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Description
Given an array of n positive integers and a positive integer s, find the minimal length of a contiguous subarray of which the sum ≥ s. If there isn't one, return 0 instead.
**Example: **
Input: s = 7, nums = [2,3,1,2,4,3]
Output: 2
Explanation: the subarray [4,3] has the minimal length under the problem constraint.
Follow up:
If you have figured out the O(n) solution, try coding another solution of which the time complexity is O(n log n).
典型的使用“滑动窗口”法。用一个左指针和右指针作为界限,两者之间的区域称为滑动窗口,一般用于连续线性区间且某组子区间所有值满足条件的题型。跟双指针(对撞指针,快慢指针)不同的是,双指针解决的是其所指的两个值满足条件的问题,而不是两个以上的区间问题。
定义一个滑动窗口,如果窗口内的和小于s,右指针加一;否则比较最小长度,左指针加一。
class Solution {
public int minSubArrayLen(int s, int[] nums) {
int n = nums.length;
if (n == 0) return 0;
int left = 0, right = 0;
int sum = nums[0], minLen = n + 1;
while (left < n && right < n) {
if (sum < s) {
++right;
if (right < n) sum += nums[right];
}
else {
minLen = Math.min(minLen, right - left + 1);
sum -= nums[left++];
}
}
return minLen == n + 1 ? 0 : minLen;
}
}时间复杂度为O(n)。
Follow-up中建议尝试O(nlogn)的方法,显然需要改进二分查找法。看了大佬的讲解,思路如下:声明一个长度为nums.length + 1的sums数组,sums[i]代表数组nums[0]-nums[i - 1]的和,这样sums[j] - sums[i]就是区间[i, j]的子数组和。遍历数组,定位左边界,从右边子数组中查找利用二分查找右边界。二分查找最后会定位到某个值,该值就是大于等于sums[i] + s的临界值。
class Solution {
private int minLen = Integer.MAX_VALUE;
public int minSubArrayLen(int s, int[] nums) {
int n = nums.length;
if (n == 0) return 0;
int[] sums = new int[n + 1];
for (int i = 1; i < n + 1; ++i)
sums[i] = sums[i - 1] + nums[i - 1];
for (int i = 0; i < n + 1; ++i) {
int right = getSubArrRight(sums, s + sums[i], i + 1, n);
if (right == n + 1) break;
minLen = Math.min(minLen, right - i);
}
return minLen == Integer.MAX_VALUE ? 0 : minLen;
}
public int getSubArrRight(int[] sums, int target, int left, int right) {
while (left <= right) {
int mid = left + (right - left) / 2;
if (sums[mid] >= target) right = mid - 1;
else left = mid + 1;
}
return left;
}
}二分查找可以写函数也可以不写函数。
参考资料:
其他滑动窗口问题:
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