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The idea behind all these algorithms is that what's ultimately important is how many comparisons you ultimately need to make.
这些算法的思想是,你最终需要做多少次的比较,这才是真正重要的。
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So now that we begin to have this ability to express strings in memory, to understand what they are, we can start to apply actually real-world algorithms to them.
现在我们开始用这个技能来在内存中表达字符串,来理解它们是什么，我们可以开始,我们可以对其使用实际的，现实生活中的算法。
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So in fact, over the next thirty or forty minutes we're going to show you a set of examples of sort of canonical algorithms, and the different classes of complexity.
在接下来的三四十分钟里面,我们将要讲一系列的,权威算法，以及不同种类的复杂度问题。
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Interpret those neural signals with mathematic algorithms as one on custom-designed chips those of you who are very familiar with this technology, that fuels and all other things running here today.
通过运算法则去解释这些神经信号,就像大家都很熟悉的,定制的芯片上那样，燃料等现在大家用到的东西。
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And I'll remind you, we saw a set of algorithms and part of my goal was to get you to begin to recognize characteristics of algorithms that map into a particular class.
我要提醒你们的是，我们看了很多算法,我的目标之一是让你们开始,意识到算法们的特性,并且能够一一映射到特定的分类中。
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Linear algorithms tend to be things where, at one pass-through, you reduce the problem by a constant amount by one. If you reduce it by two, 1 it's going to be the same thing.
有问题么？,线性复杂度的算法，当进行了一个,常量级步数的操作的时候，将问题的规模缩小了一个。
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One of the things we want to help you do is identify the different classes of algorithms, what their costs are, and how you can map problems into the most efficient class to do the computation.
我们想要教会你们的事情之一就是,学会辨别不同种类的算法，它们的代价是怎么样的，以及怎么样把问题映射到,最有效的算法种类中去做计算。
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And the reason for the scale is -- that as we saw last week the idea of these algorithms-- I broke the scale already.
之所以要用到它,是因为上周我们讨论的这些算法思想-,我把天平弄坏了。
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Alright, so we can implement any number of algorithms using this thing because the basic mechanism I have here is a comparator.
好了，利用它我们可以实现,很多种算法,因为在此我用到的基本原理是一个比较器。
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This is a very important concept, and we'll see a lot of algorithms essentially implement decision trees.
这是一个很重要的概念，我们可以看到很多,基于决策树的算法。
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And we haven't seen all of these sorts, but it's actually quite neat to recognize how different the underlying work is of each of these algorithms.
我们还没有探讨所有的排序算法，但应该很容易区分,每一种算法其内在的,不同之处。
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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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OK. At this point, if we stop, you'll think all algorithms are linear. This is really boring.
可能我们就认为所有的解决这个问题的，算法都是线性增长的了，真很没意思,但是他们真不是对不对？
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What have we done over the last three or four lectures? We've started introducing you to classes of algorithms. Things that I'd like you to be able to see are how to do some simple complexity analysis.
我们在过去三节或四节课中讲了什么？,我开始向你们,介绍算法的类型,我想你们知道的是,如何做一些简单的复杂度分析。
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And we want to help you learn how to map a problem into a class of algorithms of some efficiency That's our goal.
学会如何将一个问题,映射到一类,提升效率的算法中去,这就是我们的目标。
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People use bin packing algorithms to figure out, for example, how to load up container ships.
人么通常用装箱算法来计算这个问题，比如说怎样装载货船。
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But I want you to take away from this how we reason about the complexity of these algorithms. And I'll remind you, we said we're going to mostly talk about time.
但是我想你们理解我们,是怎么推出这些问题的复杂度的,提醒大家，我说过了我们主要讨论时间问题。
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Selection sort too really reduces to a total number of comparisons because I'm again comparing the current smallest to the next thing I see, the next thing, so really a lot of these sorting algorithms boil down to comparisons and the numbers that you actually have to make.
选择排序也可归纳为总数的比较,因为要将当前最小者与下一个进行比较,接着再下一个，可见,很多排序算法都可归结为比较,以及需要比较的次数。
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It's a very common class of algorithms, and it's very useful one to have.
这是非常常见的一类的算法，并且也非常的实用。
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Which of these algorithms is better?
哪一个算法更好呢？
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OK. Binary search is perhaps the simplest of the divide and conquer algorithms, and what does that mean? It says, in order to solve a problem, cut it down to a smaller problem and try and solve that one.
好了，二分搜索也许是,最简单的分治算法，这意味着什么？,也就是说，为了解决一个问题,把它切割成小的问题,然后再来一个个解决。
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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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One of the things you should begin to do, is to recognize what identifies those classes of algorithms, so you can map your problems into those ranges.
你们现在开始要做的事情之一，就是要学会辨别这些算法,这样你就能为你的问题，选择对应种类的算法了。
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OK. Today, for the rest of the lecture, we're going to take a break from the topic of algorithms, and computation, and things of the sort. And do something pretty pragmatic.
好，今天剩下的时间里，我们不再讲算法，计算和排序这些话题了,我们会做一些非常实用的事,我们主要的讲讲测试。
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Algorithms for searching. Debugging is simply a search process.
调试简单点来说,就是一个搜索的过程。
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And part of it is we want you to learn how to have a catalog, if you like, of different classes of algorithms, so that when you get a problem, you try and map it into an appropriate class, and use the leverage, if you like, of that class of algorithms.
另外我希望你们能学会,如何制作一个关于，算法不同课程的目录，这样当你碰到问题的时候，你可以去找到合适的课程，然后在那门课程中，寻找解决方法。
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We saw some quadratic algorithms, typically those are things with multiple nested loops, or iterative or recursive calls, where you're doing, say, a linear amount of time but you're doing it a linear number of times and so it becomes quadratic, and you'll see other polynomial kinds of algorithms.
我们看过一些平方算法，他们一般进行了多次嵌套循环,或者递归迭代调用，对一个线性操作调用线性次,这样就变成平方次了,以后你们能看到,一些多项式算法。
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We don't like exponential algorithms, or at least you shouldn't like them, because they blow up quickly. And we saw some examples of that. And unfortunately, some problems are inherently exponential, you're sort of stuck with that, and then you just have to try be as clever as you can.
我们并不喜欢指数算法，至少你不应该喜欢，因为他们会,爆炸式的增长，我们将看到,这样的例子，不幸的是，一些问题先天是指数型的，你会被它们稍稍的卡住,然后你就得尽可能的发挥你聪明才智了。
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I now want to get back to algorithms.
让我们回到算法上来。
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Building up examples of algorithms.
怎么样把代码中的特性。
应用推荐