Quadratic Algorithms

Quadratic algorithms are computational algorithms with a time complexity of O(n²), meaning their execution time grows proportionally to the square of the input size. They are characterized by nested loops that iterate over the input data, such as in bubble sort or naive matrix multiplication. These algorithms are fundamental in computer science for understanding algorithmic efficiency and performance trade-offs.

Also known as: O(n^2) algorithms, n-squared algorithms, quadratic time complexity, O(n²), n^2 algorithms

Why learn Quadratic Algorithms?

Developers should learn quadratic algorithms to grasp basic algorithmic analysis and recognize inefficient patterns in code, such as when optimizing performance-critical applications. They are essential for foundational computer science education and are often used in simple sorting or searching tasks where input sizes are small, but should be avoided for large datasets in favor of more efficient algorithms like O(n log n) or O(n).

Learning Resources

• tutorial: Big-O Notation Explained
• tutorial: Time Complexity and Quadratic Algorithms
• book: Grokking Algorithms Book
• video: Quadratic Time Complexity Explained