init greedy

chapterDynamicProgramming.tex

@@ -127,10 +127,11 @@ \section{Sequence}\label{dpSequence}
        \end{array} \right.
 \end{eqnarray*}
 
-\runinhead{Maximal product subarray.} Find the $A$'s subarray within an array which has the largest product. 
+\runinhead{Maximal product subarray.} Find the subarray within an array $A$ which has the largest product. 
 \begin{itemize}
 \item Let $small_i$ be the smallest product end with $A_i$. 
 \item Let $large_i$ be the largest product end with $A_i$.
+\item The states can be negative. 
 \end{itemize}
 \begin{eqnarray*}
 && small_i = \min\big( A_i,\ small_{i-1}\cdot A_i,\ large_{i-1}\cdot A_i \big)
@@ -248,7 +249,7 @@ \section{Backpack}
 F_{i, c}= \max\big(&&F_{i-1, c}, \\
 &&F_{i-1, c-w_i}+v_i\big)
 \end{eqnarray*}
-TODO advanced backpack problem. 
+Advanced backpack problem\footnote{\href{http://github.com/tianyicui/pack}{Nine Lectures in Backpack Problem}.}. 
 
 \section{Local and Global Extremes}
 \subsection{Long and short stocks}

chapterGreedy.tex

@@ -1,11 +1,6 @@
 \chapter{Greedy}
 
-
 \section{Introduction}
-TODO
-\begin{python}
----
-ABC
-___
-\end{python}
+Philosophy: choose the best options at the current state without reverting the choice in the future. 
 
+A greedy algorithm is an algorithm that follows the problem solving heuristic of making the locally optimal choice at each stage with the hope of finding a global optimum.

chapterSort.tex

@@ -130,7 +130,7 @@ \subsection{Stability}
 \textbf{Long-distance swap} operation is the key to find the unstable case during sorting. 
 \begin{figure}[hbtp]
 \centering
-\subfloat{\includegraphics[scale=.60]{stable_sort}}
+\subfloat{\includegraphics[scale=.50]{stable_sort}}
 \caption{Stale sort vs. unstable sort}
 \label{fig:trie} 
 \end{figure}