有没有O(1/n)种算法?

或者其他小于O(1)的数?


当前回答

那么这个呢:

void FindRandomInList(list l)
{
    while(1)
    {
        int rand = Random.next();
        if (l.contains(rand))
            return;
    }
}

随着列表大小的增加,程序的预期运行时间会减少。

其他回答

我相信量子算法可以通过叠加“一次”进行多次计算……

我怀疑这是一个有用的答案。

inline void O0Algorithm() {}

那么这个呢:

void FindRandomInList(list l)
{
    while(1)
    {
        int rand = Random.next();
        if (l.contains(rand))
            return;
    }
}

随着列表大小的增加,程序的预期运行时间会减少。

其余的大多数答案都将大o解释为专门关于算法的运行时间。但是因为问题没有提到它,我认为值得一提的是大o在数值分析中的另一个应用,关于误差。

Many algorithms can be O(h^p) or O(n^{-p}) depending on whether you're talking about step-size (h) or number of divisions (n). For example, in Euler's method, you look for an estimate of y(h) given that you know y(0) and dy/dx (the derivative of y). Your estimate of y(h) is more accurate the closer h is to 0. So in order to find y(x) for some arbitrary x, one takes the interval 0 to x, splits it up until n pieces, and runs Euler's method at each point, to get from y(0) to y(x/n) to y(2x/n), and so on.

欧拉方法是O(h)或O(1/n)算法,其中h通常被解释为步长n被解释为你划分一个区间的次数。

在实际数值分析应用中,由于浮点舍入误差,也可以有O(1/h)。你的间隔越小,某些算法的实现就会抵消得越多,丢失的有效数字就越多,因此在算法中传播的错误也就越多。

For Euler's method, if you are using floating points, use a small enough step and cancellation and you're adding a small number to a big number, leaving the big number unchanged. For algorithms that calculate the derivative through subtracting from each other two numbers from a function evaluated at two very close positions, approximating y'(x) with (y(x+h) - y(x) / h), in smooth functions y(x+h) gets close to y(x) resulting in large cancellation and an estimate for the derivative with fewer significant figures. This will in turn propagate to whatever algorithm you require the derivative for (e.g., a boundary value problem).

是的。

只有一种算法运行时为O(1/n),即“空”算法。

对于O(1/n)的算法来说,这意味着它渐进地执行的步骤比由单个指令组成的算法少。如果对于所有n个> n0,它执行的步骤少于1步,则对于这n个,它必须完全不包含任何指令。由于检查' If n > n0'至少需要1条指令,因此对于所有n个,它必须不包含任何指令。

总结: 唯一的算法是O(1/n)是空算法,不包含任何指令。