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Give an efficient algorithm that takes as input a directed acyclic graph G=V,E, and two vertices s,tV, and outputs the number of different directed paths from S to t in G.

Short Answer

Expert verified

Depth first search algorithm is the efficient algorithm to take the directed acyclic graph as input and outputs the number of different directed paths.

Step by step solution

01

Step 1:Explain Directed Acyclic graph.

A graph that has directed edges without the cycles formed is known as directed Acyclic graph. Each vertex can have more than one edge. There exists multiple paths from the source to destination.

02

Give an efficient algorithm.

Consider the directed acyclic graph G=V,Ewith two vertices s,tV. The number of different directed paths from S to t in G can be found by depth first search algorithm efficiently.

Procedure GraphGV,E

define Depth First SearchG,u:

visited[u]=True

if u=t:

return 1

for v in Gu:

count+=dfsG,v

return count

The above algorithm uses dfs as the procedure, to count the number of the directed path to from source to destination.

Therefore, the efficient algorithm to take the directed acyclic graph as input and outputs the number of different directed paths has been provided.

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Most popular questions from this chapter

Infinite paths.Let G=(V,E) be a directed graph with a designated “start vertex” sV,asetVGV, a set of “good” vertices, and a set VBV of “bad” vertices. An infinite trace of is an infinite sequence of vertices viV such that (1)v0=s, and (2) for all i0, (vi,vi+1)E. That is, p is an infinite path in G starting at vertex s. Since the setV of vertices is finite, every infinite trace of Gmust visit some vertices infinitely often.

  1. If p is an infinite trace, let Inf(p)V be the set of vertices that occur infinitely often in p. Show that Inf(p) is a subset of a strongly connected component of G.
  2. Describe an algorithm that determines if role="math" G has an infinite trace.
  3. Describe an algorithm that determines if G has an infinite trace that visits some good vertex in VG infinitely often.
  4. Describe an algorithm that determines if role="math" localid="1659627728759" G has an infinite trace that visits some good vertex in VG infinitely often, but visits no bad vertex in VB infinitely often.

Give a linear-time algorithm for the following task.
Input: A directed acyclic graph G

Does G contain a directed path that touches every vertex exactly once?

Run the strongly connected components algorithm on the following directed graphs G. When doing DFS on GR: whenever there is a choice of vertices to explore, always pick the one that is alphabetically first.

In each case answer the following questions.

(a) In what order are the strongly connected components (SCCs) found?

(b) Which are source SCCs and which are sink SCCs?

(c) Draw the “metagraph” (each meta-node is an SCC of G).

(d) What is the minimum number of edges you must add to this graph to make it strongly connected

Either prove or give a counterexample: if {u,v}is an edge in an undirected graph, and during depth-first search (u)<post (v), then vis an ancestor of uin the DFS tree.

Biconnected componentsLet G=(V,E) be an undirected graph. For any two edgese,e'E,, we’ll saye:e'if eithere=e'or there is a (simple) cycle containing both e and e'.

a. Show that : is an equivalence relation (recall Exercise 3.29) on the edges.

The equivalence classes into which this relation partitions the edges are called the biconnected components of G . A bridge is an edge which is in a biconnected component all by itself.

A separating vertexis a vertex whose removal disconnects the graph.

b. Partition the edges of the graph below into biconnected components, and identify the bridges and separating vertices.

Not only do biconnected components partition the edges of the graph, they almost partition the vertices in the following sense.

c. Associate with each biconnected component all the vertices that are endpoints of its edges. Show that the vertices corresponding to two different biconnected components are either disjoint or intersect in a single separating vertex.

d. Collapse each biconnected component into a single meta-node, and retain individual nodes for each separating vertex. (So there are edges between each component node and its separating vertices.) Show that the resulting graph is a tree.

DFS can be used to identify the biconnected components, bridges, and separating vertices of a graph in linear time.

e. Show that the root of the DFS tree is a separating vertex if and only if it has more than one child in the tree.

f. Show that a non-root vertex v of the DFS tree is a separating vertex if and only if it has a child v' none of whose descendants (including itself) has a back edge to a proper ancestor of v .

g. For each vertex u define:

Iowu=minpreuprew

Where (v,w) is a back edge for some descendant v of u.

(h) Show how to compute all separating vertices, bridges, and biconnected components of a graph in linear time.

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