Menu
I often here people talk about Big O which measures algorithms against each other
Does this measure clock cycles or space requirements.
If people want to contrast algorithms based on memory usage what measure would they use
782
Big O Notation is a way to measure an algorithm's efficiency. It measures the time it takes to run your function as the input grows. Or in other words, how well does the function scale. There are two parts to measuring efficiency — time complexity and space complexity.
We may ignore any powers of n inside of the logarithms. The set O(log n) is exactly the same as O(log(nc)). The logarithms differ only by a constant factor (since log(nc) = c log n) and thus the big O notation ignores that. Similarly, logs with different constant bases are equivalent.
Worst case — represented as Big O Notation or O(n)Big-O, commonly written as O, is an Asymptotic Notation for the worst case, or ceiling of growth for a given function. It provides us with an asymptotic upper bound for the growth rate of the runtime of an algorithm.
If someone says "This algorithm runs in O(n) time", he's talking about speed. If someone says "This algorithm runs in O(n) space", he's talking about memory.
If he just says "This algorithm is O(n)", he's usually talking about speed (though if he says it during a discussion about memory, he's probably talking about memory).
If you're not sure which one someone's talking about, ask him.
132
Short answer : you have 'Big O in space" and "Big O in time".
Long answer: Big O is just a notation, you can use it in whatever context you want.
34
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
