在这种情况下，不变量意味着在每次循环迭代的某一点上必须为真条件。

在契约编程中，不变量是在调用任何公共方法之前和之后必须为真(通过契约)的条件。

简单地说，循环不变量是对循环的每次迭代都成立的某个谓词(条件)。例如，让我们看一个简单的For循环，它是这样的:

int j = 9;
for(int i=0; i<10; i++)  
  j--;

在这个例子中，i + j == 9(对于每个迭代)是正确的。一个较弱的不变式也是成立的 I >= 0 && I <= 10。

我喜欢这个非常简单的定义:

循环不变量是(程序变量之间的)一种条件，它必须在每次循环迭代之前和之后立即为真。(请注意，在迭代过程中，这并没有说明它的真伪。)

By itself, a loop invariant doesn't do much. However, given an appropriate invariant, it can be used to help prove the correctness of an algorithm. The simple example in CLRS probably has to do with sorting. For example, let your loop invariant be something like, at the start of the loop, the first i entries of this array are sorted. If you can prove that this is indeed a loop invariant (i.e. that it holds before and after every loop iteration), you can use this to prove the correctness of a sorting algorithm: at the termination of the loop, the loop invariant is still satisfied, and the counter i is the length of the array. Therefore, the first i entries are sorted means the entire array is sorted.

一个更简单的例子:循环不变量、正确性和程序推导。

我理解循环不变量的方式是作为一个系统的，正式的工具来推理程序。我们做了一个陈述，我们专注于证明它是正确的，我们称之为循环不变量。这组织了我们的逻辑。虽然我们也可以非正式地讨论一些算法的正确性，但使用循环不变量迫使我们非常仔细地思考，并确保我们的推理无懈可击。

值得注意的是，循环不变量可以帮助迭代算法的设计，因为它被认为是一个断言，表示变量之间的重要关系，在每次迭代开始时和循环结束时，这些关系必须为真。如果这是成立的，计算是在有效的道路上。如果为false，则算法失败。

除了这些不错的答案，我想Jeff Edmonds在《如何思考算法》(How to Think About Algorithms)中举的一个很好的例子可以很好地说明这个概念:

EXAMPLE 1.2.1 "The Find-Max Two-Finger Algorithm" 1) Specifications: An input instance consists of a list L(1..n) of elements. The output consists of an index i such that L(i) has maximum value. If there are multiple entries with this same value, then any one of them is returned. 2) Basic Steps: You decide on the two-finger method. Your right finger runs down the list. 3) Measure of Progress: The measure of progress is how far along the list your right finger is. 4) The Loop Invariant: The loop invariant states that your left finger points to one of the largest entries encountered so far by your right finger. 5) Main Steps: Each iteration, you move your right finger down one entry in the list. If your right finger is now pointing at an entry that is larger then the left finger’s entry, then move your left finger to be with your right finger. 6) Make Progress: You make progress because your right finger moves one entry. 7) Maintain Loop Invariant: You know that the loop invariant has been maintained as follows. For each step, the new left finger element is Max(old left finger element, new element). By the loop invariant, this is Max(Max(shorter list), new element). Mathe- matically, this is Max(longer list). 8) Establishing the Loop Invariant: You initially establish the loop invariant by point- ing both fingers to the first element. 9) Exit Condition: You are done when your right finger has finished traversing the list. 10) Ending: In the end, we know the problem is solved as follows. By the exit condi- tion, your right finger has encountered all of the entries. By the loop invariant, your left finger points at the maximum of these. Return this entry. 11) Termination and Running Time: The time required is some constant times the length of the list. 12) Special Cases: Check what happens when there are multiple entries with the same value or when n = 0 or n = 1. 13) Coding and Implementation Details: ... 14) Formal Proof: The correctness of the algorithm follows from the above steps.

