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 23, Problem 1P

(a)

Program Plan Intro

To show that the minimum spanning tree is unique but not the second best spanning tree.

(a)

Expert Solution
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Explanation of Solution

Suppose there are 4 vertices { p, q, r, s } in the following graph. Consider the edge weights and vertices as follows:

  Introduction to Algorithms, Chapter 23, Problem 1P

The minimum spanning tree has weight of 7 and there exists two second best minimum spanning tree having weight 8.

(b)

Program Plan Intro

To prove that there exists edges ( u , v ) Tand ( x , y ) Tsuc that T{(u,v)}{(x,y)} is a second-best minimum spanning tree of G , where T is the minimum spanning tree.

(b)

Expert Solution
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Explanation of Solution

T is the minimum spanning tree of graph G . Consider adding an edge that is ( u , v ) T after removing another edge from the particular path that exists between u , v . This replacement of edge will certainly raise weight of the tree. Suppose two or more edges gets replaced, then the new tree will certainly not be better than next best minimum spanning tree.

(c)

Program Plan Intro

To describe an O ( V2 )-time algorithm that, given T , computes maximum of [ u , v ] V

(c)

Expert Solution
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Explanation of Solution

The algorithm is as follows:

Consider using dynamic programming approach. For u , v , consider the path from u , v and identify vertex x that is supposed to be occurring right after u . Then, maximum of [ u , v ] is equated to maximum of w ( u , x ) and maximum of [ w , v ]. At last, analyze the case where u , vare adjacent to each other, in this case the max edge weight is the only edge by which they are connected. Once the value of x is found in a constant time, then the program will run in time complexity of O ( V2 ).

(d)

Program Plan Intro

To give an efficient algorithm to compute the second-best minimum spanning tree of graph G.

(d)

Expert Solution
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Explanation of Solution

Firstly, compute the minimum spanning tree in O ( E + Vlog ( V )), this is in time O ( V2 ). Use part c )t to compute maximum double array. Run a min over every pair of u , v vertices, of value w(u,v)max[u,v] . The weight is considered to be infinite in case no edge is found. After that, the pair that is found to be in min value of the difference, remove from min spanning tree and to the edge, an edge occurring in the path of u , v contains weight maximum u , v .

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Students have asked these similar questions
Which is true/false. Let G be a connected undirected weighted graph. Consider the following two statements. S₁: There exists a minimum weight edge in G which is present in every minimum spanning tree of G. S2: If every edge in G has distinct weight, then G has a unique minimum spanning tree.
Consider the so-called k-Minimum Spanning Tree (k-MST) problem, which is defined as follows. An instance of the k-MST problem is given by a connected undirected graph G=(V,E) with edge weights w:E→Q and a natural number k>2. The question is to find a tree with exactly k nodes that is a subgraph of G and minimises the weight among all such trees. Informally, k-MST is the variant of the minimum spanning tree problem, where instead of a spanning tree one wants to find a tree with exactly k nodes. What would be the result if we apply Prim's, respectively Kruskal's, algorithm to the problem by stopping both algorithms after k−1 edges have been added? In the following we refer to these versions of Prim's and Kruskal's algorithm as the modified algorithm of Prime or Kruskal, respectively.   a) consider the following graph. (image provided- image 1) For which of the following edge weights, assigned to the graph above, does the modified algorithm of Kruskal provide a wrong result assuming…
Let A, B, C, D be the vertices of a square with side length 100. If we want to create a minimum-weight spanning tree to connect these four vertices, clearly this spanning tree would have total weight 300 (e.g. we can connect AB, BC, and CD). But what if we are able to add extra vertices inside the square, and use these additional vertices in constructing our spanning tree? Would the minimum-weight spanning tree have total weight less than 300? And if so, where should these additional vertices be placed to minimize the total weight? Let G be a graph with the vertices A, B, C, D, and possibly one or more additional vertices that can be placed anywhere you want on the (two-dimensional) plane containing the four vertices of the square. Determine the smallest total weight for the minimum-weight spanning tree of G. Round your answer to the nearest integer. Note: I encourage you to add n additional points (for n=1, 2, 3) to your graph and see if you can figure out where these point(s) need to…
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