leetcode53
作者:互联网
题目:
Find the contiguous subarray within an array (containing at least one number) which has the largest sum.
For example, given the array [−2,1,−3,4,−1,2,1,−5,4],
the contiguous subarray [4,−1,2,1] has the largest sum = 6.
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More practice:
If you have figured out the O(n) solution, try coding another solution using the divide and conquer approach, which is more subtle.
解法一:
这道题让我们求最大子数组之和,并且要我们用两种方法来解,分别是O(n)的解法,还有用分治法Divide and Conquer Approach,这个解法的时间复杂度是O(nlgn),那我们就先来看O(n)的解法,定义两个变量res和curSum,其中res保存最终要返回的结果,即最大的子数组之和,curSum初始值为0,每遍历一个数字num,比较curSum + num和num中的较大值存入curSum,然后再把res和curSum中的较大值存入res,以此类推直到遍历完整个数组,可得到最大子数组的值存在res中。
class Solution {
public:
int maxSubArray(vector<int>& nums) {
int res = INT_MIN, curSum = 0;
for (int num : nums) {
curSum = max(curSum + num, num);
res = max(res, curSum);
}
return res;
}
};
解法二:
题目还要求我们用分治法Divide and Conquer Approach来解,这个分治法的思想就类似于二分搜索法,我们需要把数组一分为二,分别找出左边和右边的最大子数组之和,然后还要从中间开始向左右分别扫描,求出的最大值分别和左右两边得出的最大值相比较取最大的那一个。
class Solution {
public:
int maxSubArray(vector<int>& nums) {
if (nums.empty()) return 0;
return helper(nums, 0, (int)nums.size() - 1);
}
int helper(vector<int>& nums, int left, int right) {
if (left >= right) return nums[left];
int mid = left + (right - left) / 2;
int lmax = helper(nums, left, mid - 1);
int rmax = helper(nums, mid + 1, right);
int mmax = nums[mid], t = mmax;
for (int i = mid - 1; i >= left; --i) {
t += nums[i];
mmax = max(mmax, t);
}
t = mmax;
for (int i = mid + 1; i <= right; ++i) {
t += nums[i];
mmax = max(mmax, t);
}
return max(mmax, max(lmax, rmax));
}
};
标签:nums,int,res,leetcode53,num,curSum,left 来源: https://blog.csdn.net/hua111hua/article/details/87926224