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I am learning about Big O Notation running times and amortized times. I understand the notion of O(n) linear time, meaning that the size of the input affects the growth of the algorithm proportionally...and the same goes for, for example, quadratic time O(n2) etc..even algorithms, such as permutation generators, with O(n!) times, that grow by factorials.

For example, the following function is O(n) because the algorithm grows in proportion to its input n:

Similarly, if there was a nested loop, the time would be O(n2).

But what exactly is O(log n)? For example, what does it mean to say that the height of a complete binary tree is O(log n)?

For example, the following function is O(n) because the algorithm grows in proportion to its input n:

f(int n) {

int i;

for (i = 0; i < n; ++i)

printf("%d", i);

}

Similarly, if there was a nested loop, the time would be O(n2).

But what exactly is O(log n)? For example, what does it mean to say that the height of a complete binary tree is O(log n)?

O(log N) basically means time goes up linearly while the n goes up exponentially. So if it takes 1 second to compute 10 elements, it will take 2 seconds to compute 100 elements, 3 seconds to compute 1000 elements, and so on.

?It is O(log n) when we do divide and conquer type of algorithms e.g binary search. Another example is quick sort where each time we divide the array into two parts and each time it takes O(N) time to find a pivot element. Hence it N O(log N)

?It is O(log n) when we do divide and conquer type of algorithms e.g binary search. Another example is quick sort where each time we divide the array into two parts and each time it takes O(N) time to find a pivot element. Hence it N O(log N)

If you had a function that takes:

Then it takes log2(n) time. The Big O notation, loosely speaking, means that the relationship only needs to be true for large n, and that constant factors and smaller terms can be ignored.

1 millisecond to complete if you have 2 elements.

2 milliseconds to complete if you have 4 elements.

3 milliseconds to complete if you have 8 elements.

4 milliseconds to complete if you have 16 elements.

...

n milliseconds to complete if you have 2^n elements.

Then it takes log2(n) time. The Big O notation, loosely speaking, means that the relationship only needs to be true for large n, and that constant factors and smaller terms can be ignored.

Logarithmic running time (O(log n)) essentially means that the running time grows in proportion to the logarithm of the input size - as an example, if 10 items takes at most some amount of time x, and 100 items takes at most, say, 2x, and 10,000 items takes at most 4x, then it's looking like an O(log n) time complexity.

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