# 22.2 Breadth-first search

## 22.2-1

Show the $d$ and $\pi$ values that result from running breadth-first search on the directed graph of Figure 22.2(a), using vertex $3$ as the source.

$$ \begin{array}{c|cccccc} \text{vertex} & 1 & 2 & 3 & 4 & 5 & 6 \\ \hline d & \infty & 3 & 0 & 2 & 1 & 1 \\ \pi & \text{NIL} & 4 & \text{NIL} & 5 & 3 & 3 \end{array} $$

## 22.2-2

Show the $d$ and $\pi$ values that result from running breadth-first search on the undirected graph of Figure 22.3, using vertex $u$ as the source.

$$ \begin{array}{c|cccccc} \text{vertex} & r & s & t & u & v & w & x & y \\ \hline d & 4 & 3 & 1 & 0 & 5 & 2 & 1 & 1 \\ \pi & s & w & u & \text{NIL} & r & t & u & u \end{array} $$

## 22.2-3

Show that using a single bit to store each vertex color suffices by arguing that the $\text{BFS}$ procedure would produce the same result if lines 5 and 14 were removed.

(Removed)

## 22.2-4

What is the running time of $\text{BFS}$ if we represent its input graph by an adjacency matrix and modify the algorithm to handle this form of input?

The time of iterating all edges becomes $O(V^2)$ from $O(E)$. Therefore, the running time is $O(V + V^2)$.

## 22.2-5

Argue that in a breadth-first search, the value $u.d$ assigned to a vertex $u$ is independent of the order in which the vertices appear in each adjacency list. Using Figure 22.3 as an example, show that the breadth-first tree computed by $\text{BFS}$ can depend on the ordering within adjacency lists.

(Removed)

## 22.2-6

Give an example of a directed graph $G = (V, E)$, a source vertex $s \in V$, and a set of tree edges $E_\pi \subseteq E$ such that for each vertex $v \in V$, the unique simple path in the graph $(V, E_\pi)$ from $s$ to $v$ is a shortest path in $G$, yet the set of edges $E_\pi$ cannot be produced by running $\text{BFS}$ on $G$, no matter how the vertices are ordered in each adjacency list.

(Removed)

## 22.2-7

There are two types of professional wrestlers: "babyfaces" ("good guys") and "heels" ("bad guys"). Between any pair of professional wrestlers, there may or may not be a rivalry. Suppose we have $n$ professional wrestlers and we have a list of $r$ pairs of wrestlers for which there are rivalries. Give an $O(n + r)$-time algorithm that determines whether it is possible to designate some of the wrestlers as babyfaces and the remainder as heels such that each rivalry is between a babyface and a heel. If it is possible to perform such a designation, your algorithm should produce it.

(Removed)

## 22.2-8 $\star$

The

of a tree $T = (V, E)$ is defined as $\max_{u,v \in V} \delta(u, v)$, that is, the largest of all shortest-path distances in the tree. Give an efficient algorithm to compute the diameter of a tree, and analyze the running time of your algorithm.diameter

Suppose that a and b are the endpoints of the path in the tree which achieve the diameter, and without loss of generality assume that $a$ and $b$ are the unique pair which do so. Let $s$ be any vertex in $T$. We claim that the result of a single $\text{BFS}$ will return either $a$ or $b$ (or both) as the vertex whose distance from $s$ is greatest.

To see this, suppose to the contrary that some other vertex $x$ is shown to be furthest from $s$. (Note that $x$ cannot be on the path from $a$ to $b$, otherwise we could extend). Then we have

$$d(s, a) < d(s, x)$$

and

$$d(s, b) < d(s, x).$$

Let $c$ denote the vertex on the path from $a$ to $b$ which minimizes $d(s, c)$. Since the graph is in fact a tree, we must have

$$d(s, a) = d(s, c) + d(c, a)$$

and

$$d(s, b) = d(s, c) + d(c, b).$$

(If there were another path, we could form a cycle). Using the triangle inequality and inequalities and equalities mentioned above we must have

$$ \begin{aligned} d(a, b) + 2d(s, c) & = d(s, c) + d(c, b) + d(s, c) + d(c, a) \\ & < d(s, x) + d(s, c) + d(c, b). \end{aligned} $$

I claim that $d(x, b) = d(s, x) + d(s, b)$. If not, then by the triangle inequality we must have a strict less-than. In other words, there is some path from $x$ to $b$ which does not go through $c$. This gives the contradiction, because it implies there is a cycle formed by concatenating these paths. Then we have

$$d(a, b) < d(a, b) + 2d(s, c) < d(x, b).$$

Since it is assumed that $d(a, b)$ is maximal among all pairs, we have a contradiction. Therefore, since trees have $|V| - 1$ edges, we can run $\text{BFS}$ a single time in $O(V)$ to obtain one of the vertices which is the endpoint of the longest simple path contained in the graph. Running $\text{BFS}$ again will show us where the other one is, so we can solve the diameter problem for trees in $O(V)$.

## 22.2-9

Let $G = (V, E)$ be a connected, undirected graph. Give an $O(V + E)$-time algorithm to compute a path in $G$ that traverses each edge in $E$ exactly once in each direction. Describe how you can find your way out of a maze if you are given a large supply of pennies.

First, the algorithm computes a minimum spanning tree of the graph. Note that this can be done using the procedures of Chapter 23. It can also be done by performing a breadth first search, and restricting to the edges between $v$ and $v.\pi$ for every $v$. To aide in not double counting edges, fix any ordering $\le$ on the vertices before hand. Then, we will construct the sequence of steps by calling $\text{MAKE-PATH}(s)$, where $s$ was the root used for the $\text{BFS}$.

1 2 3 4 5 6 7 | MAKE-PATH(u) for each v ∈ Adj[u] but not in the tree such that u ≤ v go to v and back to u for each v ∈ Adj[u] but not equal to u.π go to v perform the path proscribed by MAKE-PATH(v) go to u.π |