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You are given a directed graph with (possibly negative) weighted edges, in which the shortest path between any two vertices is guaranteed to have at most edges. Give an algorithm that finds the shortest path between two vertices u and v in O(KE)time.

Short Answer

Expert verified

Bellman Ford algorithm in which the edges have negative weights and it finds the shortest path between two vertices inOKE time.

Step by step solution

01

 Step 1: Algorithm used for negative weighted directed graph

Bellman-Ford algorithmis an application of a single source shortest path, which is used forfinding the shortest distance from one vertex to other vertices of a weighted directed graph.

It is almost similar to Dijkstra's algorithm but Dijkstra's algorithm works only for the graph with a positive weight and Bellman-Ford algorithm works with graphs in which edges have negative weights in its graph.

02

Design the Algorithm

Bellman-Ford algorithm applies to the graph for finding the single source’s shortest path.A directed graph with positive and negative edge weight, and returns the length of the shortest cycle in the graph and the graph is acyclic, which takes time at mostOKE So, here the vertex A is the source vertex. Now take an array as a data structure to evaluate a single source’s shortest path between the source and the destination.

From A the distance of A is zero and take the distance of vertex A from each and every vertex is infinity. Now take A as the first vertex and evaluate the weight towards each vertex. Draw a directed positive and negative weighted graph as shown below:

Choose the next vertex from the vertices which have minimum weight and select that node as the second vertex. Then again evaluate the distance of it from every vertex and as get the minimum weight of the node and consider it as the main node. Through this the series of the vertex arises.

Here the vertex A is the source vertex. now take a minheap as a data structure for evaluate single source shortest path between the source and the destination.

From A the distance of A is zero and take the distance of vertex A from each and every vertex is infinity.

All vertices will be released many times in Bellman Ford algorithm.

Select every vertex one by one and put it into the array as a data structure one by one as shown in the figure.

Hence, the shortest distance from the vertex A to vertex D is evaluate in OKEtime.

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Most popular questions from this chapter

Shortest path algorithms can be applied in currency trading. Let c1,c2,cn be various currencies; for instance, c1might be dollars, c2pounds, and c3 lire.

For any two currencies ci and cj , there is an exchange rate τi,j; this means that you can purchase τi,j units of currency cj in exchange for one unit of cj. These exchange rates satisfy the condition that rij.rji<1 so that if you start with a unit of currency cj, change it into currency and then convert back to currency localid="1658917254028" ci, you end up with less than one unit of currency ci (the difference is the cost of the transaction).

a. Give an efficient algorithm for the following problem: Given a set of exchange rates rij , and two currencies s and t , find the most advantageous sequence of currency exchanges for converting currency into currency . Toward this goal, you should represent the currencies and rates by a graph whose edge lengths are real numbers.

The exchange rates are updated frequently, rejecting the demand and supply of the various currencies. Occasionally the exchange rates satisfy the following property: there is a sequence of currencies ci1,ci2,.......ciksuch that ri1,ri2.i3,.........ri(k-1),ik,rik+1>1. This means that by starting with a unit of currency ci1and then successively converting it to currencies ci1,ci2.......cik, and finally back to ci1, you would end up with more than one unit of currency ci1 . Such anomalies Last only a fraction of a minute on the currency exchange, but they provide an opportunity for risk-free profits.

b. Give an efficientalgorithm for detecting the presence of such an anomaly. Use the graph representation you found above.

Shortest paths are not always unique: sometimes there are two or more different paths with the minimum possible length. Show how to solve the following problem in O((|V|+|E|)log|V|)time.

Input:An undirected graph G=(V,E);edge lengths le>0; starting vertex s∈V.

Output:A Boolean array for each node u , the entry usp[u]should be true if and only if there is a unique shortest path s to u (Note:usp[s]=true)

Section 4.5.2 describes a way of storing a complete binary tree of n nodes in an array indexed by 1, 2, K, n .

(a) Consider the node at position j of the array. Show that its parent is at position [j2]and its children are at 2 jand 2 j + 1 (if these numbers are ≤n).

(b) What the corresponding indices when a complete d-ary tree is stored in an array?

Figure 4.16 shows pseudocode for a binary heap, modeled on an exposition by R.E. Tarjan. The heap is stored as an array , which is assumed to support two constant-time operations:

  • |h|, which returns the number of elements currently in the array;
  • h-1, which returns the position of an element within the array.

The latter can always be achieved by maintaining the values of h-1as an auxiliary array.

(c) Show that themakeheapprocedure takesO(n) time when called on a set of elements. What is the worst-case input? (Hint:Start by showing that the running time is at most ∑1=1nlog(ni)).

(d) What needs to be changed to adapt this pseudocode to d-ary heaps?

You are given a strongly connected directed graph G=(V,E) with positive edge weights along with a particularv0∈V . Give an efficient algorithm for finding shortest paths between all pairs of nodes, with the one restriction that these paths must all pass throughv0 .

In cases where there are several different shortest paths between two nodes (and edges have varying length),the most convenient of these paths is often the one with fewest edges. Forinstance, if nodes represent cities and edge lengths represent costs of flying between cities, theremight be many ways to get from cityto city t which all have the same cost. The mostconvenientof these alternatives is the one which involves the fewest stopovers. Accordingly, for a specific starting node S , define

bestu=minimum number of edges in a shortest path from S to u .

In the example below, thebestvalues for nodes S,A,B,C,D,E,Fare 0,1,1,1,2,2,3, respectively.

Give an efficient algorithm for the following problem.

Input:Graph G=V,E; positive edge lengths le; starting node s∈V.

Output: The values of bestu should be set for all nodesu∈V

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