Download pdf
Review on greedy algorithm
- 1 Southeast University
* Author to whom correspondence should be addressed.
Abstract
The greedy algorithm is a commonly used algorithm design idea that can provide efficient solutions to many practical problems. This paper aims to review and summarize the basic ideas, characteristics and application fields of greedy algorithms, and discuss their advantages and limitations. Firstly, the basic concepts of greedy algorithms are introduced, including the greedy selection properties and optimal substructures. Then, some classic greedy algorithms such as the backpack problem, the activity selection problem, and the minimum spanning tree problem are introduced, and the concept of time complexity is introduced. Next, the application of greedy algorithms in practical problems, such as scheduling problems, network routing, and graph generation, will be discussed. Finally, the advantages of the greedy algorithm and the limitation of the inability to obtain the global optimal solution will be evaluated, and the improvement direction combined with other algorithms will be proposed.
Keywords
Greedy Algorithm, Optimal Substructure, Backpack Problem, Minimum Spanning Tree, Advantages and Limitations
[1]. Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1(1), 269-271.
[2]. Wen, L., Wu, X., Zhao, P., Liao, X., & Xu, X. (2018). Understanding the Limitations of Greedy Algorithms: A Survey and Case Study. IEEE Access.
[3]. Bremert, G., & Kaplingat, M. (2016). Greedy Algorithms for the Knapsack Problem. International Journal of Advanced Computer Science and Applications, 7(4), 239-244.
[4]. Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to Algorithms.
[5]. Prim, R. C. (1957). A new algorithm for minimum spanning trees. Journal of the ACM (JACM).
[6]. Kruskal, J. (1956). On the Shortest Spanning Subtree of a Graph and the Traveling Salesman Problem. Proceedings of the American Mathematical Society.
[7]. Smith, J., Johnson, L., & Brown, E. (2018). A Study on the Kruskal Algorithm for Solving the Minimum Spanning Tree Problem. Journal of Computer Science, 42(3), 567-582.
[8]. M. R. Garey&D. S. Johnson(1976) A Greedy Algorithm for Job Shop Scheduling with Multiprocessor Machines. Information Processing Letters.
[9]. Kirkpatrick, S., Gelatt Jr, C. D., & Vecchi, M. P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680.
[10]. Hochbaum, D. S.& Levin, A(1997), A Fast and Space-Efficient Greedy Algorithm for Set Cover.
[11]. Zhao, Y., Zhang, Q., & Li, H. (2018). A Hybrid Approach Combining Greedy Algorithm and Divide-and-Conquer for Optimization Problems. Journal of Experimental & Theoretical Artificial Intelligence.
[12]. Sheng, Z., Cai, H., & Zhou, H. (2018). Combining Greedy and Dynamic Programming for Energy-Efficient Cooperative Sensing in Cognitive Radio Networks. IEEE Transactions on Cognitive Communications and Networking, 4(3), 580-590.
[13]. Smith, J. A. (2018). A Hybrid Algorithm Combining Greedy and Backtracking Strategies for Optimal Scheduling Problem. Journal of Optimization, 25(4), 789-801.
Cite this article
Wang,Y. (2023). Review on greedy algorithm. Theoretical and Natural Science,14,233-239.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).