算法课程大纲

A. 200分悬赏急用英译汉

这几个的确句型有些长,翻译习惯也可以参照一下别人的,反正今天好象回答你的人好多.
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overview
The order of growth of the running time of an algorithm, defined in Chapter 2, gives a simple
characterization of the algorithm's efficiency and also allows us to compare the relative
performance of alternative algorithms. Once the input size n becomes large enough, merge
sort, with its Θ(n lg n) worst-case running time, beats insertion sort, whose worst-case running
time is Θ(n2). Although we can sometimes determine the exact running time of an algorithm,
as we did for insertion sort in Chapter 2, the extra precision is not usually worth the effort of
computing it. For large enough inputs, the multiplicative constants and lower-order terms of
an exact running time are dominated by the effects of the input size itself.

When we look at input sizes large enough to make only the order of growth of the running
time relevant, we are studying the asymptotic efficiency of algorithms. That is, we are
concerned with how the running time of an algorithm increases with the size of the input in
the limit, as the size of the input increases without bound. Usually, an algorithm that is
asymptotically more efficient will be the best choice for all but very small inputs.

概要

算法运行时间的成长次序, 被定义在第二章里, 给算法的特性一个简单的描述并允许我们把它与供选择的算法进行比较。一旦输入大小n变得足够大, 合并排序法, 以Θ(nlgn) 最差运行时间,来检验最差运行时间是Θ(n2)的插入排序。虽然我们有时能确定算法确切的运行时间, 但如同在第二章里我们为了插入排序,而努力计算额外精确度通常并没有价值。为了足够大的投入, 常数和一具体运行时间的低秩序期限的积由投入规模本身所控制.
当投入规模大到足够确定相关命令运行时间的情况下, 我们应该学习渐进效率算法。即，当输入大小的增加没有限制，我们就要考虑怎样计算运行时间的增长变化，就像输入大小有限制的时候那样。通常, 除了非常小的输入，渐进算法是更加高效率的算法，将是最佳的选择。

This chapter gives several standard methods for simplifying the asymptotic analysis of
algorithms. The next section begins by defining several types of "asymptotic notation," of
which we have already seen an example in Θ-notation. Several notational conventions used
throughout this book are then presented, and finally we review the behavior of functions that
commonly arise in the analysis of algorithms.

本章讲述了算法渐进分析的几种简化方法。下一部分将首先定义几种“渐进符号”，在这些符号中我们已经见过的如Θ符号。还将给出本书中通篇出现的几种符号约定。最后我们将重温一下出现在算法分析中的常用函数的特性。

3.1 Asymptotic notation The notations we use to describe the asymptotic running time of an algorithm are defined in
terms of functions whose domains are the set of natural numbers N = {0, 1, 2, ...}.
Such notations are convenient for describing the worst-case running-time function T (n), which is
usually defined only on integer input sizes.
It is sometimes convenient, however, to abuse asymptotic notation in a variety of ways.

For example, the notation is easily extended to the domain of real numbers or, alternatively, restricted to a subset of the natural numbers. It is important, however, to understand the precise meaning of the notation so that when it is
abused, it is not misused. This section defines the basic asymptotic notations and also
introces some common abuses.

3.1 渐进法

我们用来描述连续运行时间算法的一种记数法，其定义是：函数项的域值为自然数N={0，1，2，…}。
这种记数法在描述最简单的连续时间函数T(n)是方便的，因为它的输入范围通常仅取整数。它有时是方便的，然而，我们需要用比较多的方式来描述不规则渐进记法。例如，记法很容易在实数域内取值，或者交替地限定在自然数的一个子集内。尽管记数法重要，但我们需要了解其精确涵义，以便函数不规则时它没有被误用。本节定义了基本的渐进记法并且介绍一些一般的不规则函数。

B. python中有哪些简单的算法，看了黑马和中公的课程大纲，有推荐的不

编程语言和算法没有必然关系，
如果是实现的算法，一般都是第三方库方式，比如数据分析用到很多算法
如果是方法论的算法，比如贪心，分治之类的就更没有具体实现了
Python里面有的算法恐怕就是排序了，timsort