Design a linear-time algorithm for the following task.

Input: A connected, undirected graphG.

Question:Is there an edge you can remove fromGwhile still leavingGconnected?

Can you reduce the running time of your algorithm toO(V)?

Short Answer

Expert verified

The linear-time algorithm to find an edge in a graph removing which the graph remains connected can be implemented by finding if the graph contains a cycle. And the running time of the algorithm can be reduced toO(|V|)by keeping the track of the visited vertices in the graph.

Step by step solution

01

Define Linear-time algorithm 

A linear-time algorithm is one in which the method's execution time is proportional to the amount of the input.The aim of the questions is to determine whether or not there is a cycle in the graph G(V,E).

Here, V is the vertex set and E is the edge set of the graph G .

If there is no cycle in the graph, there is only one route from one vertex to the other vertex. So, deleting an edge will result in unconnected components.

02

Determine an algorithm to check if graph contains a cycle

The only way to remove an edge from a graph without disconnecting it is if the graph has a cycle. In such instance, one of the cycle's edges can be deleted without causing the graph to be disconnected. The number of edges in a graph with no cycle is equal to one less than the number of vertices. The algorithm return 1, if deleting an edge does not disconnects the graph. Otherwise, the algorithm return 0. The algorithm is as follows:

  1. Calculate the number of vertices in the graph and store it in a variablecv.
  2. Calculate the number of edges in the graph and store it in a variablece.
  3. If the value ofceis higher than or equal tocv, return 1
  4. Else return 0

Calculating the number of vertices and edges in the graph takes the linear time.

03

Reduce the running time toO(V) 

Run the recursive graph traversal. While traversing the graph, the visited nodes are marked. Whenever the already visited node is encountered, the graph traversal return true. The whole graph is traversed if it does not contain cycle. The cycle is found in the graph after traversing edges equal to the number of vertices. The run time of the algorithm can be reduced toOV.

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Most popular questions from this chapter

Ternary Huffman. Trimedia Disks Inc. has developed “ternary” hard disks. Each cell on a disk can now store values 0,1, or 2(instead of just 0 or 1). To take advantage of this new technology, provide a modified Huffman algorithm for compressing sequences of characters from an alphabet of size n, where the characters occur with known frequencies f1, f2,...., fn. Your algorithm should encode each character with a variable-length codeword over the values 0,1,2, such that no codeword is a prefix of another codeword and so as to obtain the maximum possible compression. Prove that your algorithm is correct

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

We use Huffman's algorithm to obtain an encoding of alphabet {a,b,c}with frequencies fa,fb,fc. In each of the following cases, either give an example of frequencies (fa,fb,fc)that would yield the specified code, or explain why the code cannot possibly be obtained (no matter what the frequencies are).

(a) Code:{0,10,11}

(b) Code:{0,1,00}

(c) Code:{10,01,00}

The following statements may or may not be correct, In each case, either prove it (if it is correct) or give a counter-example (if it isn’t correct). Always assume that the graph G=(V,E)is undirected. Do not assume that edge weights are distinct unless this is specifically stated.

  1. If a graph G has more than |V|-1edges, and there is a unique heaviest edge, then this edge cannot be part of a minimum spanning tree.
  2. If G has a cycle with a unique heaviest edge e, then e cannot be part of any MST.
  3. Let e be any edge of minimum weight in G. Then e must be part of some MST.
  4. If the lightest edge in a graph is unique, then it must be part of every MST.
  5. If e is part of some MST of G, then it must be a lightest edge across some cut of .
  6. If G has a cycle with a unique lightest edge e must be part of every MST.
  7. The shortest-path tree computed by Dijkstra’s algorithm is necessarily an MST.
  8. The shortest path between two nodes is necessarily part of some MST.
  9. Prim’s algorithm works correctly when there are negative edges.
  10. (For any r>0, define an r-path to be a path whose edges all have weight <r). If G contains an r-path from node s to t , then every MST of G must also contain an r-path from node s to node t.

Give a linear-time algorithm that takes as input a tree and determines whether it has a perfect matching: a set of edges that touches each node exactly once.

A feedback edge set of an undirected graph G(V,E) is a subset of edgesE'Ethat intersects every cycle of the graph. Thus, removing the edges will render the graph acyclic.

Give an efficient algorithm for the following problem:

Input: Undirected graph G(V,E) with positive edge weights we.

Output: A feedback edge set E'Eminimum total weight eE'we.

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