简单的递归?
我是编程新手,很难理解递归。有一个问题我一直在解决但无法解决。我真的不明白它们是如何解决的。
“定义一个过程 plus,它接受两个非负整数并返回它们的和。您可以使用的唯一过程(除了对 plus 的递归调用之外)是:zero?、sub1 和 add1。
我知道这是一个内置过程方案中的函数,所以我知道它们是可以解决的,我只是不明白递归是如此棘手
! =] 谢谢
我正在 Petite Chez Scheme 工作(使用 SWL 编辑器)
I'm new to programming and have had a hard time understanding recursion. There's a problem I've been working on but can't figure out. I really just don't understand how they are solvable.
"Define a procedure plus that takes two non-negative integers and returns their sum. The only procedures (other than recursive calls to plus) that you may use are: zero?, sub1, and add1.
I know that this is a built in functions in scheme, so I know they're possible to solve, I just don't understand how. Recursion is so tricky!
Any help would be greatly appreciated!
=] Thanks
I'm working in Petite Chez Scheme (with the SWL editor)
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递归是软件开发中一个非常重要的概念。我不知道(petit chez)方案,所以我将从一般角度来处理这个问题。
递归函数的概念是一遍又一遍地重复相同的任务,直到达到某个限制边界。就第一个问题而言,您有两个数字,您需要将它们加在一起。但是,您只能将数字加 1 或将数字减 1。您的字面值也为零。
所以。将数字视为两个桶。他们每人都有 10 块石头。您想将这两个桶“添加”在一起。您一次只能移动一块石头(即您不能抓起一把石头或将一个桶倒入另一个桶中)。
假设您想将所有东西从左边的桶移到右边的桶中,一次一块石头。你需要做什么?
首先,您必须从左边的桶中取出 1 块石头,即您使用
sub1
从桶中取出一颗石头。然后,您将同一块石头添加到右侧的桶中,即您add1
到右侧的桶中。现在您可以循环执行此操作,但您不知道任何给定解决方案中会有多少石头。你真正想做的是说“从左边的桶中取出一颗石头,将其放入右边的桶中,然后重复,直到左边的桶中没有石头为止。”左桶中没有石头的情况称为“基本情况”,此时您会说“好吧,我现在完成了”,
这种情况的伪代码示例是(使用您的 plus、add1、sub1)。和零):
如果您仍然需要更多帮助,请告诉我,我可以运行其他示例,但这看起来对您来说可能是一个家庭作业问题,并且递归对于理解非常重要,所以我不想只给您全部答案。
Recursion is a very important concept in software development. I don't know (petit chez) scheme so I will approach this from a general angle.
The concept of a recursive function is to repeat the same task over and over again until you reach some limiting boundary. Taking your first question, you have two numbers and you need to add them together. However, you only have the ability to add 1 to a number or subtract 1 from a number. You also have the literal value zero.
So. consider you numbers as two buckets. They each have 10 stones in them. You want to "add" those two buckets together. You are only permitted to move one stone at a time (i.e. you can't grab a handful or tip one bucket into the other).
Lets say you want to move everything from the left bucket into the right bucket, one stone at a time. What are you going to have to do?
First, you have to take 1 stone from the left bucket, i.e. you are using
sub1
to remove one stone from the bucket. You then add that same stone to the right bucket, i.e. youadd1
to the right bucket.Now you could do this in a loop, but you don't know how many stones there will be in any given solution. What you really want to do is say "Take one stone from the left bucket, put it in the right bucket and repeat until there are no stones in the left bucket.' This case of there being no stones in the left bucket is called you "Base Case". It's the point at which you say OK, I'm done now.
A pseudocode example of this situation would be (using your plus, add1, sub1 and zero):
If you still need more help, let me know, I can run through other examples but this looks like it's probably a homework problem for you and recursion is incredibly important to understand so I don't want to just give you all the answers.
递归只是一个调用自身的函数。最常见、最容易理解的递归示例是遍历看起来像树的数据结构。
您将如何访问树的每个分支?您将从树干开始并调用访问(分支),将树干作为第一个分支传递。 Visit() 为每个分支的每个分支调用自身,依此类推。
Recursion is simply a function that calls itself. The most common, easily understood examples of recursion is walking a data structure that looks like a tree.
How would you visit each branch of a tree? You would start at the trunk and call visit(branch), passing the trunk of the tree as the first branch. Visit() calls itself for each branch of each branch, and so on.
递归与归纳密切相关 - 首先解决(或证明)基本情况,然后假设您的解决方案对于某些值n是正确的,并用它来解决(或证明)n + 1。
所以这里的第一步是看第一个问题。将两个数字相加的良好基本情况是什么?
好吧,我们有了基本情况:当其中一个数字为零时。
为了简单起见,我们假设第二个数字为零,只是为了让事情变得更容易一些。
所以我们知道
(+ n 0)
等于n
。因此,现在对于我们的递归步骤,我们想要进行任意调用(+ xy)
,并将其转换为更接近我们理想的(+ n 0)
。这样我们就会取得一些进展并最终解决我们的问题。那么我们要怎么做呢?
(+ xy)
当然等价于(+ (add1 x) (sub1 y))
- 这让我们更接近(zero? y)
。这给了我们最终的解决方案:(
当然,您可以交换参数的顺序,它仍然是等效的)。
类似的机制可以用来解决另外两个问题。
Recursion is closely related to induction - first you solve (or prove) a base case, and then you assume your solution is correct for some value n, and use that to solve (or prove) it for n + 1.
So the first step here is to look at the first problem. What would be a good base case for adding two numbers together?
Alright, so we have our base case: when one of the numbers is zero.
For simplicity's sake, we'll assume that the second number is zero, just to make things a little easier.
So we know that
(+ n 0)
is equal ton
. So now for our recursive step, we want to take an arbitrary call(+ x y)
, and turn that into a call which is closer to our ideal(+ n 0)
. That way we'll have made some progress and will eventually solve our problem.So how are we going to do this?
(+ x y)
is of course equivalent to(+ (add1 x) (sub1 y))
- which takes us closer to our base case of(zero? y)
.This gives us our final solution:
(you can, of course, swap the order of the arguments and it will still be equivalent).
A similar mechanism can be used to solve the other two problems.