DEV Community

Cover image for Greedy Algorithm With Examples
Shrijan ♥️
Shrijan ♥️

Posted on

Greedy Algorithm With Examples

A greedy algorithm is a problem-solving approach that makes a sequence of decisions, each of which is the best or most optimal choice at that moment (locally optimal), with the hope that this will lead to a globally optimal solution.

In essence, a greedy algorithm:

  1. Chooses the best option available at each step without considering the broader consequences.
  2. Does not revisit previous decisions or backtrack.
  3. Relies on a specific property, called the greedy choice property, which ensures that local optimization leads to global optimization.
  4. Assumes the problem has an optimal substructure, meaning the optimal solution can be constructed from the optimal solutions of its subproblems.

Key Characteristics of Greedy Algorithms:

  1. They are generally more efficient in terms of time complexity compared to exhaustive search methods.
  2. They may not always produce the globally optimal solution unless the problem guarantees correctness (e.g., greedy choice property and optimal substructure hold).

Common Examples of Greedy Algorithms:

  1. Activity Selection Problem - Selecting the maximum number of activities that don't overlap.
  2. Huffman Coding - Building optimal prefix codes for data compression.
  3. Kruskal's Algorithm - Finding the minimum spanning tree in a graph.
  4. Prim's Algorithm - Another approach to finding the minimum spanning tree.
  5. Fractional Knapsack Problem - Maximizing the total value by selecting fractions of items based on value-to-weight ratio.

Greedy algorithms are typically easier to implement but require thorough validation to ensure they are appropriate for the problem at hand.

Image of Datadog

The Future of AI, LLMs, and Observability on Google Cloud

Datadog sat down with Google’s Director of AI to discuss the current and future states of AI, ML, and LLMs on Google Cloud. Discover 7 key insights for technical leaders, covering everything from upskilling teams to observability best practices

Learn More

Top comments (0)

Image of Datadog

Create and maintain end-to-end frontend tests

Learn best practices on creating frontend tests, testing on-premise apps, integrating tests into your CI/CD pipeline, and using Datadog’s testing tunnel.

Download The Guide

👋 Kindness is contagious

Immerse yourself in a wealth of knowledge with this piece, supported by the inclusive DEV Community—every developer, no matter where they are in their journey, is invited to contribute to our collective wisdom.

A simple “thank you” goes a long way—express your gratitude below in the comments!

Gathering insights enriches our journey on DEV and fortifies our community ties. Did you find this article valuable? Taking a moment to thank the author can have a significant impact.

Okay