Greedy Algorithm

A greedy algorithm is a simple, intuitive algorithmic paradigm used for optimization problems, where the goal is to find the most optimal solution (maximum or minimum) for a given problem. The core principle that defines a greedy algorithm is its myopic decision-making process: at each step, the algorithm picks the most beneficial immediate choice, without considering the broader implications of that choice for future decisions. This "greediness" is where the algorithm gets its name.

Fundamental Truths of Greedy Algorithms:

  1. Local Optima: At each step, the algorithm makes a choice that looks best at the moment, aiming for local optima.
  2. No Backtracking: Once a choice is made, the algorithm never revisits or reverses this choice. There's no backtracking.
  3. Optimization Problem: Greedy algorithms are used for optimization (finding the maximum or minimum solution) problems.
  4. Efficiency: They often provide a quick, efficient, albeit not always optimal, solution.
  5. Simplicity: Greedy algorithms are generally easier to code and understand than other complex algorithms like dynamic programming or backtracking.

Characteristics and Limitations:

  • Not Always Optimal: Greedy algorithms don't always yield the globally optimal solution. They are optimal for some problems but not for others.
  • Problem-Specific: Whether a greedy algorithm works for a particular problem depends heavily on the problem's structure.
  • Choice of Selection Strategy: The way you choose the "best" option at each step (the greedy choice property) is crucial and problem-specific.
  • Irrevocability: Once a choice is made, it's final. The algorithm doesn't reconsider this decision.

Examples of Greedy Algorithms:

  1. Dijkstra's Algorithm: Finds the shortest path in a graph.
  2. Kruskal's and Prim's Algorithms: Find the minimum spanning tree in a graph.
  3. Huffman Coding: Used for data compression.

Why Use Greedy Algorithms?

  • Simplicity and Speed: They are straightforward to understand and can be faster than other complex algorithms.
  • Good Enough Solutions: For some problems, especially where perfect optimality isn't required, they provide sufficiently good solutions quickly.
  • Starting Point: They can serve as a baseline or starting point for more complex algorithms.

How to Approach a Problem with a Greedy Algorithm:

  1. Understand the Problem: Know what you're trying to optimize and what the constraints are.
  2. Identify the Greedy Choice: Determine what your local optimal solution is at each step.
  3. Prove Greediness Works: Sometimes, you need to mathematically prove that making the local best choice will lead to a global optimum (this is often the trickiest part).
  4. Implement and Test: Code the algorithm and test it on various cases to ensure it works as expected.


Greedy algorithms are a foundational concept in computer science and optimization. They provide a way to approach problems where finding an immediate local optimum at each stage leads to a global optimum. However, their effectiveness is heavily reliant on the problem's structure and the strategy for making greedy choices. Understanding when and how to apply them is key to leveraging their power effectively.