THE CONCEPT, PRINCIPLES, AND PRACTICAL APPLICATIONS OF GREEDY ALGORITHMS IN COMPUTER SCIENCE
Keywords:
Greedy algorithms, optimization problems, local optimum, global optimum, greedy-choice property, optimal substructure, algorithm design, graph algorithms, Dijkstra’s algorithm, Kruskal’s algorithm, Prim’s algorithm, Huffman coding, scheduling, data compression, shortest path, minimum spanning tree, resource allocation, computational efficiency, time complexity, approximation algorithms.Abstract
Greedy algorithms are a significant topic in computer science because they provide a simple and efficient way to solve many optimization problems. An optimization problem is a problem where the aim is to find the best possible solution among several available choices. The main idea of a greedy algorithm is to make the best choice at the current step and continue this process until the final solution is obtained. This method is called “greedy” because the algorithm always chooses the option that seems most beneficial immediately, without looking deeply into all future possibilities. Greedy algorithms are widely used in graph theory, scheduling, data compression, network routing, and resource management. Their main advantages are simplicity, speed, and low memory usage. However, greedy algorithms do not always guarantee the optimal solution. They work correctly only for problems that have special mathematical properties, such as the greedy-choice property and optimal substructure. This paper discusses the definition, working principles, applications, advantages, limitations, and scientific importance of greedy algorithms in computer science.
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