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<Plain English Explanation of Big O Notation ⁄ >




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Big O notation seeks to describe the relative complexity of an algorithm by reducing the growth rate to a single factor when the key factor tends towards infinity. In doing so, all other factors are ignored. It is a relative representation of complexity.
What Isn't Big O?

Big O isn't a performance test of an algorithm. It is also notional or abstract in that it tends to ignore other factors. Sorting algorithm complexity is typically reduced to the number of elements being sorted as being the key factor. This is fine but it doesn't take into account issues such as:

* Memory Usage: one algorithm might use much more memory than another. Depending on the situation this could be anything from completely irrelevant to critical;
* Cost of Comparison: It may be that comparing elements is really expensive, which will potentially change any real-world comparison between algorithms;
* Cost of Moving Elements: copying elements is typically cheap but this isn't necessarily the case;



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