a. Removing the back edge will result in a graph with no back edges, and thus a graph with no cycles (as every graph with at least one cycle has at least one back edge). Notice that a graph can have two cycles but a single back edge, thus removing some edge that disrupts that cycle is insufficient, you have to remove specifically the back edge. For example, in graph G = (V, E) = ({a, b, c}, {(a, b),(b, c),(a, c),(c, a)}), there are two cycles [a, b, c, a] and [a, c, a], but only one back edge (c, a). Removing edge (b, c) disrupts one of the cycles that gave rise to the back edge ([a, b, c, a]), but another cycle remains, [a, c, a].
Other answer is incorrect Problem 1. (15 points) Consider an undirected connected graph G = (V,...
An orientation of an undirected graph G = (V, E) is an assignment of a direction to each edge e ∈ E. An acyclic orientation is the assignment of a direction to every edge such that the resulting directed graph contains no cycles. Either prove that there exist undirected graphs with no acyclic orientation, or provide an efficient O(V +E) algorithm for producing an acyclic orientation for an undirected graph G and explain why it produces a valid acyclic orientation.
2. Let G = (V, E) be an undirected connected graph with n vertices and with an edge-weight function w : E → Z. An edge (u, v) ∈ E is sparkling if it is contained in some minimum spanning tree (MST) of G. The computational problem is to return the set of all sparkling edges in E. Describe an efficient algorithm for this computational problem. You do not need to use pseudocode. What is the asymptotic time complexity of...
Problem 5. (15 marks) Given a connected, undirected, weighted graph G- (V, E), define the cost of a spanning tree to be the maximum weight among the weights associated with the edges of the spanning tree. Design an efficient algorithm to find the spanning tree of G which maximize above defined cost What is the complexity of your algorithm.
Let G = (V, E, W) be a connected weighted graph where each edge e has an associated non-negative weight w(e). We call a subset of edges F subset of E unseparating if the graph G' = (V, E\F) is connected. This means that if you remove all of the edges F from the original edge set, this new graph is still connected. For a set of edges E' subset of E the weight of the set is just the...
MST For an undirected graph G = (V, E) with weights w(e) > 0 for each edge e ∈ E, you are given a MST T. Unfortunately one of the edges e* = (u, z) which is in the MST T is deleted from the graph G (no other edges change). Give an algorithm to build a MST for the new graph. Your algorithm should start from T. Note: G is connected, and G − e* is also connected. Explain...
Consider an unweighted, undirected graph G = 〈V, E). The neighbourhood of a node u E V in the graph is the set of all nodes that are adjacent (or directly connected) to v. Subsequently, we can define the neighbourhood degree of the node v as the sum of the degrees of all its neighbours (those nodes that are directly connects to v) (a) Design an algorithm that returns a list containing the neighbourhood degree for each node v V,...
This question needs to be done using pseudocode (not any particular programming language). Thanks Consider an unweighted, undirected graph G = 〈V, E). The neighbourhood of a node u E V in the graph is the set of all nodes that are adjacent (or directly connected) to v. Subsequently, we can define the neighbourhood degree of the node v as the sum of the degrees of all its neighbours (those nodes that are directly connects to v) (a) Design an...
Problem 3's picture are given below. 5. (a) Let G = (V, E) be a weighted connected undirected simple graph. For n 1, let cycles in G. Modify {e1, e2,.. . ,en} be a subset of edges (from E) that includes no Kruskal's algorithm in order to obtain a spanning tree of G that is minimal among all the spanning trees of G that include the edges e1, e2, . . . , Cn. (b) Apply your algorithm in (a)...
1. Here is a randomized algorithm for MAX-CUT on an undirected graph G = (V,E): 1. Initialize S to be the empty set 2. For every vertex v in V: 3. Put v into S with probability 1/2 4. Let T = V\S be the complement of S 5. Output (S,T) Recall that E(S, T) CE is the set of edges that have one endpoint in S and the other endpoint in T, ie., E(S, T) = {(u, u) :...
Let G = (V, E) be a weighted undirected connected graph that contains a cycle. Let k ∈ E be the edge with maximum weight among all edges in the cycle. Prove that G has a minimum spanning tree NOT including k.