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.

http://javabat.com是一个有趣而令人兴奋的练习递归的地方。他们的例子开始时相当简单，然后逐步扩展(如果你想这么做的话)。注意:他们的方法是在实践中学习。这是我写的一个递归函数，用来替换for循环。

for循环:

public printBar(length)
{
  String holder = "";
  for (int index = 0; i < length; i++)
  {
    holder += "*"
  }
  return holder;
}

这是做同样事情的递归。(请注意，我们重载了第一个方法，以确保它像上面那样使用)。我们还有另一种方法来维护索引(类似于上面的for语句)。递归函数必须维护自己的索引。

public String printBar(int Length) // Method, to call the recursive function
{
  printBar(length, 0);
}

public String printBar(int length, int index) //Overloaded recursive method
{
  // To get a better idea of how this works without a for loop
  // you can also replace this if/else with the for loop and
  // operationally, it should do the same thing.
  if (index >= length)
    return "";
  else
    return "*" + printBar(length, index + 1); // Make recursive call
}

简而言之，递归是一种编写更少代码的好方法。在后面的printBar中，请注意我们有一个if语句。如果我们的条件已经达到，我们将退出递归并返回到前一个方法，该方法返回到前一个方法，等等。如果我发送一个printBar(8)，我得到********。我希望通过一个简单函数的例子，它做的事情与for循环相同，这可能会有所帮助。不过，您可以在Java Bat中进行更多的练习。

你的大脑爆炸是因为它进入了无限递归。这是初学者常犯的错误。

信不信由你，你已经理解了递归，你只是被一个常见的，但错误的函数比喻拖了下来:一个小盒子，里面有东西进进出出。

而不是考虑一个任务或过程，比如“在网上找到更多关于递归的知识”。这是递归的，没有问题。要完成这个任务，你可以:

a) Read a Google's result page for "recursion"
b) Once you've read it, follow the first link on it and...
a.1)Read that new page about recursion 
b.1)Once you've read it, follow the first link on it and...
a.2)Read that new page about recursion 
b.2)Once you've read it, follow the first link on it and...

如您所见，您已经做了很长一段时间的递归工作，没有出现任何问题。

你会坚持做这个任务多久?永远，直到你的大脑爆炸?当然不是，只要你相信你已经完成了任务，你就会停在一个给定的点上。

当要求你“在网上找到更多关于递归的知识”时，没有必要指定这一点，因为你是一个人，你可以自己推断。

计算机无法推断任何东西，所以你必须包含一个明确的结尾:“在网上找到更多关于递归的知识，直到你理解它或你阅读了最多10页”。

您还推断应该从谷歌的结果页面开始进行“递归”，这也是计算机无法做到的。递归任务的完整描述还必须包括一个显式的起点:

“在网上找到更多关于递归的知识，直到你理解它，或者你已经阅读了最多10页，并从www.google.com/search?q=recursion开始”

要想全面了解，我建议你试试下面这些书:

普通Lisp:符号计算的简单介绍。这是对递归最可爱的非数学解释。 小阴谋家。

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.

递归函数只是一个函数，它可以根据需要多次调用自己。如果您需要多次处理某件事，但不确定实际需要多少次，那么它就很有用。在某种程度上，你可以把递归函数看作是一种循环。然而，就像循环一样，您需要指定中断流程的条件，否则它将变得无限。