And so the fact that in this whole slide here, this algorithm for sorting, I'm using the verb sort.

在这个排序算法中,我用到了一个动词排序。

哈佛公开课 - 计算机科学课程节选

All right? I don't care what's in there, but just assume that's my list. And just to remind you, on your handout, and there it is on the screen, I'm going to bring it back up, there's the little binary search algorithm.

好，我不管列表里是什么，就假定那是一个列表,要提醒你们的是,在你们的讲义上和屏幕上，有一个简单的二分查找算法。

麻省理工公开课 - 计算机科学及编程导论课程节选

Alright, I have the problem so clearly this algorithm whatever it's gonna be is gonna be at least twice as fast because I'm doing half as much work.

现在，这个问题已变得清晰,不管怎么样，排序算法的效率,至少是之前的两倍,因为我只需要做一半的工作。

哈佛公开课 - 计算机科学课程节选

It will probably quit at some point because of space issues, unless I'm writing an algorithm that is using no additional space.

其中一个的时候，它会一直运行下去,它可能在某一处因为空间问题退出,除非我写了一个。

麻省理工公开课 - 计算机科学及编程导论课程节选

If I'm running a quadratic algorithm, it'll take one millisecond to complete.

算法会在1毫秒内完成，如果问题的复。

麻省理工公开课 - 计算机科学及编程导论课程节选

Let's pull together what this algorithm actually does. If I generalize binary search, here's what I'm going to stake that this thing does.

总结下二分查找法,下面列举几点它的操作，首先，找中点。

麻省理工公开课 - 计算机科学及编程导论课程节选

If I'm running a linear algorithm, it'll take one microsecond to complete.

算法会在1微秒内完成，如果是一个平方级的方法。

麻省理工公开课 - 计算机科学及编程导论课程节选

And if I'm running an exponential algorithm, any guesses?

杂度是指数级的呢？,有人猜猜么？

麻省理工公开课 - 计算机科学及编程导论课程节选

And in this case, we go from 8 to 4 to 2 to 1 three times and then on each iteration of this algorithm, each pass across the board I'm touching N numbers, so that means I'm doing N things, log N times.

在这个例子中，我们从8得到4，到2，再到1，是3次,在这个算法的每次迭代中，每一趟我都会操作N个数，也就是所我每次要做N步操作，一共要做，log，N，次。

哈佛公开课 - 计算机科学课程节选

If I'm using algorithm that I'm now calling merge sort, T the running time involved in sorting N elements, T of N, you know, is just the same as running the algorithm for the right half, plus what's this plus N come from?

如果我用归并排序算法，对N个元素其运行时间，就等于此算法一半元素的运行时间,另一半的运行时间，再加上N，这个N是什么呢？

哈佛公开课 - 计算机科学课程节选

And that's just a way of reminding you that we want to think carefully, but what are the things we're trying to measure when we talk about complexity here? It's both the size of the thing and how often are we going to use it? And there are some trade offs, but I still haven't said how I'm going to get an n log n sorting algorithm, and that's what I want to do today.

这只是在提醒你们我们要仔细的思考问题,但是当我们在讨论复杂性的时候,我们到底要衡量哪些东西？,是列表的大小和对其进行查找的频率吗？,这里面临一些取舍,但是我还没有说明,怎样得到一个n，log，n复杂度的排序算法,并且这是我今天想要讲的内容。

麻省理工公开课 - 计算机科学及编程导论课程节选