Given an integer array nums
, return the number of range sums that lie in [lower, upper]
inclusive. 數組
Range sum S(i, j)
is defined as the sum of the elements in nums
between indices i
and j
(i
≤ j
), inclusive.post
Note: spa
A naive algorithm of O(n2) is trivial. You MUST do better than that.指針
Example: code
Given nums = [-2, 5, -1]
, lower = -2
, upper = 2
, Return 3
. The three ranges are : [0, 0]
, [2, 2]
, [0, 2]
and their respective sums are: -2, -1, 2
.對象
這道題最直觀的一個想法就是枚舉出全部的子數組,而後檢查他們是否在要求的取值範圍內,這種方法的時間複雜度是O(n^2)的,顯然會超時。blog
看到這種題目最容易想到的是什麼呢?Two Pointers!對,可是在這道題上僅僅使用Two Pointers確定是不夠的,在Two Pointers的思想基礎上,融合歸併排序,就能找到一個比較好的解決方案。排序
這裏咱們的排序對象是前綴求和數組,在歸併排序的合併階段,咱們有左數組和右數組,且左和右數組都是排好序的,因此咱們能夠用i遍歷左數組,j,k兩個指針分別取在右數組搜索,使得:three
那麼此時,咱們就找到了j-k個符合要求的子數組。element
因爲左右數組都是排好序的,因此當i遞增以後,j和k的位置不用從頭開始掃描。
最後還有一點須要注意的就是,爲了防止溢出,咱們的vector容納的是long long型元素。
class Solution { public: int countRangeSum(vector<int>& nums, int lower, int upper) { int n = nums.size(); if (n <= 0) { return 0; } vector<long long> sums(n + 1, 0); for (int i = 0; i < n; ++i) { sums[i+1] = sums[i] + nums[i]; } return merge(sums, 0, n, lower, upper); } int merge(vector<long long>& sums, int start, int end, int lower, int upper) { if (start >= end) { return 0; } int mid = start + (end - start) / 2; int count = merge(sums, start, mid, lower, upper) + merge(sums, mid + 1, end, lower, upper); vector<long long> tmp(end - start + 1, 0); int j = mid + 1, k = mid + 1, t = mid + 1, i = start, r = 0; for (; i <= mid; ++i, ++r) { while (j <= end && sums[j] - sums[i] <= upper) ++j; while (k <= end && sums[k] - sums[i] < lower) ++k; count += j - k; while (t <= end && sums[t] <= sums[i]) tmp[r++] = sums[t++]; tmp[r] = sums[i]; } for (int i = 0; i < r; ++i) { sums[start + i] = tmp[i]; } return count; } };