EBK DATA STRUCTURES AND ALGORITHMS IN C
4th Edition
ISBN: 9781285415017
Author: DROZDEK
Publisher: YUZU
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Show all the steps of Kruskal''s minimum cost spanning tree algorithm for a complete graph of 6 vertices where the weight of the edge between the distinct vertices i and j is |i-j-1|, for 1 <= i, j <= 6.
The algorithm is based on being able to efficiently collapse the vertices of G that belong to the same club of T into a single vertex, remove all loops from the resulting graph, and keep only a minimum-weight edge between any pair of new vertices. Let Gt denote the ''collapsed" version of G right after the tth stage of the algorithm. Stage 0 of the algorithm is defined by setting G0 = G = (V, E) and T = (V, ). The tth stage of the algorithm is defined by the following steps
Johnson’s algorithm with the tightest asymptotic upper bound for its worst-case running time is O(n2logn)
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EBK DATA STRUCTURES AND ALGORITHMS IN C
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- please answer both of the questions. 7. The Bellman-Ford algorithm for single-source shortest paths on a graph G(V,E) as discussed in class has a running time of O|V |3, where |V | is the number of vertices in the given graph. However, when the graph is sparse (i.e., |E| << |V |2), then this running time can be improved to O(|V ||E|). Describe how how this can be done.. 8. Let G(V,E) be an undirected graph such that each vertex has an even degree. Design an O(|V |+ |E|) time algorithm to direct the edges of G such that, for each vertex, the outdegree is equal to the indegree. Please give proper explanation and typed answer only.arrow_forwardgive an example of a graph on n vertices where ford and fulkerson algorithm achieve its worst time complexityarrow_forwardLet DIST (u, v) denote the distance between vertex u and v. It is well known that distances in graphs satisfy the triangle inequality. That is, for any three vertices u, v, w, DIST (u, v) ≤ DIST (u, w) + DIST (w, v). Let D∗ denote the distance between the two farthest nodes in G. Show that for any vertex s D∗ ≤ 2 max DIST (s, v).arrow_forward
- The following table presents the implementation of Dijkstra's algorithm on the evaluated graph G with 8 vertices. a) What do the marks (0, {a}) and (∞, {x}) in the 1st row of the table mean? b) What do the marks marked in blue in the table mean? c) Reconstruct all edges of the graph G resulting from the first 5 rows of the table of Dijkstra's algorithm. d) How many different shortest paths exist in the graph G between the vertices a and g?arrow_forwardImplement the dijkstra's algorithm on a directed graph from a given vertex. all edges have non-negative edge weights. Output the edge as they are added to the shortest path trees. Compute and print the weight of the shortest path to every reachable vertex. The source vertex is S. show the following: -program code -Screenshot of the output -Representation of the graph transversalarrow_forwardLet G = (X ∪ Y, E) be a bipartite graph such that the vertices are partitioned into two groups Xand Y , and each edge has one end point in X and one end point in Y .A 2-1 generalized matching is a set of edges S ⊂ E satisfying the following two conditions:1. Every vertex in X belongs to at most two edges in S.2. Every vertex in Y belongs to at most one edge in S.Give an algorithm to find the size (number of edges) of maximum 2-1 generalized matchingarrow_forward
- True or false: For graphs with negative weights, one workaround to be able to use Dijkstra’s algorithm (instead of Bellman-Ford) would be to simply make all edge weights positive; for example, if the most negative weight in a graph is -8, then we can simply add +8 to all weights, compute the shortest path, then decrease all weights by -8 to return to the original graph. Select one: True Falsearrow_forward3. From the graph above determine the vertex sequence of the shortest path connecting the following pairs of vertex and give each length: a. V & W b. U & Y c. U & X d. S & V e. S & Z 4. For each pair of vertex in no. 3 give the vertex sequence of the longest path connecting them that repeat no edges. Is there a longest path connecting them?arrow_forwardFrom the given information please provide a greedy algorithm and estimate it's time complexity. (will thumbs up) Given: a graph G, find the smallest integer k such that the vertex set V of G contains a set A consisting of k elements satisfying the condition: for each edge of G at least one of its ends is in A. The size of the problem is the number n of vertices in G.arrow_forward
- Given an undirected, weighted graph G(V, E) with n vertices and m edges, design an (O(m + n)) algorithm to compute a graph G1 (V, E1 ) on the same set of vertices, where E1 subset of E and E1 contains the k edges with the smallest edge weights , where k < m.arrow_forwardProvide an eficient algorithm that given a directed graph G with n vertices and m edges as input, finds the outdegree of each vertex in G. Note that outdegree of a vertex u is the number of edges directed from u to some other vertex v. Discuss the running-time of your algorithm and Provide an algorithm that given a directed graph G with n vertices and m edges as input, nds the indegree of each vertex in G. Note that indegree of a vertex u is the number of edges directed into u from some other vertex v. Discuss the running-time of your algorithm.arrow_forward- If an algorithm for a graph problem with |V | vertices and |E| edges has two stages where the first stage is O(|E|) and the second stage which follows after this is O(|V |) then what is the best big-O set for the algorithm overall in terms of |V | on sparse graphs? Consider sparse graphs to have |E| = O(|V |).arrow_forward
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