在处理循环和不变量时，有一件事很多人没有马上意识到。他们混淆了循环不变量和循环条件(控制循环终止的条件)。

正如人们指出的那样，循环不变量必须为真

在循环开始之前 在每次循环迭代之前 在循环结束之后

(尽管在循环体期间它可以暂时为假)。另一方面，循环条件在循环结束后必须为false，否则循环将永远不会终止。

因此循环不变量和循环条件必须是不同的条件。

复杂循环不变量的一个很好的例子是用于二分搜索。

bsearch(type A[], type a) {
start = 1, end = length(A)

    while ( start <= end ) {
        mid = floor(start + end / 2)

        if ( A[mid] == a ) return mid
        if ( A[mid] > a ) end = mid - 1
        if ( A[mid] < a ) start = mid + 1

    }
    return -1

}

因此循环条件看起来非常简单——当开始>结束时，循环终止。但是为什么循环是正确的呢?什么是循环不变量来证明它的正确性?

不变量是逻辑语句:

if ( A[mid] == a ) then ( start <= mid <= end )

这句话是逻辑重言——在我们试图证明的特定循环/算法的上下文中，它总是正确的。并且在循环结束后，它提供了关于循环正确性的有用信息。

If we return because we found the element in the array then the statement is clearly true, since if A[mid] == a then a is in the array and mid must be between start and end. And if the loop terminates because start > end then there can be no number such that start <= mid and mid <= end and therefore we know that the statement A[mid] == a must be false. However, as a result the overall logical statement is still true in the null sense. ( In logic the statement if ( false ) then ( something ) is always true. )

那么我说的循环条件在循环结束时必然为假呢?当在数组中找到元素时，循环条件在循环结束时为true !?实际上不是，因为隐含的循环条件实际上是while (A[mid] != A && start <= end)，但我们缩短了实际的测试，因为第一部分是隐含的。这个条件在循环结束后明显为false，而不管循环如何结束。

不变的意思是永不改变

这里循环不变量的意思是“发生在循环中的变量的变化(增加或减少)并没有改变循环条件，即条件是满足的”，因此循环不变量的概念就产生了

之前的回答已经很好地定义了循环不变量。

以下是CLRS的作者如何使用循环不变量来证明插入排序的正确性。

插入排序算法(见书):

INSERTION-SORT(A)
    for j ← 2 to length[A]
        do key ← A[j]
        // Insert A[j] into the sorted sequence A[1..j-1].
        i ← j - 1
        while i > 0 and A[i] > key
            do A[i + 1] ← A[i]
            i ← i - 1
        A[i + 1] ← key

循环不变量在这种情况下: 子数组[1到j-1]始终被排序。

现在让我们检查一下，证明这个算法是正确的。

初始化:在第一次迭代j=2之前。所以子数组[1:1]就是要测试的数组。因为它只有一个元素，所以它是有序的。这样不变性就被满足了。

维护:这可以通过在每次迭代后检查不变量来轻松验证。在这种情况下，它被满足了。

终止:这是我们将证明算法正确性的步骤。

当循环结束时，j=n+1。循环不变量再次被满足。这意味着子数组[1到n]应该排序。

这就是我们想用算法做的。因此，我们的算法是正确的。

Loop invariant is a mathematical formula such as (x=y+1). In that example, x and y represent two variables in a loop. Considering the changing behavior of those variables throughout the execution of the code, it is almost impossible to test all possible to x and y values and see if they produce any bug. Lets say x is an integer. Integer can hold 32 bit space in the memory. If that number exceeds, buffer overflow occurs. So we need to be sure that throughout the execution of the code, it never exceeds that space. for that, we need to understand a general formula that shows the relationship between variables. After all, we just try to understand the behavior of the program.

