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Show that for any integer n that is a power of 2 , there is an instance of the set cover problem (Section 5.4) with the following properties:

  1. There are n elements in the base set.
  2. The optimal cover uses just two sets.
  3. The greedy algorithm picks at least log n sets.

Thus the approximation ratio we derived in the chapter is tight.

Short Answer

Expert verified

Any integer which count with 2 or count with power of 2 it gives elements in base set and optimal cover using two set in greedy algorithm.

Step by step solution

01

Elements of Even /Odd Numbers

Represent a set with n items that would be a positive integer of 2 . Permit the collection to be. (1,2,...,2k) for some K2.

So ideal number of matches would be two. • One has even numbers, while the other has odd numbers. As a result, the two sets will be localid="1658922602021" X=1,3,...,2k-3,2k-1 andY=2,4,...,2k-2,2k.Y=2,4,...,2k-2,2k..

02

Algorithm of number of 2 ’s in odd / even calculation

Now, Take, for example, a greedy algorithm.

Take into account everything. i ,

Let Di=2(k-1),2(k-i)+1,...,2k-i+1.

Clearly,

A=i=1kD1

Asn=2k

Thus,logn=k

Its foundation setting is the combination of sets handled by greedy.

• Although X and Y do not cover and over half of both the elements, they provide the best solution.

D1, on the other hand, actually covers one additional vertex than X or Y .

As a result, there is a close approximated ratio among both greedy and optimum solutions.

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Most popular questions from this chapter

Sometimes we want light spanning trees with certain special properties. Here’s an example.

Input: Undirected graph G=(V,E) ; edge weights we; subset of vertices UV

Output: The lightest spanning tree in which the nodes of U are leaves (there might be other leaves in this tree as well).

(The answer isn’t necessarily a minimum spanning tree.)

Give an algorithm for this problem which runs in O(ElogV) time. (Hint: When you remove nodes Ufrom the optimal solution, what is left?)

Consider an undirected graph G=(V,E)with nonnegative edge weights role="math" localid="1658915178951" we0. Suppose that you have computed a minimum spanning tree of G, and that you have also computed shortest paths to all nodes from a particular node role="math" localid="1658915296891" sV. Now suppose each edge weight is increased by 1: the new weights are w0e=we+1.

(a) Does the minimum spanning tree change? Give an example where it changes or prove it cannot change.

(b) Do the shortest paths change? Give an example where they change or prove they cannot change.

Show how to find the maximum spanning tree of a graph, that is , the spanning tree of largest total weight.

Suppose we want to find the minimum spanning tree of the following graph.

(a) Run Prim’s algorithm; whenever there is a choice of nodes, always use alphabetic ordering (e.g., start from node A). Draw a table showing the intermediate values of the cost array.

(b) Run Kruskal’s algorithm on the same graph. Show how the disjoint-sets data structure looks at every intermediate stage (including the structure of the directed trees), assuming path compression is used.

Consider the following graph.

(a) What is the cost of its minimum spanning tree?

(b) How many minimum spanning trees does it have?

(c) Suppose Kruskal’s algorithm is run on this graph. In what order are the edges added to the MST? For each edge in this sequence, give a cut that justifies its addition.

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