Boy, there's a dumb question, because I've been telling you n log n for the last two lectures the complexity is n log n, but let's see if it really is.

孩子们，这是一个愚蠢的问题，因为前两节课的时候我就已经告诉你们了,复杂度是,但是让我们来看一下是不是真的是这样。

麻省理工公开课 - 计算机科学及编程导论课程节选

N log N is not nearly as good as log N. As a sanity check, what algorithm have we seen that runs in log N time?

而N，log，N和log，N并不一样，我们之前探讨过的哪个算法其时间复杂度是log，N呢？

哈佛公开课 - 计算机科学课程节选

So I have n operations log n times, n log n there we go, n log n. Took us a long time to get there, but it's a nice algorithm to have.

所以我log，n遍的n次操作，就得到了,虽然花了不少时间得到了这个结论。

麻省理工公开课 - 计算机科学及编程导论课程节选

If we can sort things, you know, we get this n log n behavior, and we got a n log n behavior overall. But can we actually do better in terms of searching.

如果我们可以排序，如你所知，我们有n，log，n级别的算法，并且我有一个整体的n，log，n级别的算法,但是我们在搜索方面可以做的更好吗？

麻省理工公开课 - 计算机科学及编程导论课程节选

But that merging process only takes N steps, N*log N so that's N times log of N. Now, it's a little tricky to reason through this perhaps the first time, let's just take a very simple example and see if we can do a little sanity check here.

但这个合并过程只需要N步，所以时间复杂度是,第一次对此进行推论可能会有点儿棘手，我们举一个简单的例子,看看我们能否做一些完整性的检查。

哈佛公开课 - 计算机科学课程节选

And if you ask the TAs in recitation tomorrow, they'll tell you that you see a lot of n log n algorithms in computer science.

如果你明天在复习课上问助教的话，他们会告诉你在计算机科学中,存在着非常多的n，log，n规模的算法。

麻省理工公开课 - 计算机科学及编程导论课程节选

And what does N log N feel like vis-a-vis N squared?

同N平方相比，N*log，N又是怎样的呢？

哈佛公开课 - 计算机科学课程节选

Perhaps more importantly, how to recognize a kind of algorithm based on its properties and know what class it belongs to. This is a hint. If you like, leaning towards the next quiz, that you oughta be able to say that looks like a logarithmic algorithm because it's got a particular property. That looks like an n log n algorithm because it has a particular property.

也许更重要的是,如何根据一个算法的特点将其辨别出来,并且知道它属于哪一类算法,这是一个提示，就对于接下来的测验来说,如果你喜欢你可以说它看起来像一个对数算法,因为它有一个特定的性质,那个看起来像一个n，log，n的算法,因为它有一个特定的性质。

麻省理工公开课 - 计算机科学及编程导论课程节选