Description
Given an integer array nums
, return an array answer
such that answer[i]
is equal to the product of all the elements of nums
except nums[i]
.
The product of any prefix or suffix of nums
is guaranteed to fit in a 32-bit integer.
You must write an algorithm that runs in O(n)
time and without using the division operation.
Example 1:
Input: nums = [1,2,3,4] Output: [24,12,8,6]
Example 2:
Input: nums = [-1,1,0,-3,3] Output: [0,0,9,0,0]
Constraints:
2 <= nums.length <= 105
-30 <= nums[i] <= 30
- The product of any prefix or suffix of
nums
is guaranteed to fit in a 32-bit integer.
Follow up: Can you solve the problem in O(1)
extra space complexity? (The output array does not count as extra space for space complexity analysis.)
Code
Time, Space
class Solution {
public:
vector<int> productExceptSelf(vector<int>& nums) {
vector<int> prefixSum(nums);
vector<int> postfixSum(nums);
for(int i = 1; i < prefixSum.size(); i++) {
prefixSum[i] *= prefixSum[i - 1];
}
for(int j = postfixSum.size() - 2; j >= 0; j--) {
postfixSum[j] *= postfixSum[j + 1];
}
int n = nums.size();
vector<int> answer(n);
for(int i = 0; i < n; i++) {
int pre = i - 1 >= 0 ? prefixSum[i - 1] : 1;
int post = i + 1 < n ? postfixSum[i + 1] : 1;
answer[i] = pre * post;
}
return answer;
}
};
Time, Space
思路和 Space 的解法相同,都是要計算由右至左,以及由左至右的 prefix product,只是 do it inplace。
class Solution {
public:
vector<int> productExceptSelf(vector<int>& nums) {
int n = nums.size();
vector<int> res(n, 0);
res[0] = 1;
for(int i = 1; i < n; i++) {
res[i] = res[i - 1] * nums[i - 1];
}
int r = 1;
for(int i = n - 1; i >= 0; i--) {
res[i] *= r;
r *= nums[i];
}
return res;
}
};