有没有O(1/n)种算法?

或者其他小于O(1)的数?


当前回答

正如已经指出的,除了null函数可能的例外,不可能有O(1/n)个函数,因为所花费的时间必须接近0。

当然,有一些算法,比如康拉德定义的算法,它们至少在某种意义上应该小于O(1)

def get_faster(list):
    how_long = 1/len(list)
    sleep(how_long)

If you want to investigate these algorithms, you should either define your own asymptotic measurement, or your own notion of time. For example, in the above algorithm, I could allow the use of a number of "free" operations a set amount of times. In the above algorithm, if I define t' by excluding the time for everything but the sleep, then t'=1/n, which is O(1/n). There are probably better examples, as the asymptotic behavior is trivial. In fact, I am sure that someone out there can come up with senses that give non-trivial results.

其他回答

这里有另一种方法来证明它:为了拥有一个函数,你必须调用这个函数,并且你必须返回一个答案。这需要固定的时间。即使余下的处理过程对较大的输入花费更少的时间,打印出答案(我们可以假设是单个位)至少需要常数时间。

如果根本不运行函数(NOOP)呢?或者使用固定值。这算吗?

正如已经指出的,除了null函数可能的例外,不可能有O(1/n)个函数,因为所花费的时间必须接近0。

当然,有一些算法,比如康拉德定义的算法,它们至少在某种意义上应该小于O(1)

def get_faster(list):
    how_long = 1/len(list)
    sleep(how_long)

If you want to investigate these algorithms, you should either define your own asymptotic measurement, or your own notion of time. For example, in the above algorithm, I could allow the use of a number of "free" operations a set amount of times. In the above algorithm, if I define t' by excluding the time for everything but the sleep, then t'=1/n, which is O(1/n). There are probably better examples, as the asymptotic behavior is trivial. In fact, I am sure that someone out there can come up with senses that give non-trivial results.

我不知道算法,但复杂度小于O(1)出现在随机算法中。实际上,o(1)(小o)小于o(1)这种复杂性通常出现在随机算法中。例如,如你所说,当某个事件的概率为1/n阶时,他们用o(1)表示。或者当他们想说某件事发生的概率很高时(例如1 - 1/n),他们用1 - o(1)表示。

O(1)仅仅表示“常数时间”。

当你给循环[1]添加一个早期退出时,你(在大O符号中)把一个O(1)算法变成了O(n)算法,但使它更快。

诀窍是一般情况下,常数时间算法是最好的,线性算法比指数算法好,但对于n很小的时候,指数算法可能更快。

1:假设这个例子的列表长度是静态的