THINK++ — Engineering Visualized

📖 LESSON

Space Complexity Basics

Space complexity measures the total amount of memory an algorithm needs relative to the input size.

Auxiliary Space

Extra space used by the algorithm (excluding input).

Input Space

Space required to store the input data.

Stack Space

Space used by recursive calls on the call stack.

Growth Rate Comparison

Input N: 100

Toggles

Scale

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

💡 Space Saving Tip

Always consider if you can solve the problem "in-place" to achieve O(1) auxiliary space. This is often a key optimization requirement in interviews.

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