Submitted by Robert MacLean

The series post, which contains more stuff formally trained programmers know, can be found here.

This one has had me always confused and always seemed to be something out of my reach. It really is simple once I actually sat down and worked through it. Lets start with the syntax:

O(n)

The "O" just is a indicator that we using big O notation and the n is the cost. Cost could mean a variety of things, memory, cpu cycles but mostly people think of it as the number of times the code will execute. The best cost would be code that never runs (i.e. `O(0)`) but that likely has no value.

To help explain it, let's look at a simple example:

Console.WriteLine("Hello 1");

The cost for that is 1, so we could write `O(1)`. If we put that in a for loop like this:

for (var counter = 0; counter < 10; counter++) { Console.WriteLine("Hello "+counter); }

The cost would be 10, so we could write `O(10)`.

Rather than having to be explicit with number (like 10 above) we can use a short hand notation. The common one is ```n``` which means it will run once per item. For our for loop example about that means it could be written as `O(n)` so that regardless if we looping 10 times or a 100 times the relative cost is the same and can be referenced the same. From this point on it really is just about adding math to it.

If we were to have a loop inside a loop as follows, which will run 100 times (10 X 10) we could write this as `O(n<sup>2</sup>)`.

var n = 10; for (var outerCounter = 0; outerCounter<n; outerCounter++) { for (var counter = 0; counter < n; counter++) { Console.WriteLine("Hello "+counter); } }

The other common one used with Big O Notation is `log`, i.e. Logarithm, which could be written like this: O(log n). In this case the cost per item gets less (relative to earlier items) as we add more items.

The best guide I found was from Rob Bell.

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## More on Big-O notation

More on Big-O notation

Big-O notation is one of four notations for complexity (big-O, little-O, Omega, and Theta). Big-O is the most important one most of the time, because it gives the worst-case performance for an algorithm.

When dealing with big-O notation, any constants are ignored - it's only the shape of the growth that matters. So O(10) is the same as (and notated as) O(1). And if you do an O(n) operation followed by another O(n) operation, the total would be O(2n) but is notated as O(n).

Also, only the most significant term is included - again, higher terms determine the shape, so the lower terms are just dropped. So O(n^2 + n) is the same as (and notated as) O(n^2).

And then there's the whole issue of "amortized", which takes the edge off, so to speak. For example, hash sets usually hash into buckets, such as linked lists. So, the actual big-O notation for inserting into a hash set is O(n), but when amortized, it's only O(1).

O(n) notation is one of those topics that it's helpful to get a book on if you really want to learn it in depth (do mathematical analysis instead of inspection for complex algorithms, how amortization works, etc).

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