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write 500–700 words  (4 paragraphs) that respond to the following questions with your thoughts, ideas, and comments. This will be the foundation for future discussions by your classmates. Be substantive and clear, and use examples to reinforce your ideas.

Discuss and analyze the greedy paradigm. This paradigm, like divide and conquer, is fairly intuitive, and programmers likely use it in their everyday lives. Complete the following:

  • Discuss 2 scenarios where you use or might use this scheme in everyday life.
  • Be sure to detail the constraints of the scenario, and detail how and why your approach is a greedy approach.

Responses to Other Students: Respond to at least 2 of your fellow classmates with a reply of 100–200 words about their Primary Task Response regarding items you found to be compelling and enlightening. To help you with your discussion, please consider the following questions:

  • What did you learn from your classmate’s posting?
  • What additional questions do you have after reading the posting?
  • What clarification do you need regarding the posting?
  • What differences or similarities do you see between your posting and other classmates’ postings?

write 500–700 words (4 paragraphs) that respond to the following questions with your thoughts, ideas, and comments. This will be the foundation for future discussions by your classmates. Be substanti
Olawale A greedy algorithm is an algorithm that attempts to find possible ways to solve problems by taking the best optimal options at each stage. for instance, not all plans materialize into existence. Some will not or will not, which means this algorithm is a trial and error methodology. For this discussion, I will give two possible scenarios. 1. one of the greedy algorithms that provide optimal results is Dijkstra’s algorithm with a guaranteed shortest path in solving problems within a graph from one vertex to another’s vertices, according to M.u.s.e . This algorithm continues to search for the best approach to a closeth vertex with a minimum expense. For example, take a google map from our car G.P.S. or Smartphone for a direction; the idea is to look for the shortest distance to the final destination meaning the most straightforward path within two distances. The algorithm denoted street map as a graph and estimated driving time as edge weight. The algorithm uses four lumens steps to locate the shortest possible path. 1. identify the ending vertex with a distance of zero. Initialize this vertex as current. 2. identify all vertices leading to the current vertex. Calculate their distance apart which will require adding the most recent edge. If the distance is more significant than the initially recorded distance will not be recorded. Please view this attach youtube view for a more in-depth understanding. https://www.youtube.com/watch?v=KvRwplnIoEM 2. scenario considering building a distributed network telecommunication. what comes to mind is how to minimize network connective channels and ports for proper routine and cost-saving or efficiency. The telecommunication network will use the minimum spanning tree, part of greedy algorithms, to solve the problem. This algorithm selects the best edge and adds it to the spanning trees. The main idea is to set up a connection that uses a lease network connection to all branch locations. The minimum spanning tree is a sub-graph of the edges of an associated, edge-weighted undirected graph that connects all the vertices without any circuit and with the minimum possible total edge weight according to GeeksforGeeks. The main idea of the algorithm is not to create a circuit. Starting from the lowest edge in the graph, add a new, unused edge, except adding an edge would lead to a circuit. Repeat the process until a minimum spanning tree is formed. Please view this attach youtube view for a more in-depth understanding. Samicchya A greedy algorithm is a straightforward method for solving optimization problems. As it tries to determine the best approach to tackle the entire problem, the algorithm selects the best choice at each phase. Huffman numeric encoding, which is used to compress data, and the Dijkstra algorithm, which is used to determine the shortest path through a graph, are examples of greedy algorithms that work well in particular situations. For many issues, a greedy strategy does not deliver the best solution. The greedy algorithm has two stages for solving any problem.  1. Scanning the list of items 2. Optimization In many cases, a greedy technique does not yield the best result. However, according to the understanding, greedy algorithms are optimal locally but not necessarily worldwide. Assume you have an objective function that needs to be optimized (either maximized or minimized) at some point in the future. To ensure that the objective function is optimal, a greedy algorithm takes greedy decisions at each step. The Greedy algorithm only has one chance to find the best solution, therefore it never goes back and reverses the decision. The greedy algorithm is used to address the problems in the following two cases. Scenario 1: Knapsack Problem – Example: A corporation is holding a fresher’s recruitment exam. There are 12 questions on the exam, each worth ten points. It is a diverse test with a maximum score of 125 points. Freshmen are expected to respond to all inquiries to the best of their abilities. Out of all the potential subsets of problems with total point values of 100, choose one. The issue at hand is as follows: Determine which subset provides the maximum potential score for each student. Scenario 2: Job Sequencing Problem – Given an array of jobs, each with a deadline and a profit if completed before the deadline, solve this problem. Given that each job requires a single unit of time, the shortest possible deadline for any job is 1. When just one work can be booked at a time, how can overall profit be maximized?