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Obviously the algorithm is likely to depend on the size of the input, so this is not a great idea.
很明显这个算法是与我的,输入的大小相关的，因此这不是个好方法。
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And the obvious question I could start with is, and suppose I ask you, how long does the algorithm implemented by this program take to run?
我开始这个话题的第一个问题是，假设我问你，由这个算法实现的程序大概,要运行多久才能给出答案？
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This is really the guts of the algorithm 'cause this line here, someone pointed it out before.
这才是算法的核心，因为在这儿，之前已经有人指出过。
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It couldn't have been a very complicated algorithm because it basically paired people up randomly on the campus.
这个算法不是很复杂,基本上是给校园里的人随机配对。
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Why don't I just keep track on each pass through the algorithm whether I have done any swaps?
我为什么不仅仅跟踪算法的,每一遍来看看我做了哪些交换呢？
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The algorithm doesn't know that it's going to take this long to compute, it's just busy crunching away, trying to see if it can make it happen.
这很可能会发生，但是你知道的,算法本身并不知道，计算这个问题需要多长的时间，它就一直忙碌的算啊算。
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N But it's definitely not one and in fact it wasn't N in the case of Selection Sort because remember the algorithm we implemented on stage last week had me going back and forth across the stage selecting on iteration, the smallest person I can find, the smallest number and then putting them into place.
但在选择排序中，肯定不会是1，也不是,注意，上周我们在这儿,实现的算法中，反复地,迭代进行选择，选出最小的数,然后将其放在合适的位置。
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Because the algorithm I proposed is going to leverage this idea of recursion which recall was just a piece of jargon we tossed out at the last-- at the end of last week's lecture, last time's lecture recursion really in this context refers to the act of a function calling it's self.
因为我提出的这种算法使用了,递归的思想，这是上周课程的最后,所提出的一个术语，上次的课程中，递归是指,函数的自我调用。
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So again, the algorithm looks like this.
算法是这样的。
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All right? Because if I don't do any swaps on a pass through the algorithm, then it says everything's in the right order.
好么？因为如果我,这一遍没有做任何交换，那么就意味着已经排好序了。
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I want you to recognize classes of algorithms and match what you see in the performance of the algorithm to the complexity of that algorithm. All right?
让我再试一次这么说,我期望大家能通过,算法的复杂度,来看到算法的性能？
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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？
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Again, I want you to start seeing how to map the characteristics of the code- the characteristics of the algorithm, let's not call it the code-- to the complexity.
是算法中的那些特性,我们还是别说代码了，转化为复杂度-,我一会儿还会再讲点儿这个的，但是我们再看一个例子。
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What's the next step in the algorithm?
那算法中的下一步是什么呢？
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All right. If you flip to the second side of your handout, you'll see that I have a piece of code there, that does this-- let me, ah, I didn't want to do that, let me back up slightly- this is the algorithm called search.
你可以看到实现这个功能的代码,让我，额，我不想这么做,让我们回来一点,这就是命名为search的算法-,我们来看看这个算法吧。
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Cut the problem in half. Cut the problem in half again. And that's a typical characterization of a log algorithm.
是每次除以特定的量,将问题减一半，再减一半,如此，这就是对数算法的典型特性。
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The algorithm on mine professed to be implementing now is this thing.
这个算法现在要,解决的就是这个问题。
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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。
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A log algorithm typically is one where you cut the size of the problem down by some multiplicative factor.
对数级复杂度的算法就是指,通过一系列常量级步数的操作,可以将问题的规模。
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Now, as obvious a statement as that is in this algorithm, it turns out that is the key to this whole problem being solved correctly without my algorithm looping infinitely.
这是此算法中很明显的一句话，也是正确解决,整个问题的关键,否则此算法就会陷入死循环。
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You might remember vaguely logarithms from high school math and such but what this suggests for us , the computer scientists, is that this is certainly a smarter, a faster algorithm.
你可能还会依稀记得,高中数学里的对数,这就给了我们这些计算机科学家们，一些启示,即，这种算法更智能，更迅速。
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It implies that this algorithm is calling itself again and again, and again, and on each time the size of the problem I'm trying to sort is being divided by what?
这就说明此算法会一次又一次地调用自己,每次我要排序的问题规模大小,会除以多少呢？
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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？
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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。
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It's an example of a very common tool that's going to be really useful to us, not just for doing search, but for doing a whole range of problems. That is, in essence, the template the describes a log style algorithm.
不仅仅是做搜索,还可以解决一整类问题,本质上，这个模板就描述了,对数形的算法,我们一会再回来。
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What is the class of algorithm that's- that probably applies to this, and how do I pull something out of that, if you like, a briefcase of possible algorithms to solve?
以及哪种算法-,可能可以应用到这类问题上,问题本身又能提取出什么东西来，以及可能解决问题的一系列方法的简介？
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and we like log algorithms, because they're really fast. A typical characteristic of a log algorithm is a pro-- or sorry, an algorithm where it reduces the size of the problem by a constant factor.
并且我们也很喜欢对数算法,因为它很快,对数算法的典型特性是高速，哦，抱歉，是他能以常数因子的速度,降低问题的大小，很明显。
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And so one of the things we'll look at today is how can we leverage an algorithm, how can we implement an algorithm that at least at first glance the second time we've now seen it feels so obviously better.
因此，今天我们要学习的一点就是,我们怎样才能影响一个算法，怎样实现一个算法,使你在第二次看它时会觉得,至少比你第一次看到它时更好。
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