Merged Madhu and Mainline branches.
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/day1/data/pos.txt Wed Oct 28 20:33:17 2009 +0530
@@ -0,0 +1,41 @@
+0. 0.
+0.25 0.47775
+0.5 0.931
+0.75 1.35975
+1. 1.764
+1.25 2.14375
+1.5 2.499
+1.75 2.82975
+2. 3.136
+2.25 3.41775
+2.5 3.675
+2.75 3.90775
+3. 4.116
+3.25 4.29975
+3.5 4.459
+3.75 4.59375
+4. 4.704
+4.25 4.78975
+4.5 4.851
+4.75 4.88775
+5. 4.9
+5.25 4.88775
+5.5 4.851
+5.75 4.78975
+6. 4.704
+6.25 4.59375
+6.5 4.459
+6.75 4.29975
+7. 4.116
+7.25 3.90775
+7.5 3.675
+7.75 3.41775
+8. 3.136
+8.25 2.82975
+8.5 2.499
+8.75 2.14375
+9. 1.764
+9.25 1.35975
+9.5 0.931
+9.75 0.47775
+10. 0.
--- a/day1/session3.tex Wed Oct 28 20:33:01 2009 +0530
+++ b/day1/session3.tex Wed Oct 28 20:33:17 2009 +0530
@@ -588,18 +588,19 @@
\item A is also called a Van der Monde matrix
\item It can be generated using \typ{vander}
\end{itemize}
-Van der Monde matrix of order M
+\begin{lstlisting}
+In []: A = vander(L, 2)
+\end{lstlisting}
+Gives the required Van der Monde matrix
\begin{equation*}
\begin{bmatrix}
- l_1^{M-1} & \ldots & l_1 & 1 \\
- l_2^{M-1} & \ldots &l_2 & 1 \\
- \vdots & \ldots & \vdots & \vdots\\
- l_N^{M-1} & \ldots & l_N & 1 \\
+ l_1 & 1 \\
+ l_2 & 1 \\
+ \vdots & \vdots\\
+ l_N & 1 \\
\end{bmatrix}
\end{equation*}
-\begin{lstlisting}
-In []: A = vander(L,2)
-\end{lstlisting}
+
\end{frame}
\begin{frame}[fragile]
--- a/day1/session5.tex Wed Oct 28 20:33:01 2009 +0530
+++ b/day1/session5.tex Wed Oct 28 20:33:17 2009 +0530
@@ -129,7 +129,6 @@
\begin{frame}[fragile]
\frametitle{Interpolation}
\begin{itemize}
-\item Let us begin with interpolation
\item Let's use the L and T arrays and interpolate this data to obtain data at new points
\end{itemize}
\begin{lstlisting}
--- a/day1/session6.tex Wed Oct 28 20:33:01 2009 +0530
+++ b/day1/session6.tex Wed Oct 28 20:33:17 2009 +0530
@@ -285,7 +285,7 @@
%% \end{frame}
\begin{frame}[fragile]
-\frametitle{Newton Raphson Method}
+\frametitle{Newton-Raphson Method}
\begin{enumerate}
\item Start with an initial guess of x for the root
\item $\Delta x = -f(x)/f^{'}(x)$
@@ -295,7 +295,7 @@
\end{frame}
%% \begin{frame}[fragile]
-%% \frametitle{Newton Raphson \dots}
+%% \frametitle{Newton-Raphson \dots}
%% \begin{lstlisting}
%% In []: def our_df(x):
%% ....: return -sin(x)-2*x
@@ -310,10 +310,10 @@
%% \end{frame}
\begin{frame}[fragile]
-\frametitle{Newton Raphson \ldots}
+\frametitle{Newton-Raphson \ldots}
\begin{itemize}
\item What if $f^{'}(x) = 0$?
-\item Can you write a better version of the Newton Raphson?
+\item Can you write a better version of the Newton-Raphson?
\item What if the differential is not easy to calculate?
\item Look at Secant Method
\end{itemize}
@@ -368,7 +368,7 @@
\item Finding Roots
\begin{itemize}
\item Estimating Interval
- \item Newton Raphson
+ \item Newton-Raphson
\item Scipy methods
\end{itemize}
\end{itemize}