THINK++ — Engineering Visualized

📖 LESSON

Time Complexity Basics

Time complexity measures how the runtime of an algorithm grows as the input size increases. It's represented using Big O notation.

O(1)

Constant

O(log N)

Logarithmic

O(N)

Linear

O(N log N)

Linearithmic

O(N²)

Quadratic

O(2ᴺ)

Exponential

Growth Rate Comparison

Input N: 100

Toggles

Scale

Observe how $O(N^2)$ dominates other functions as $N$ increases.

💡 Big O Rules

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