They are computationally challenged, meaning, at the time they were invented, they were perfectly good sorting algorithms, there are better ones, we're going to see a much better one next time around, but this is a good way to just start thinking about how to do the algorithm, or how to do the sort.

他们是相当棒的排序算法，是有更好的算法，我们下一次,就会看一个更好的，但是开始想想,如何完成算法，或者说是如何排序,是一个好的学习方法,恩，再试试吧,如何来排序呢？

麻省理工公开课 - 计算机科学及编程导论课程节选

So to just preface what we're going to do next time, what would happen if I wanted to do sort, and rather than in sorting the entire list at once, I broke it into pieces, and sorted the pieces, and then just figured out a very efficient way to bring those two pieces and merge them back together again?

所以为了引导下一次,我们要讲的内容,如果我想做排序,而且不是一次吧整个列表排完,会发生什么，我把它拆成小的列表,然后把各个小列表排序,接着用高效的方法再把小的列表？

麻省理工公开课 - 计算机科学及编程导论课程节选

So supposed that I give you 16 elements to sort, well, following the logic before, the running time involved in sorting 16 elements is gonna be twice the running time 16 of sorting 8 elements, left half and right half plus 16 - and again, a little sanity check, 16 means-- just the merge steps, right?

现在要对16个元素进行排序，根据之前的逻辑，对16个元素排序,要花的时间是对8个元素排序所花时间的,2倍，分别用于左半部分和右半部分，再加上6,这里16是-,做合并的步数，对吗？

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