下面是另一个简单的概念，可以解决O(N)中的问题。

        int[] arr = new int[] {1, 2, 3, 4, 5};
        int[] outArray = new int[arr.length]; 
        for(int i=0;i<arr.length;i++){
            int res=Arrays.stream(arr).reduce(1, (a, b) -> a * b);
            outArray[i] = res/arr[i];
        }
        System.out.println(Arrays.toString(outArray));

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]

{-
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)

下面是一个C实现 O(n)时间复杂度。 输入

#include<stdio.h>
int main()
{
    int x;
    printf("Enter The Size of Array : ");
    scanf("%d",&x);
    int array[x-1],i ;
    printf("Enter The Value of Array : \n");
      for( i = 0 ; i <= x-1 ; i++)
      {
          printf("Array[%d] = ",i);
          scanf("%d",&array[i]);
      }
    int left[x-1] , right[x-1];
    left[0] = 1 ;
    right[x-1] = 1 ;
      for( i = 1 ; i <= x-1 ; i++)
      {
          left[i] = left[i-1] * array[i-1];
      }
    printf("\nThis is Multiplication of array[i-1] and left[i-1]\n");
      for( i = 0 ; i <= x-1 ; i++)
      {
        printf("Array[%d] = %d , Left[%d] = %d\n",i,array[i],i,left[i]);
      }
      for( i = x-2 ; i >= 0 ; i--)
      {
          right[i] = right[i+1] * array[i+1];
      }
   printf("\nThis is Multiplication of array[i+1] and right[i+1]\n");
      for( i = 0 ; i <= x-1 ; i++)
      {
        printf("Array[%d] = %d , Right[%d] = %d\n",i,array[i],i,right[i]);
      }
    printf("\nThis is Multiplication of Right[i] * Left[i]\n");
      for( i = 0 ; i <= x-1 ; i++)
      {
          printf("Right[%d] * left[%d] = %d * %d = %d\n",i,i,right[i],left[i],right[i]*left[i]);
      }
    return 0 ;
}

输出

    Enter The Size of Array : 5
    Enter The Value of Array :
    Array[0] = 1
    Array[1] = 2
    Array[2] = 3
    Array[3] = 4
    Array[4] = 5

    This is Multiplication of array[i-1] and left[i-1]
    Array[0] = 1 , Left[0] = 1
    Array[1] = 2 , Left[1] = 1
    Array[2] = 3 , Left[2] = 2
    Array[3] = 4 , Left[3] = 6
    Array[4] = 5 , Left[4] = 24

    This is Multiplication of array[i+1] and right[i+1]
    Array[0] = 1 , Right[0] = 120
    Array[1] = 2 , Right[1] = 60
    Array[2] = 3 , Right[2] = 20
    Array[3] = 4 , Right[3] = 5
    Array[4] = 5 , Right[4] = 1

    This is Multiplication of Right[i] * Left[i]
    Right[0] * left[0] = 120 * 1 = 120
    Right[1] * left[1] = 60 * 1 = 60
    Right[2] * left[2] = 20 * 2 = 40
    Right[3] * left[3] = 5 * 6 = 30
    Right[4] * left[4] = 1 * 24 = 24

    Process returned 0 (0x0)   execution time : 6.548 s
    Press any key to continue.

下面是我尝试用Java来解决这个问题。抱歉格式不规范，但代码有很多重复，这是我能做的最好的，使它可读。

import java.util.Arrays;

public class Products {
    static int[] products(int... nums) {
        final int N = nums.length;
        int[] prods = new int[N];
        Arrays.fill(prods, 1);
        for (int
           i = 0, pi = 1    ,  j = N-1, pj = 1  ;
           (i < N)         && (j >= 0)          ;
           pi *= nums[i++]  ,  pj *= nums[j--]  )
        {
           prods[i] *= pi   ;  prods[j] *= pj   ;
        }
        return prods;
    }
    public static void main(String[] args) {
        System.out.println(
            Arrays.toString(products(1, 2, 3, 4, 5))
        ); // prints "[120, 60, 40, 30, 24]"
    }
}

循环不变量为pi = nums[0] * nums[1] *..* nums[N-2] *..num [j + 1]。左边的i部分是“前缀”逻辑，右边的j部分是“后缀”逻辑。

递归一行程序

Jasmeet给出了一个(漂亮的!)递归解;我把它变成了这样(可怕!)Java一行程序。它进行就地修改，堆栈中有O(N)个临时空间。

static int multiply(int[] nums, int p, int n) {
    return (n == nums.length) ? 1
      : nums[n] * (p = multiply(nums, nums[n] * (nums[n] = p), n + 1))
          + 0*(nums[n] *= p);
}

int[] arr = {1,2,3,4,5};
multiply(arr, 1, 0);
System.out.println(Arrays.toString(arr));
// prints "[120, 60, 40, 30, 24]"

左旅行->右和保持保存产品。称之为过去。- > O (n) 旅行右->左保持产品。称之为未来。- > O (n) 结果[i] =过去[i-1] *将来[i+1] -> O(n) 过去[-1]= 1;和未来(n + 1) = 1;

O(n)