Chapter 03 — O Yes! Big O Notation
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Last updated 3:02 PM on 8/28/26
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13 Terms
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Big O notation
A way to describe how algorithm steps grow as input size grows
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N
The input size or number of data elements
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O(1)
Constant time; steps do not grow with N
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O(N)
Linear time; steps grow directly with N
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O(log N)
Logarithmic time; the problem is repeatedly cut in half
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O(N²)
Quadratic time; often caused by nested loops
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Worst case
The maximum number of steps an algorithm may take
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Best case
The fewest number of steps an algorithm may take
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Linear search best case
O(1), if the target is first
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Linear search worst case
O(N), if the target is last or missing
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Binary search Big O
O(log N), because each step removes half the data
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Big O focus
Growth rate, not exact seconds
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OA speed order
O(1) is faster than O(log N), which is faster than O(N), which is faster than O(N²