int[] b = new int[] { 1, 2, 3, 4, 5 };            
int j;
for(int i=0;i<b.Length;i++)
{
  int prod = 1;
  int s = b[i];
  for(j=i;j<b.Length-1;j++)
  {
    prod = prod * b[j + 1];
  }
int pos = i;    
while(pos!=-1)
{
  pos--;
  if(pos!=-1)
     prod = prod * b[pos];                    
}
Console.WriteLine("\n Output is {0}",prod);
}

{-
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Ruby中的一行程序解决方案。

全国矿工工会。映射{|n| (num - [n]).inject(:*)}

还有一个解决方案，使用除法。有两次遍历。 把所有元素相乘，然后除以每个元素。

    int[] arr1 = { 1, 2, 3, 4, 5 };
    int[] product = new int[arr1.Length];              

    for (int i = 0; i < arr1.Length; i++)
    {
        for (int j = 0; j < product.Length; j++)
        {
            if (i != j)
            {
                product[j] = product[j] == 0 ? arr1[i] : product[j] * arr1[i];
            }
        }
    }

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)。