So I haven't done magic, I've given you a really fast way to solve a knapsack problem, but it's still exponential deep down in its heart, in something.

所以我并没有施魔法，我已经告诉了你,一种快速解决背包问题的方法了,但是某些方面它的核心仍然是指数增长的。

麻省理工公开课 - 计算机科学及编程导论课程节选

Let's now go back and instantiate these ideas for the knapsack problem we looked at last time In particular, for the 0-1 knapsack problem.

让我们回来用具体例子,来说明我们上次看过的背包问题,特别是对0-1背包问题来说。

麻省理工公开课 - 计算机科学及编程导论课程节选

But let's look for a slight variant of it, where greedy is not so good. And that's what's called the zero-one knapsack problem.

但是让我们找一找它的一些变种，在这些变种中贪婪算法用处不大,这些问题也就是0/1背包问题。

麻省理工公开课 - 计算机科学及编程导论课程节选

So we'll start looking in detail at one problem, and that's the knapsack problem. Let's see.

让我们开始仔细讲讲一个问题，那就是背包问题。

麻省理工公开课 - 计算机科学及编程导论课程节选

with the continuous knapsack problem as we've formulated it, greedy is good.

因为正如我们已经归越过的,对于一般连续性背包问题贪婪算法很实用。

麻省理工公开课 - 计算机科学及编程导论课程节选

So let's look at an example of a zero-one knapsack problem.

我们要像之前一样将其最优化,现在让我们来看一个0/1背包问题的例子。

麻省理工公开课 - 计算机科学及编程导论课程节选

Typically up till now, we've looked at things that can be done in sublinear time. Or, at worst, polynomial time. We'll now look at a problem that does not fall into that. And we'll start with what's called the continuous knapsack problem.

至今为止我们已经处理过,亚线性问题，最多也就是多项式问题,我们现在要看的问题则是不能用这些解决的,我们将要开始讲连续背包问题。

麻省理工公开课 - 计算机科学及编程导论课程节选