构建递归函数的真正数学方法如下:

1:假设你有一个函数对f(n-1)是正确的，构造f使f(n)是正确的。 2:构造f，使得f(1)是正确的。

This is how you can prove that the function is correct, mathematically, and it's called Induction. It is equivalent to have different base cases, or more complicated functions on multiple variables). It is also equivalent to imagine that f(x) is correct for all x Now for a "simple" example. Build a function that can determine if it is possible to have a coin combination of 5 cents and 7 cents to make x cents. For example, it's possible to have 17 cents by 2x5 + 1x7, but impossible to have 16 cents. Now imagine you have a function that tells you if it's possible to create x cents, as long as x < n. Call this function can_create_coins_small. It should be fairly simple to imagine how to make the function for n. Now build your function: bool can_create_coins(int n) { if (n >= 7 && can_create_coins_small(n-7)) return true; else if (n >= 5 && can_create_coins_small(n-5)) return true; else return false; } The trick here is to realize that the fact that can_create_coins works for n, means that you can substitute can_create_coins for can_create_coins_small, giving: bool can_create_coins(int n) { if (n >= 7 && can_create_coins(n-7)) return true; else if (n >= 5 && can_create_coins(n-5)) return true; else return false; } One last thing to do is to have a base case to stop infinite recursion. Note that if you are trying to create 0 cents, then that is possible by having no coins. Adding this condition gives: bool can_create_coins(int n) { if (n == 0) return true; else if (n >= 7 && can_create_coins(n-7)) return true; else if (n >= 5 && can_create_coins(n-5)) return true; else return false; } It can be proven that this function will always return, using a method called infinite descent, but that isn't necessary here. You can imagine that f(n) only calls lower values of n, and will always eventually reach 0. To use this information to solve your Tower of Hanoi problem, I think the trick is to assume you have a function to move n-1 tablets from a to b (for any a/b), trying to move n tables from a to b.

Ouch. I tried to figure out the Towers of Hanoi last year. The tricky thing about TOH is it's not a simple example of recursion - you have nested recursions which also change the roles of towers on each call. The only way I could get it to make sense was to literally visualize the movement of the rings in my mind's eye, and verbalize what the recursive call would be. I would start with a single ring, then two, then three. I actually ordered the game on the internet. It took me maybe two or three days of cracking my brains to get it.

你在用哪本书?

关于算法的标准教科书是Cormen & Rivest。我的经验是，它很好地教授了递归。

递归是编程中较难掌握的部分之一，虽然它确实需要本能，但它是可以学习的。但它确实需要一个好的描述，好的例子和好的插图。

此外，30页通常是很多的，30页是用一种编程语言编写的。在你从一本普通的书中理解递归之前，不要尝试用C或Java学习递归。

要理解递归，你只需要看看洗发水瓶上的标签:

function repeat()
{
   rinse();
   lather();
   repeat();
}

这样做的问题是没有终止条件，递归将无限重复，或者直到洗发水或热水用完为止(外部终止条件，类似于吹你的堆栈)。

递归函数就像弹簧，每次调用都要压缩一点。在每一步中，您将一些信息(当前上下文)放在堆栈上。当到达最后一步时，释放弹簧，立即收集所有值(上下文)!

不确定这个比喻是否有效…: -)

无论如何，除了经典的例子(阶乘是最糟糕的例子，因为它效率低，很容易被平化，Fibonacci, Hanoi…)，这些都有点人为(我很少，如果有的话，在实际编程案例中使用它们)，看看它真正被使用的地方是有趣的。

A very common case is to walk a tree (or a graph, but trees are more common, in general). For example, a folder hierarchy: to list the files, you iterate on them. If you find a sub-directory, the function listing the files call itself with the new folder as argument. When coming back from listing this new folder (and its sub-folders!), it resumes its context, to the next file (or folder). Another concrete case is when drawing a hierarchy of GUI components: it is common to have containers, like panes, to hold components which can be panes too, or compound components, etc. The painting routine calls recursively the paint function of each component, which calls the paint function of all the components it holds, etc.

不确定我是否很清楚，但我喜欢展示现实世界中教材的使用，因为这是我过去偶然发现的东西。

构建递归函数的真正数学方法如下:

1:假设你有一个函数对f(n-1)是正确的，构造f使f(n)是正确的。 2:构造f，使得f(1)是正确的。

This is how you can prove that the function is correct, mathematically, and it's called Induction. It is equivalent to have different base cases, or more complicated functions on multiple variables). It is also equivalent to imagine that f(x) is correct for all x Now for a "simple" example. Build a function that can determine if it is possible to have a coin combination of 5 cents and 7 cents to make x cents. For example, it's possible to have 17 cents by 2x5 + 1x7, but impossible to have 16 cents. Now imagine you have a function that tells you if it's possible to create x cents, as long as x < n. Call this function can_create_coins_small. It should be fairly simple to imagine how to make the function for n. Now build your function: bool can_create_coins(int n) { if (n >= 7 && can_create_coins_small(n-7)) return true; else if (n >= 5 && can_create_coins_small(n-5)) return true; else return false; } The trick here is to realize that the fact that can_create_coins works for n, means that you can substitute can_create_coins for can_create_coins_small, giving: bool can_create_coins(int n) { if (n >= 7 && can_create_coins(n-7)) return true; else if (n >= 5 && can_create_coins(n-5)) return true; else return false; } One last thing to do is to have a base case to stop infinite recursion. Note that if you are trying to create 0 cents, then that is possible by having no coins. Adding this condition gives: bool can_create_coins(int n) { if (n == 0) return true; else if (n >= 7 && can_create_coins(n-7)) return true; else if (n >= 5 && can_create_coins(n-5)) return true; else return false; } It can be proven that this function will always return, using a method called infinite descent, but that isn't necessary here. You can imagine that f(n) only calls lower values of n, and will always eventually reach 0. To use this information to solve your Tower of Hanoi problem, I think the trick is to assume you have a function to move n-1 tablets from a to b (for any a/b), trying to move n tables from a to b.