根据Billz的回答——抱歉我不能评论，但这里是一个正确处理列表中重复项的scala版本，可能是O(n):

val list1 = List(1, 7, 3, 3, 4, 4)
val view = list1.view.zipWithIndex map { x => list1.view.patch(x._2, Nil, 1).reduceLeft(_*_)}
view.force

返回:

List(1008, 144, 336, 336, 252, 252)

下面是一个使用c#的函数式示例:

            Func<long>[] backwards = new Func<long>[input.Length];
            Func<long>[] forwards = new Func<long>[input.Length];

            for (int i = 0; i < input.Length; ++i)
            {
                var localIndex = i;
                backwards[i] = () => (localIndex > 0 ? backwards[localIndex - 1]() : 1) * input[localIndex];
                forwards[i] = () => (localIndex < input.Length - 1 ? forwards[localIndex + 1]() : 1) * input[localIndex];
            }

            var output = new long[input.Length];
            for (int i = 0; i < input.Length; ++i)
            {
                if (0 == i)
                {
                    output[i] = forwards[i + 1]();
                }
                else if (input.Length - 1 == i)
                {
                    output[i] = backwards[i - 1]();
                }
                else
                {
                    output[i] = forwards[i + 1]() * backwards[i - 1]();
                }
            }

我不完全确定这是O(n)，因为所创建的Funcs是半递归的，但我的测试似乎表明它在时间上是O(n)。

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];

这是ptyhon版本

  # This solution use O(n) time and O(n) space
  def productExceptSelf(self, nums):
    """
    :type nums: List[int]
    :rtype: List[int]
    """
    N = len(nums)
    if N == 0: return

    # Initialzie list of 1, size N
    l_prods, r_prods = [1]*N, [1]*N

    for i in range(1, N):
      l_prods[i] = l_prods[i-1] * nums[i-1]

    for i in reversed(range(N-1)):
      r_prods[i] = r_prods[i+1] * nums[i+1]

    result = [x*y for x,y in zip(l_prods,r_prods)]
    return result

  # This solution use O(n) time and O(1) space
  def productExceptSelfSpaceOptimized(self, nums):
    """
    :type nums: List[int]
    :rtype: List[int]
    """
    N = len(nums)
    if N == 0: return

    # Initialzie list of 1, size N
    result = [1]*N

    for i in range(1, N):
      result[i] = result[i-1] * nums[i-1]

    r_prod = 1
    for i in reversed(range(N)):
      result[i] *= r_prod
      r_prod *= nums[i]

    return result

import java.util.Arrays;

public class Pratik
{
    public static void main(String[] args)
    {
        int[] array = {2, 3, 4, 5, 6};      //  OUTPUT: 360  240  180  144  120
        int[] products = new int[array.length];
        arrayProduct(array, products);
        System.out.println(Arrays.toString(products));
    }

    public static void arrayProduct(int array[], int products[])
    {
        double sum = 0, EPSILON = 1e-9;

        for(int i = 0; i < array.length; i++)
            sum += Math.log(array[i]);

        for(int i = 0; i < array.length; i++)
            products[i] = (int) (EPSILON + Math.exp(sum - Math.log(array[i])));
    }
}

输出:

[360, 240, 180, 144, 120]

时间复杂度:O(n) 空间复杂度:O(1)

我有一个O(n)空间和O(n²)时间复杂度的解，如下所示，

public static int[] findEachElementAsProduct1(final int[] arr) {

        int len = arr.length;

//        int[] product = new int[len];
//        Arrays.fill(product, 1);

        int[] product = IntStream.generate(() -> 1).limit(len).toArray();


        for (int i = 0; i < len; i++) {

            for (int j = 0; j < len; j++) {

                if (i == j) {
                    continue;
                }

                product[i] *= arr[j];
            }
        }

        return product;
    }