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OK. And then the exponentials, as you saw is when typically I reduce the problem of one size into two or more sub-problems of a smaller size.
好，然后说到指数级，正如你所见，典型的例子是,我讲一个问题分解成为,两个更小规模的子问题。
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We have seen log, linear, quadratic, and exponential.
平方级的和指数级复杂度的方法，再说一遍，可能会有些常量。
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Change happens exponentially.
改变以指数级发生。
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And you really, wherever possible, want to avoid that exponential algorithm, because that's really deadly. Yes.
比你的电脑的性能增长的快多了，并且大家无论何时，都要避免去用指数级的函数。
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All right. The question is, is there a point where it'll quit.
因为指数级函数真的很致命，好，问题是。
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We've seen log, we've seen linear, we've seen quadratic, we've seen exponential.
我们看过了对数级的，线性的，二次平方的，指数级的算法。
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I could get a really big upper bound, this thing grows exponentially.
那么我可以得出一个相当大的上界，我们可以给一个指数级增长的上限。
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And if I'm running an exponential algorithm, any guesses?
杂度是指数级的呢？,有人猜猜么？
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Now, it's also the case that this is fundamentally what class this algorithm falls into, it is going to take exponential amount of time.
哪个种类的一个实例,这个问题的时间复杂度是指数级的，也就是当n上升的时候。
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If you substitute it all in, you get basically order 2 to the n.
得到的是2的n次方个基本问题，指数级的，这是个问题，好。
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