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\nI just can't quite understand what an algorithm is \"big O\" of... I can take this example:\nTime Complexity: O(n)\nSpace Complexity: O(1)\nIs this considered big O?\nIn this particular example you can eliminate a loop since it is constant. So in this case I think it is big O of O(1) to O(1)
\nA:
\nBig-O is a measure of worst-case performance.\nThis would be the time complexity of the algorithm if you had a huge list of strings (with each string of length n) to sort: (O(n^2))\nString.length = n
\nThis is big O(1) (if n is large) but not big O(n).\nSince big O doesn't take into account what is happening in a \"typical\" case, so for example, a sort that takes 10% of the time (O(0.1)) would be considered big O(1) (since we are discussing time complexity).\nIn your example, since you are considering the time complexity, it is not big O(1).
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