Linear Space: O(n)

Grokking Algorithm Complexity and Big-O

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Linear Space: O(n)

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Linear Space Complexity $O(n)$ refers to algorithms where the memory usage grows linearly with the input size. This occurs when an algorithm needs to store a new copy of the input or requires auxiliary data structures proportional to the input size.

Key Characteristics

In an algorithm with $O(n)$ space complexity:

The memory usage increases linearly with the size of the input.
Common in tasks that duplicate the input data or process each element with additional storage.

Code Example 1: Copying a String

Let’s look at an example where we create a copy of a string

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