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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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So in fact, what does that suggest about the order of growth here? What is the complexity of this? Yeah. Logarithmic. Why?
复杂度是多少？,对的，对数级的么？，为什么呢？,学生：对数级的？
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All right? It's now something that I can search in constant time. And that's what's going to allow me to keep this thing as being log.
在固定的时间内搜索,这样就可以让时间复杂度保持在对数级,好的，考虑过了这些。
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It's a good sign that this is logarithmic, and I'm going to come back in a second to why logs are a great thing.
为什么对数级复杂度是个好事情，让我们再来看一个算法,噢，抱歉是让我们再来看两个算法。
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Right? If that was the case in that code, then my complexity is no longer log, because I need linear access for each time I've got to go to the list, and it's going to Lisp be much worse than that.
这里的复杂度不再是对数的了，因为每次在列表中，查找需要线性访问,可能还要糟糕,其实，有些编程语言，如。
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This case, I reduced the size of the problem in half.
这很好的表明了这是，对数级复杂度的问题，我马上就要解释。
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Yeah. Is the last one there?
对数级复杂度的算法的优势对不对？
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And as a consequence, it is log.
因此这是对数级复杂度的算法。
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With this, if I can assume that accessing the i'th element of a list is constant, then you can't see that the rest of that analysis looks just like the log analysis I did before, and each step, no matter which branch I'm taking, I'm cutting the problem down in half.
读取数组中的第i个元素,是个常量时间的操作的话，我也就能像以前那样得到,这个算法是对数级复杂度的分析,并且每一步不管我选择哪个区间，我都可以把问题的规模缩小一半。
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