Visualizers
Big-O Visualizer
Interactive growth-curve chart for common complexity classes.
Big-O Complexity Chart & Growth Curve Visualizer
Big-O notation describes the limiting behavior of a function when the argument tends towards a particular value or infinity — essential for analyzing algorithmic time and space complexity. This Big-O Visualizer charts common complexity classes: O(1) Constant, O(log n) Logarithmic, O(n) Linear, O(n log n) Linearithmic, O(n²) Quadratic, and O(2^n) Exponential. Adjust the input slider (n) to inspect step operation counts visually.
Frequently Asked Questions
What is Big-O Notation?
Big-O notation is a mathematical notation used in computer science to classify algorithms according to how their run time or space requirements grow as the input size (n) grows.
Which Big-O complexity class is fastest?
O(1) is the fastest (constant time, regardless of dataset size), followed by O(log n) (binary search), O(n) (linear scan), O(n log n) (efficient sorting like Merge Sort), O(n²) (nested loops), and O(2^n) (recursive combinations).