Introduction to Algorithms
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 24, Problem 1P

(a)

Program Plan Intro

To represent the graph’s acyclic topological nature.

(a)

Expert Solution
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Explanation of Solution

Given Information: A graph G=(V,E) that is having an arbitrary linear order of vertices v1,v2....,v|V| and edges set partitioned into EfEb . Where Ef={(vi,vj)E:i<j} and Eb={(vi,vj)E:i>j} . This graph contains no self-loop therefore every edges is in either Ef or Eb .

Explanation:

As mentioned, graph does not contained any self-loop therefore; every edge in graph Gf will go from higher index vertices to lower indexes vertices. It means, it will never come back to same index where it was started and viceversa .

It means Gf is acyclic and its vertices (v1,v2,.......,v|V|) are sorted with topologically. As in same case, vertices (v|V|,.......,v2,v1) for graph Gb are sorted topologically.

(b)

Program Plan Intro

To represent the Yen’s improvement in Bellman-Ford algorithm.

(b)

Expert Solution
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Explanation of Solution

Given Information: Implementation of Bellman- Ford algorithm and a graph with relaxing edges of Ef and Eb .

Explanation:

Bellman- Ford algorithm is simpler than Dijkstra’s algorithm and worked very well with distributed systems. In the first step, it calculate the shortest path that is having at-most one edge in bottom-up approach then it will go for at most 2-edges and so-on up to ith iterations. This process proved that there is no negative weight cycle.

In Bellman- Ford algorithm and previous part a declare that the graph’s edges Ef are going in increasing order and Eb are going in decreasing order. It means any sequence {ki}i of vertices’ length can change from |V| to ||V|/2| times and it initially expected that vertices will increase their indexes before it run through Ef and Eb . For this change, vertices will increase |v+1| with respect to represent the sources in ordering of the vertices |vk2| .

(c)

Program Plan Intro

To calculate the effects of running time complexity for Bellman-Ford algorithm.

(c)

Expert Solution
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Explanation of Solution

Given Information: Implementation of Bellman- Ford algorithm and a scheme for evaluating the time complexity.

Explanation:

It cannot improve the asymptotic running time of Bellman-Ford algorithm becausehaving a co-efficient of 1 to 12 .It will drop the runtime of the algorithm.

The final runtime for the algorithm will be Ο(EV) .

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We are given a directed graph G = (V, E) with edge weights w(e) for e E E. The edge weights are allowed to be negative. Let -C be the minimum value of the edge weights. Create a new set of edge weights w' (e) = w(e) + C. Thus these new weights are non-negative: w' (e) > 0 for all e € E. Run Dijkstra's algorithm from a specified start vertex s E V on G with these new weights w'. For every graph G, every weights w, and every s € V, the above algorithm is guaranteed to find the shortest paths in G (not the actual lengths but the paths) from s with respect to the original weights w: True False If you entered true, provide a short explanation. If you entered false provide a counterexample (show the graph and what the algorithm produces vs. the correct solution).
Consider the Minimum-Weight-Cycle Problem: Input: A directed weighted graph G = :(V, E) (where the weight of edge e is w(e)) and an integer k. Output: TRUE if there is a cycle with total weight at most k and FALSE if there is no cycle with total weight at most k. Remember, a cycle is a list of vertices such that each vertex has an edge to the next and the final vertex has an edge to the first vertex. Each vertex can only occur once in the cycle. A vertex with a self-loop forms a cycle by itself. (a) Assume that all edge weights are positive. Give a polynomial-time algorithm for the Minimum-Weight-Cycle Problem. For full credit, you should: Give a clear description of your algorithm. If you give pseudocode, you should support it with an expla- nation of what the algorithm does. Give the running time of your algorithm in terms of the number of vertices n and the number of edges m. - You do not need to prove the correctness of your algorithm or the correctness of your running time…
One can manually count path lengths in a graph using adjacency matrices. Using the simple example below, produces the following adjacency matrix: A B A 1 1 B 1 0 This matrix means that given two vertices A and B in the graph above, there is a connection from A back to itself, and a two-way connection from A to B. To count the number of paths of length one, or direct connections in the graph, all one must do is count the number of 1s in the graph, three in this case, represented in letter notation as AA, AB, and BA. AA means that the connection starts and ends at A, AB means it starts at A and ends at B, and so on. However, counting the number of two-hop paths is a little more involved. The possibilities are AAA, ABA, and BAB, AAB, and BAA, making a total of five 2-hop paths. The 3-hop paths starting from A would be AAAA, AAAB, AABA, ABAA, and ABAB. Starting from B, the 3-hop paths are BAAA, BAAB, and BABA. Altogether, that would be eight 3-hop paths within this graph. Write a program…
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