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This lesson covers Big O notation — the standard way to express the efficiency of algorithms in terms of time and space. Understanding algorithmic complexity is essential for the OCR A-Level Computer Science (H446) specification.
Two algorithms can solve the same problem but differ dramatically in how long they take or how much memory they use. Measuring efficiency allows us to:
Big O notation describes the worst-case (or upper bound) growth rate of an algorithm's resource usage (time or space) as the input size n grows towards infinity.
It focuses on the dominant term and ignores:
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