Time Complexity & Big O Notation

Big O notation is used to describe the performance or complexity of an algorithm. It describes the worst-case scenario, indicating the maximum time an algorithm will take to complete as the input size N grows.

• O(1) Constant: Time does not depend on input size. (e.g., Array index lookup)
• O(N) Linear: Time grows proportionally with input size. (e.g., Linear Search)
• O(N log N) Linearithmic: Highly efficient sorting algorithms. (e.g., Merge Sort, Quick Sort)
• O(N²) Quadratic: Inefficient nested loops. (e.g., Bubble Sort, Selection Sort)

How the Analyzer Works

When you click analyze for JavaScript, your code is benchmarked locally. Random arrays of varying sizes are passed to your function and execution time is plotted on the graph.

For Python, Java, and C++, your code is sent to Gemini AI for static analysis. The AI calculates the asymptotic complexity and returns a detailed explanation of why it performs as it does.