Better names for variables [closes #18]

chapter11.tex

@@ -480,7 +480,7 @@ efficiently implemented using them.
 A convenient way to store the adjacency lists is to declare
 an array of vectors as follows:
 \begin{lstlisting}
-vector<int> v[N];
+vector<int> adj[N];
 \end{lstlisting}
 
 The constant $N$ is chosen so that all
@@ -503,11 +503,11 @@ For example, the graph
 \end{center}
 can be stored as follows:
 \begin{lstlisting}
-v[1].push_back(2);
-v[2].push_back(3);
-v[2].push_back(4);
-v[3].push_back(4);
-v[4].push_back(1);
+adj[1].push_back(2);
+adj[2].push_back(3);
+adj[2].push_back(4);
+adj[3].push_back(4);
+adj[4].push_back(1);
 \end{lstlisting}
 
 If the graph is undirected, it can be stored in a similar way,
@@ -517,7 +517,7 @@ For a weighted graph, the structure can be extended
 as follows:
 
 \begin{lstlisting}
-vector<pair<int,int>> v[N];
+vector<pair<int,int>> adj[N];
 \end{lstlisting}
 
 If there is an edge from node $a$ to node $b$
@@ -541,11 +541,11 @@ For example, the graph
 \end{center}
 can be stored as follows:
 \begin{lstlisting}
-v[1].push_back({2,5});
-v[2].push_back({3,7});
-v[2].push_back({4,6});
-v[3].push_back({4,5});
-v[4].push_back({1,2});
+adj[1].push_back({2,5});
+adj[2].push_back({3,7});
+adj[2].push_back({4,6});
+adj[3].push_back({4,5});
+adj[4].push_back({1,2});
 \end{lstlisting}
 
 The benefit in using adjacency lists is that
@@ -555,7 +555,7 @@ For example, the following loop goes through all nodes
 to which we can move from node $s$:
 
 \begin{lstlisting}
-for (auto u : v[s]) {
+for (auto u : adj[s]) {
     // process node u
 }
 \end{lstlisting}
@@ -570,14 +570,14 @@ We can efficiently check from an adjacency matrix
 if there is an edge between two nodes.
 The matrix can be stored as an array
 \begin{lstlisting}
-int v[N][N];
+int mat[N][N];
 \end{lstlisting}
-where each value $\texttt{v}[a][b]$ indicates
+where each value $\texttt{mat}[a][b]$ indicates
 whether the graph contains an edge from
 node $a$ to node $b$.
 If the edge is included in the graph,
-then $\texttt{v}[a][b]=1$,
-and otherwise $\texttt{v}[a][b]=0$.
+then $\texttt{mat}[a][b]=1$,
+and otherwise $\texttt{mat}[a][b]=0$.
 For example, the graph
 \begin{center}
 \begin{tikzpicture}[scale=0.9]
@@ -696,7 +696,7 @@ at a given node.
 
 The edge list can be stored in a vector
 \begin{lstlisting}
-vector<pair<int,int>> v;
+vector<pair<int,int>> edges;
 \end{lstlisting}
 where each pair $(a,b)$ denotes that
 there is an edge from node $a$ to node $b$.
@@ -718,18 +718,18 @@ Thus, the graph
 \end{center}
 can be represented as follows:
 \begin{lstlisting}
-v.push_back({1,2});
-v.push_back({2,3});
-v.push_back({2,4});
-v.push_back({3,4});
-v.push_back({4,1});
+edges.push_back({1,2});
+edges.push_back({2,3});
+edges.push_back({2,4});
+edges.push_back({3,4});
+edges.push_back({4,1});
 \end{lstlisting}
 
 \noindent
 If the graph is weighted, the structure can
 be extended as follows:
 \begin{lstlisting}
-vector<tuple<int,int,int>> v;
+vector<tuple<int,int,int>> edges;
 \end{lstlisting}
 Each element in this list is of the
 form $(a,b,w)$, which means that there
@@ -753,10 +753,10 @@ For example, the graph
 \begin{samepage}
 can be represented as follows:
 \begin{lstlisting}
-v.push_back(make_tuple(1,2,5));
-v.push_back(make_tuple(2,3,7));
-v.push_back(make_tuple(2,4,6));
-v.push_back(make_tuple(3,4,5));
-v.push_back(make_tuple(4,1,2));
+edges.push_back({1,2,5});
+edges.push_back({2,3,7});
+edges.push_back({2,4,6});
+edges.push_back({3,4,5});
+edges.push_back({4,1,2});
 \end{lstlisting}
 \end{samepage}