最近有人问我这个问题，虽然我不能得到O(N)，但我有一个不同的方法(不幸的是O(N²))，但我想无论如何都要分享。

首先转换为列表<Integer>。

遍历原始数组array.length()次。

使用while循环乘下一组所需的数字:

while (temp < list.size() - 1) {
    res *= list.get(temp);
    temp++;
}

然后将res添加到一个新数组(当然，您已经在前面声明了)，然后将数组[i]的值添加到List，依此类推。

我知道这不会有太大的用处，但这是我在面试的压力下想到的:)

    int[] array = new int[]{1, 2, 3, 4, 5};
    List<Integer> list = Arrays.stream(array).boxed().collect(Collectors.toList());
    int[] newarray = new int[array.length];
    int res = 1;
    for (int i = 0; i < array.length; i++) {
        int temp = i;
        while (temp < list.size() - 1) {
            res *= list.get(temp);
            temp++;
        }
        newarray[i] = res;
        list.add(array[i]);
        res = 1;
    }

输出:[24,120,60,40,30]

以下是线性O(n)时间内的简单Scala版本:

def getProductEff(in:Seq[Int]):Seq[Int] = {

   //create a list which has product of every element to the left of this element
   val fromLeft = in.foldLeft((1, Seq.empty[Int]))((ac, i) => (i * ac._1, ac._2 :+ ac._1))._2

   //create a list which has product of every element to the right of this element, which is the same as the previous step but in reverse
   val fromRight = in.reverse.foldLeft((1,Seq.empty[Int]))((ac,i) => (i * ac._1,ac._2 :+ ac._1))._2.reverse

   //merge the two list by product at index
   in.indices.map(i => fromLeft(i) * fromRight(i))

}

这是可行的，因为本质上答案是一个数组，它是左右所有元素的乘积。

这是O(n²)但f#太漂亮了

List.fold (fun seed i -> List.mapi (fun j x -> if i=j+1 then x else x*i) seed) 
          [1;1;1;1;1]
          [1..5]

{-
Recursive solution using sqrt(n) subsets. Runs in O(n).

Recursively computes the solution on sqrt(n) subsets of size sqrt(n). 
Then recurses on the product sum of each subset.
Then for each element in each subset, it computes the product with
the product sum of all other products.
Then flattens all subsets.

Recurrence on the run time is T(n) = sqrt(n)*T(sqrt(n)) + T(sqrt(n)) + n

Suppose that T(n) ≤ cn in O(n).

T(n) = sqrt(n)*T(sqrt(n)) + T(sqrt(n)) + n
    ≤ sqrt(n)*c*sqrt(n) + c*sqrt(n) + n
    ≤ c*n + c*sqrt(n) + n
    ≤ (2c+1)*n
    ∈ O(n)

Note that ceiling(sqrt(n)) can be computed using a binary search 
and O(logn) iterations, if the sqrt instruction is not permitted.
-}

otherProducts [] = []
otherProducts [x] = [1]
otherProducts [x,y] = [y,x]
otherProducts a = foldl' (++) [] $ zipWith (\s p -> map (*p) s) solvedSubsets subsetOtherProducts
    where 
      n = length a

      -- Subset size. Require that 1 < s < n.
      s = ceiling $ sqrt $ fromIntegral n

      solvedSubsets = map otherProducts subsets
      subsetOtherProducts = otherProducts $ map product subsets

      subsets = reverse $ loop a []
          where loop [] acc = acc
                loop a acc = loop (drop s a) ((take s a):acc)

我们可以先从列表中排除nums[j](其中j != i)，然后得到其余部分的乘积;下面是python解决这个难题的方法:

from functools import reduce
def products(nums):
    return [ reduce(lambda x,y: x * y, nums[:i] + nums[i+1:]) for i in range(len(nums)) ]
print(products([1, 2, 3, 4, 5]))

[out]
[120, 60, 40, 30, 24]

O(n)时间的简洁解:

对于每个元素，计算在它之前出现的所有元素的乘积，并将其存储在数组“pre”中。 对于每个元素，计算该元素之后所有元素的乘积，并将其存储在数组“post”中 为元素i创建一个最终数组result， 结果[i] = pre[i-1]*post[i+1];