多基因润滑剂方法的一个解释是:

诀窍是构造数组(在4个元素的情况下):

{              1,         a[0],    a[0]*a[1],    a[0]*a[1]*a[2],  }
{ a[1]*a[2]*a[3],    a[2]*a[3],         a[3],                 1,  }

这两种方法都可以在O(n)中分别从左右边开始。

然后，将两个数组逐个元素相乘，得到所需的结果。

我的代码看起来是这样的:

int a[N] // This is the input
int products_below[N];
int p = 1;
for (int i = 0; i < N; ++i) {
    products_below[i] = p;
    p *= a[i];
}

int products_above[N];
p = 1;
for (int i = N - 1; i >= 0; --i) {
    products_above[i] = p;
    p *= a[i];
}

int products[N]; // This is the result
for (int i = 0; i < N; ++i) {
    products[i] = products_below[i] * products_above[i];
}

如果你也需要空间中的解是O(1)，你可以这样做(在我看来不太清楚):

int a[N] // This is the input
int products[N];

// Get the products below the current index
int p = 1;
for (int i = 0; i < N; ++i) {
    products[i] = p;
    p *= a[i];
}

// Get the products above the current index
p = 1;
for (int i = N - 1; i >= 0; --i) {
    products[i] *= p;
    p *= a[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)。

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

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。

我用Javascript想出了两个解决方案，一个有除法，一个没有

//不除法 函数methodOne(arr) { 加勒比海盗。Map (item => { 加勒比海盗。Reduce ((result, num) => { If (num !== item) { 结果=结果* num; ｝ 返回结果; }, 1) })； ｝ //使用除法 函数methodTwo(arr) { Var mul = arr。Reduce ((result, num) => { 结果=结果* num; 返回结果; }, 1) 加勒比海盗。Map (item => mul/item); ｝ console.log(methodOne([1,2,3,4,5])); console.log(methodTwo([1,2,3,4,5]));

{-
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)