Greedy algorithm presentations final year.pptx
- 1. Seminar on Greedy Algorithm PRESENTED BY: SANIKA KATOLE CSE : B.TECH SECOND YEAR ROLL NO. : 25
- 2. J. T. Mahajan College Of Engineering, Faizpur Guide By Prof. M. S. Chaudhari Presented By Sanika Nandkishor Katole Department of Computer Engineering Second Year
- 3. Contents Introduction What is Greedy Algorithm ? Application of Greedy Algorithm Advantages of Greedy Algorithm Disadvantages of Greedy Algorithm Conclusion Reference
- 4. Introduction Greedy algorithm defined as a method for solving optimization problems by taking decisions that result in the most evident and immediate benefit irrespective of the final outcome. It works for cases where minimization or maximization leads to the required solution.
- 5. What is Greedy Algorithm ? Greedy Algorithm is a simple yet powerful algorithmic technique that makes the locally optimal choice at each stage with the hope of finding a global optimum.
- 6. Applications of Greedy Algorithm It is used in finding the shortest path. It is used to find the minimum spanning tree using the prim’s algorithm or the Kruskal’s algorithm. It is used in a job sequencing with a deadline. This algorithm is also used to solve the fractional knapsack problem.
- 7. Advantages of Greedy Algorithm Simplicity – Greedy algorithms are often simple and easy to understand, making them accessible to programmers of all skill levels. Efficiency – Greedy algorithms can be very efficient in terms of time and space complexity. They typically involve iterating through a problem’s inputs once, and their solutions often have linear or logarithmic time complexity. Flexibility – Greedy algorithms can be applied to a wide range of problems, from scheduling to graph traversal to data compression. They are a versatile tool that can be used in many different contexts.
- 8. Disadvantages of Greedy Algorithm Local Optimality – Greedy algorithms make locally optimal choices at each step, without considering the larger context. This means that they may not always lead to the globally optimal solution. Lack of Backtracking – Once a greedy algorithm makes a decision, it can be difficult or impossible to backtrack and undo that decision. This can lead to suboptimal solutions or even incorrect results. Sensitivity to Input – Greedy algorithms are often sensitive to the order and structure of the input data. Changing the input can result in significantly different output, making it difficult to generalize the algorithm to new contexts.
- 9. Conclusion In conclusion, greedy algorithms offer a straightforward and efficient approach to solving optimization problems by making locally optimal choices at each step. Their simplicity, ease of implementation, and low time complexity make them valuable in various applications across different domains.
- 10. Reference Black, Paul E. (2 February 2005). “greedy algorithm”. Dictionary of Algorithms and Data Structures. U.S. National Institute of Standards and Technology (NIST). Retrieved 17 August 2012. Cormen et al. 2001, Ch. 16 Gutin, Gregory; Yeo, Anders; Zverovich, Alexey (2002). “Traveling salesman should not be greedy: Domination analysis of greedy- type heuristics for the TSP”. Discrete Applied Mathematics.
- 11. Thank You