrete approximation of continuous quantity
+ \item truncation error $\equiv$ discrepancy between true answer and practical calculation
+ \item persists even on hypothetical perfect computer ($\epsilon_m = 0$)
+ \item machine independent, characteristic of used algorithm
+ \item numerical analysis: minimizing truncation error
+ \item unstable method: roundoff error interacting at early stage
+ \end{itemstep}
+\end{slide}}
-\end{slide}
+\overlays{4}{
+\begin{slide}{warning - recursive functions}
+ \begin{itemstep}
+ \item avoid recursive functions!
+ \verbatiminput{fak1.c}
+ \item better:
+ \verbatiminput{fak2.c}
+ \end{itemstep}
+\end{slide}}
\begin{slide}{computational techniques}
techniques discussed in the talk:
\hspace{6cm}
\end{slide}
+\overlays{2}{
+\begin{slide}{rough discretization}
+ \begin{itemstep}
+ \item example: homogenous field of force $\vec{F} = (0,-mg)$ \\
+ \begin{tabular}{ll}
+ equation of motion: & $\vec{F} = m \vec{a} = m \frac{d^2 \vec{r}}{dt^2}$ \\
+ initial condition: & $\vec{r}(t=0) = \vec{r_0} = (x_0,y_0)$ \\
+ & $\frac{d \vec{r}}{dt}|_{t=0} = (v_{x_0},v_{y_0})$ \\
+ \end{tabular}
+ \item algorithm using discretized time ($T_{total} = N \tau$):
+ \begin{tabular}{lll}
+ $x^1 = x_0;$ & $y^1 = y_0;$ & \\
+ $v^1_x = v_{x_0};$ & $v^1_y = v_{y_0};$ & \\
+ loop: & $x^2 = x^1 + \tau v^1_x;$ & $y^2 = y^1 + \tau v^1_y;$ \\
+ & $v^2_x = v^1_x;$ & $v^2_y = v^1_y + (-mg) \tau;$ \\
+ & $x^1 = x^2;$ & $y^1 = y^2$ \\
+ & $v^1_x = v^2_x;$ & $v^1_y = v^2_y;$ \\
+ \end{tabular}
+ \end{itemstep}
+\end{slide}}
+
+%\overlays{3}{
+%\begin{slide}{}
+% \begin{itemstep}
+% \item
+% \item
+% \item
+% \end{itemstep}
+%\end{slide}}
+
+%\overlays{3}{
+%\begin{slide}{}
+% \begin{itemstep}
+% \item
+% \item
+% \item
+% \end{itemstep}
+%\end{slide}}
+
+%\overlays{3}{
+%\begin{slide}{}
+% \begin{itemstep}
+% \item
+% \item
+% \item
+% \end{itemstep}
+%\end{slide}}
+
+%\overlays{3}{
+%\begin{slide}{}
+% \begin{itemstep}
+% \item
+% \item
+% \item
+% \end{itemstep}
+%\end{slide}}
+
+%\overlays{3}{
+%\begin{slide}{}
+% \begin{itemstep}
+% \item
+% \item
+% \item
+% \end{itemstep}
+%\end{slide}}
+
+%\overlays{3}{
+%\begin{slide}{}
+% \begin{itemstep}
+% \item
+% \item
+% \item
+% \end{itemstep}
+%\end{slide}}
+
+%\overlays{3}{
+%\begin{slide}{}
+% \begin{itemstep}
+% \item
+% \item
+% \item
+% \end{itemstep}
+%\end{slide}}
+
+%\overlays{3}{
+%\begin{slide}{}
+% \begin{itemstep}
+% \item
+% \item
+% \item
+% \end{itemstep}
+%\end{slide}}
+
+%\overlays{3}{
+%\begin{slide}{}
+% \begin{itemstep}
+% \item
+% \item
+% \item
+% \end{itemstep}
+%\end{slide}}
+
+%\overlays{3}{
+%\begin{slide}{}
+% \begin{itemstep}
+% \item
+% \item
+% \item
+% \end{itemstep}
+%\end{slide}}
+
\end{document}