A Case Study on Greedy Algorithm - Myassignmenthelp.com

Question: Discuss and analyze the greedy paradigm. This paradigm, like divide and conquer, is fairly intuitive, and programmers likely use it in their everyday lives? Answer: Introduction: A greedy algorithm is a mathematical process that helps to implement most easy solution for the multi-stage, complex problems by deciding which is possible solution is giving the utmost benefit. Such algorithms are known as greedy method as the optimal solution to each smaller instance will provide an instantaneous output and the respective algorithm looks for the smaller problem domain. There are numbers of algorithms are there which are in use, such as Dijkstras algorithm to find the best possible path by placing the start node for all of the nodes, Prims algorithm to find the minimum spanning tree and Huffman trees to compress data while they are moving across the network. Scenario 1: Greedy algorithm is used to find the shortest path strategy using the Dijkstras algorithm. There are numbers of applications are available across the industry to determine the best possible solutions those are found to meet the solution with less time and cost. Applications those have been built to give best possible path to reach up to a certain distance across the world. There are numbers of route-finding app. In a daily basis, there is lots of people commerce their journeys of several distances and sometime it is required for us to indentify most efficient path that can reduce cost as well as time (Blanchard Tanner, 2014). Scenario 2: There are various algorithms for classical optimization of problems. The use of the methods for generation of minimum spanning tree and optimal prefix codes for data compression provides the best possible solution using greedy algorithm. For example, in a given scenario of peer-to-peer communication network, there are numbers of communication network that made up of set of networks and allows a bunch of bi-directional optical fibre communication channels between them. To set up a graph for the intermediate nodes are needed to be connected for the smooth flow of network. In this scenario greedy algorithm give the utmost privilege by reducing the cost of maintenance of the network on the basis of time, cost and all of these parameters are expressed with the help of non-negative number (Hibi Fujito, 2015). Conclusion: Greedy algorithms are fast and optimum for certain problems. The problem most of the time with the greedy method obtains some local or the small optimum result not the global one, for example, there are numbers of boxes with fixed size and the goal is to reduce the use of numbers of boxes. The boxes are placed in a queue as per their best fitted position. In this boxes are placed as per their best possible situation but cannot be replaced for twice. Greedy algorithms generally work by using the recursive methods and construct the problem dividing problem set into chunks of similar problem. The advantage is that the solutions are straight forward and very easy to depict. As far as the disadvantages are concerned, for every chunk of problem, short term solutions are generated that may cause long-term outcome. References Blanchard, J., Tanner, J. (2014). Performance comparisons of greedy algorithms in compressed sensing. Numerical Linear Algebra With Applications, 22(2), 254-282. doi:10.1002/nla.1948 Hibi, T., Fujito, T. (2015). Multi-rooted Greedy Approximation of Directed Steiner Trees with Applications. Algorithmica. doi:10.1007/s00453-015-9973-1 Iwen, M., Krahmer, F. (2015). Fast Subspace Approximation Via Greedy Least-Squares. Constr Approx. doi:10.1007/s00365-014-9273-z Velzquez-Iturbide, J. (2013). An Experimental Method for the Active Learning of Greedy Algorithms. Trans. Comput. Educ., 13(4), 1-23. doi:10.1145/2534972