Introduction to Algorithms
3rd Edition
ISBN: 9780262033848
Author: Thomas H. Cormen, Ronald L. Rivest, Charles E. Leiserson, Clifford Stein
Publisher: MIT Press
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Chapter 22.4, Problem 1E
Program Plan Intro
To show the vertices order created by topological sort under the DAG.
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using R
Use the igraph library to define and plot graphs (multigraph and digraph).
setting up the arcs/edges algorithmically. (the list of arcs/edges or the adjacency
matrix should be implemented and the code should be able to deal with a different list of vertices and still produce graphs satisfying the stated rules.)
5. (This question goes slightly beyond what was covered in the lectures, but you can solve it by combining algorithms that we have described.)
A directed graph is said to be strongly connected if every vertex is reachable from every other vertex; i.e., for every pair of vertices u, v, there is a directed path from u to v and a directed path from v to u.
A strong component of a graph is then a maximal subgraph that is strongly connected. That is all vertices in a strong component can reach each other, and any other vertex in the directed graph either cannot reach the strong component or cannot be reached from the component.
(Note that we are considering directed graphs, so for a pair of vertices u and v there could be a path from u to v, but no path path from v back to u; in that case, u and v are not in the same strong component, even though they are connected by a path in one direction.)
Given a vertex v in a directed graph D, design an algorithm for com- puting the strong connected…
Please Answer this in Python language:
You're given a simple undirected graph G with N vertices and M edges. You have to assign, to each
vertex i, a number C; such that 1 ≤ C; ≤ N and Vi‡j, C; ‡ Cj.
For any such assignment, we define D; to be the number of neighbours j of i such that C; < C₁.
You want to minimise maai[1..N) Di - mini[1..N) Di.
Output the minimum possible value of this expression for a valid assignment as described above,
and also print the corresponding assignment.
Note:
The given graph need not be connected.
• If there are multiple possible assignments, output anyone.
• Since the input is large, prefer using fast input-output methods.
Input
1
57
12
13
14
23
24
25
35
Output
2
43251
Q
Chapter 22 Solutions
Introduction to Algorithms
Ch. 22.1 - Prob. 1ECh. 22.1 - Prob. 2ECh. 22.1 - Prob. 3ECh. 22.1 - Prob. 4ECh. 22.1 - Prob. 5ECh. 22.1 - Prob. 6ECh. 22.1 - Prob. 7ECh. 22.1 - Prob. 8ECh. 22.2 - Prob. 1ECh. 22.2 - Prob. 2E
Ch. 22.2 - Prob. 3ECh. 22.2 - Prob. 4ECh. 22.2 - Prob. 5ECh. 22.2 - Prob. 6ECh. 22.2 - Prob. 7ECh. 22.2 - Prob. 8ECh. 22.2 - Prob. 9ECh. 22.3 - Prob. 1ECh. 22.3 - Prob. 2ECh. 22.3 - Prob. 3ECh. 22.3 - Prob. 4ECh. 22.3 - Prob. 5ECh. 22.3 - Prob. 6ECh. 22.3 - Prob. 7ECh. 22.3 - Prob. 8ECh. 22.3 - Prob. 9ECh. 22.3 - Prob. 10ECh. 22.3 - Prob. 11ECh. 22.3 - Prob. 12ECh. 22.3 - Prob. 13ECh. 22.4 - Prob. 1ECh. 22.4 - Prob. 2ECh. 22.4 - Prob. 3ECh. 22.4 - Prob. 4ECh. 22.4 - Prob. 5ECh. 22.5 - Prob. 1ECh. 22.5 - Prob. 2ECh. 22.5 - Prob. 3ECh. 22.5 - Prob. 4ECh. 22.5 - Prob. 5ECh. 22.5 - Prob. 6ECh. 22.5 - Prob. 7ECh. 22 - Prob. 1PCh. 22 - Prob. 2PCh. 22 - Prob. 3PCh. 22 - Prob. 4P
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- The Graph Data Structure is made up of nodes and edges. (A Tree Data Structure is a special kind of a Graph Data Structure). A Graph may be represented by an Adjacency Matrix or an Adjacency List. Through this exercise, you should be able to have a better grasp the Adjacency Matrix concept. You are expected to read about the Adjacency Matrix concept as well as the Adjacency List concept. Suppose the vertices A, B, C, D, E, F, G and H of a Graph are mapped to row and column indices(0,1,2,3,4,5,6,and 7) of a matrix (i.e. 2-dimensional array) as shown in the following table. Vertex of Graph Index in the 2-D Array Adjacency Matrix Representation of Graph A B 2 F 6. H 7 Suppose further, that the following is an Adjacency Matrix representing the Graph. 3 4 5. 6. 7 0. 1 1 1 1 01 1 01 1. 3 14 1 1 1 6. 1 Exercise: Show/Draw the Graph that is represented by the above Adjacency matrix. Upload the document that contains your result. (Filename: AdjacencyMatrixExercise.pdf) Notes: -The nodes of the…arrow_forwardI have employed two different traversal algorithms, namely, Algo I and Algo Il on a graph. Then I have shown the output of the two traversal algorithms in Figure -I and Figure -II. In both cases, the traversal starts at the node shown by the block right arrow. The nodes are numbered/labeled in the sequence they are visited. Identify the traversal algorithm for Figure -I and Figure -II and justify my answer. 3 COLOR 02x02 202020 (XD) CO COZOZO 8282620 () COS Figure I Figure I 02.02arrow_forwardWrite a program RandomSparseGraph to generate random sparse graphs for a well-chosen set of values of V and E such that you can use it torun meaningful empirical tests on graphs drawn from the Erdös-Renyi modelarrow_forward
- Do some outside research on depth-first traversal as it relates to traversing graphs. Then answer the following questions: a. Suppose you have an arbitrary connected graph G, shown in the image below. Use the vertex A as your starting point. Write out the order in which the algorithm could traverse the graph with a depth-first search, and explain your reasoning (there are multiple correct answers, hence the need for an explanation). b. Use a proof by induction to prove that when a depth-first traversal is performed, every vertex v in your graph G will have been visited at least one time. B D H E A G с I FLarrow_forwardRun experiments to determine empirically the average number ofvertices that are reachable from a randomly chosen vertex, for various digraph modelsarrow_forwardLet G (V, E) be a digraph in which every vertex is a source, or a sink, or both a sink and a source. (a) Prove that G has neither self-loops nor anti-parallel edges.arrow_forward
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