1
00:00:00,390 --> 00:00:03,630
‫So our next big go is of one.

2
00:00:04,020 --> 00:00:08,970
‫And to explain this, I'm going to build out a function that we're going to call add items.

3
00:00:09,480 --> 00:00:18,870
‫And all this is going to do is return in plus MN and this is our first big O where the number of operations

4
00:00:18,870 --> 00:00:23,250
‫does not change as in becomes larger.

5
00:00:23,610 --> 00:00:29,910
‫So in this situation, if N was one, we would have one operation, which is the addition.

6
00:00:30,240 --> 00:00:37,980
‫But if N went all the way to a million, we would still have one operation, which is the addition,

7
00:00:38,520 --> 00:00:41,400
‫and that is o of one.

8
00:00:42,040 --> 00:00:49,240
‫But what if instead of having n+ n we had in plus n less rn.

9
00:00:49,660 --> 00:00:54,160
‫Well, now this would be two operations and you could call that o of two.

10
00:00:54,640 --> 00:01:02,200
‫But as you may have guessed, we're going to simplify this and we're still going to call this o of one.

11
00:01:02,740 --> 00:01:05,260
‫So now let's take a look at O of one.

12
00:01:05,980 --> 00:01:07,180
‫On the graph.

13
00:01:07,450 --> 00:01:11,980
‫So OV one is just that purple line across the bottom.

14
00:01:12,250 --> 00:01:20,470
‫It is the most efficient big o because as in becomes larger, the number of operations does not become

15
00:01:20,470 --> 00:01:21,190
‫larger.

16
00:01:21,550 --> 00:01:29,230
‫So any time that you can do something with o of one time complexity is going to be very efficient.

17
00:01:29,740 --> 00:01:33,910
‫And that is our overview of of one.