Greedy Algorithm


A Greedy Algorithm is a problem-solving approach in computer science and optimization, where the best possible solution at a particular step is chosen to achieve global optimality. It functions by making a sequence of choices, each yielding the most immediate benefit or profit, without regard for future consequences. Although greedy algorithms do not always provide the optimal solution, they can be more efficient and easier to implement compared to other methods, especially for simpler problems.


The phonetics of the keyword “Greedy Algorithm” are: /ˈɡriːdi ˈælɡəˌrɪðəm/

Key Takeaways

  1. Greedy algorithms make the best/optimal choice at each step, aiming for a globally optimal solution.
  2. Greedy algorithms are often simple, easy to implement, and efficient, but may not always yield the most optimal solution.
  3. Greedy algorithms work best on problems with the “Greedy-choice property” and “Optimal substructure”, ensuring the locally optimal choices lead to a globally optimal solution.


The term “Greedy Algorithm” holds significant importance in the field of computer science and technology because it offers a simple, intuitive, yet powerful technique for solving various optimization problems.

These algorithms make local optimal choices at each step, prioritizing immediate rewards and assuming that such choices will lead to a globally optimal solution.

While greedy algorithms may not always produce the most efficient output, they tend to deliver good approximations to the optimal solution in a relatively short amount of time, especially for problems with large datasets.

Consequently, their ease of implementation and computational efficiency make them a valuable tool for developers, engineers, and researchers working on diverse problems ranging from networking to artificial intelligence, even when an ideal solution might not be achievable.


A greedy algorithm is a powerful problem-solving approach that is employed in various fields such as optimization, computer science, and artificial intelligence to achieve viable solutions to complex problems. The algorithm’s purpose is to make use of short-term gains to efficiently obtain near-optimal solutions to problems while avoiding unnecessary complexity and calculations. It simplifies the decision-making process by employing a strategy in which, at every iteration, the best possible choice that appears imminent is used to advance closer to the overall objective.

Greedy algorithms are most effective when applied to problems where selecting the ideal local solution leads to an optimal global solution. In many cases, greedy algorithms are applied to problems in network routing, graph theory, and optimization. They are utilized to improve computational efficiency and reduce processing time when tackling sizeable problems.

Examples of their applications include the Kruskal’s algorithm for finding a minimum spanning tree in a weighted graph or the Dijkstra’s algorithm to determine the shortest path from a starting point to other nodes in a graph. However, it is essential to note that greedy algorithms may not always produce optimal solutions, as they prioritize immediate gains over long-term consequences. Nevertheless, it remains a highly valuable technique for solving problems where obtaining a near-optimal solution is sufficient, and the trade-off in computation time is valuable.

Examples of Greedy Algorithm

Huffman Coding: Huffman coding is a widely-used lossless data compression technique in the field of data communications and computer science. The greedy algorithm is used in creating a binary tree where the most frequently occurring characters (or data) are assigned shorter binary codes, while less frequent characters receive longer binary codes. This can significantly reduce the size of transmitted data without any loss of information.

Kruskal’s Minimum Spanning Tree (MST) Algorithm: Kruskal’s Algorithm is a famous and efficient algorithm used in network design and graph theory to find the minimum spanning tree of a connected, undirected graph. The algorithm follows a greedy strategy by sorting the edges in non-decreasing order of their weights and iteratively adding the next lowest-cost edge that does not form a cycle. This allows for efficient planning of wiring or pipelines over large areas, minimizing the total cost of the network while still maintaining connectivity.

Dijkstra’s Shortest Path Algorithm: Dijkstra’s Algorithm is an efficient greedy algorithm used in various applications such as network routing, finding the shortest path in maps, and transportation logistics. The algorithm works iteratively by maintaining a set of unvisited nodes, selecting the node with the shortest known distance from the starting node, and updating the neighboring unvisited nodes with the new shortest distances, until the destination node is reached or all possible paths have been evaluated. This algorithm is the foundation for many real-time routing and navigation applications, such as GPS technology and Google Maps.

FAQ: Greedy Algorithm

What is a Greedy Algorithm?

A Greedy Algorithm is a problem-solving approach that makes the best possible decision at each step, based on the current problem state. It follows the principle of choosing the most optimal solution at the moment, without considering the global optimal solution, hoping that it will lead to an overall optimum result.

When should I use a Greedy Algorithm?

Greedy Algorithm should be used when a problem exhibits the properties of greedy choice and optimal substructure. In other words, using a locally optimal choice at each step should lead to a globally optimal solution. If the problem does not have these properties, a greedy algorithm may not provide the best solution.

What are some examples of Greedy Algorithms?

There are several well-known algorithms that use the greedy approach, including Dijkstra’s shortest path algorithm, Kruskal’s and Prim’s minimum spanning tree algorithms, and Huffman coding algorithm. These algorithms use the principle of making the most optimal choice at each step to solve specific problems efficiently.

What are the advantages of Greedy Algorithms?

Greedy Algorithms are typically easier to implement and have a faster runtime compared to other algorithms. They usually provide an efficient solution for problems that can be solved with the greedy approach. Additionally, the concept of making optimal choices at each step can be intuitively understood, making the algorithm more straightforward to comprehend.

What are the limitations of Greedy Algorithms?

Greedy Algorithms have certain limitations. The primary limitation is that a greedy approach will not always provide the globally optimal solution, especially if the problem does not exhibit the properties of greedy choice and optimal substructure. Sometimes, making locally optimal choices can lead to suboptimal solutions for the entire problem. Therefore, it’s essential to verify if a greedy algorithm is suitable for the problem at hand before implementing it.

Related Technology Terms

  • Optimization Problem
  • Local Optimum
  • Choice Criterion
  • Dynamic Programming
  • Backtracking

Sources for More Information


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