在线性搜索(根据书中给出的练习)中，我们需要在给定的数组中找到值V。

它很简单，从0 <= k < length开始扫描数组并比较每个元素。如果找到V，或者扫描到数组的长度，就终止循环。

根据我对上述问题的理解-

循环不变量(初始化): 在k - 1迭代中找不到V。第一次迭代，这是-1因此我们可以说V不在-1位置

保养: 在下一次迭代中，V不在k-1中成立

Terminatation: 如果V位于k个位置，或者k达到数组的长度，则终止循环。

It is hard to keep track of what is happening with loops. Loops which don't terminate or terminate without achieving their goal behavior is a common problem in computer programming. Loop invariants help. A loop invariant is a formal statement about the relationship between variables in your program which holds true just before the loop is ever run (establishing the invariant) and is true again at the bottom of the loop, each time through the loop (maintaining the invariant). Here is the general pattern of the use of Loop Invariants in your code:

... // the Loop Invariant must be true here while ( TEST CONDITION ) { // top of the loop ... // bottom of the loop // the Loop Invariant must be true here } // Termination + Loop Invariant = Goal ... Between the top and bottom of the loop, headway is presumably being made towards reaching the loop's goal. This might disturb (make false) the invariant. The point of Loop Invariants is the promise that the invariant will be restored before repeating the loop body each time. There are two advantages to this:

Work is not carried forward to the next pass in complicated, data dependent ways. Each pass through the loop in independent of all others, with the invariant serving to bind the passes together into a working whole. Reasoning that your loop works is reduced to reasoning that the loop invariant is restored with each pass through the loop. This breaks the complicated overall behavior of the loop into small simple steps, each which can be considered separately. The test condition of the loop is not part of the invariant. It is what makes the loop terminate. You consider separately two things: why the loop should ever terminate, and why the loop achieves its goal when it terminates. The loop will terminate if each time through the loop you move closer to satisfying the termination condition. It is often easy to assure this: e.g. stepping a counter variable by one until it reaches a fixed upper limit. Sometimes the reasoning behind termination is more difficult.

应该创建循环不变量，以便当达到终止条件时，且不变量为真，则达到目标:

不变+终止=>目标 它需要实践来创建简单而相关的不变式，这些不变式捕获了除了终止之外的所有目标实现。最好使用数学符号来表示循环不变量，但当这导致过于复杂的情况时，我们依赖于清晰的散文和常识。

对不起，我没有评论权限。

正如你提到的@Tomas Petricek

另一个较弱的不变式也是成立的，即i >= 0 && i < 10(因为这是连续条件!)”

为什么它是循环不变量?

我希望我没有错，据我理解[1]，循环不变将在循环开始时为真(初始化)，它将在每次迭代(维护)之前和之后为真，它也将在循环结束后为真(终止)。但是在最后一次迭代之后，i变成了10。因此，条件i >= 0 && i < 10变为假值并终止循环。它违反了循环不变量的第三个性质(终止)。

[1] http://www.win.tue.nl/~kbuchin/teaching/JBP030/notebooks/loop-invariants.html

循环不变量属性是一个条件，适用于循环执行的每一步。For循环，while循环，等等)

这对于循环不变证明是必不可少的，如果在执行的每一步都保持循环不变属性，则可以证明算法正确执行。

对于一个正确的算法，循环不变量必须保持在:

初始化(开始)

维护(之后的每一步)

终止(当它完成时)

这被用来计算很多东西，但最好的例子是加权图遍历的贪婪算法。对于贪心算法产生最优解(穿过图的路径)，它必须达到连接所有节点在最小权值路径可能。

因此，循环不变的性质是所选择的路径具有最小的权值。在开始时，我们没有添加任何边，所以这个属性为真(在这种情况下，它不是假的)。在每一步中，我们都遵循最小权值边(贪婪步)，所以我们再次采用最小权值路径。最后，我们找到了最小加权路径，所以我们的性质也是成立的。

如果一个算法不这样做，我们可以证明它不是最优的。

简单地说，它是一个循环条件，在每次循环迭代中都为真:

for(int i=0; i<10; i++)
{ }

在这里，我们可以说i的状态是i<10并且i>=0

循环不变量是在循环执行前后为真的断言。

《如何思考算法》的定义，Jeff Edmonds著

循环不变式是放置在循环和循环顶部的断言 每次计算返回到循环的顶部时，这必须成立。