一、题目说明
题目是53. Maximum Subarray,求最长连续子序列最大和。难度是Easy!
二、我的解答
Easy的题目,居然没做出来。
后来看了用dp方法,其中dp[i]表示以第i个元素结尾的最大和。
dp[i] = nums[i] > nums[i]+dp[i-1] ? nums[i] : nums[i]+dp[i-1];
然后求出最大的dp即可。知道思路,实现非常简单,问题是没有往动态规划上面去想。
#include<iostream>
#include<vector>
using namespace std;
class Solution{
public:
int maxSubArray(vector<int>& nums) {
int len=nums.size();
vector<int> dp;
dp.resize(len);
//以第i个元素结尾的最大和
dp[0] = nums[0];
int max = dp[0];
for(int i=1;i<len;i++){
dp[i] = nums[i] > nums[i]+dp[i-1] ? nums[i] : nums[i]+dp[i-1];
max = max > dp[i] ? max : dp[i];
}
return max;
}
};
int main(){
Solution s;
vector<int> m;
m = {-2,1,-3,4,-1,2,1,-5,4};
cout<<(6==s.maxSubArray(m))<<"
";
m = {-2,1,-3};
cout<<(1==s.maxSubArray(m))<<"
";
m = {1};
cout<<(1==s.maxSubArray(m))<<"
";
m = {-2,-1};
cout<<(-1==s.maxSubArray(m))<<"
";
return 0;
}
性能居然还不错,空间复杂的可以优化,dp复用nums,还是算了,可读性不好:
Runtime: 4 ms, faster than 98.55% of C++ online submissions for Maximum Subarray.
Memory Usage: 9.4 MB, less than 17.65% of C++ online submissions for Maximum Subarray.
三、优化
题目说让用分治算法,分而治之。我想想,应该是类似“二分查找”。不会,看了大神的实现:
class Solution{
public:
//divide & conquer approach
int maxSubArray(vector<int>& nums) {
return maxSubArrayPart(nums,0,nums.size()-1);
}
private:
int maxSubArrayPart(vector<int>& nums,int left,int right){
if(left==right){
return nums[left];
}
int mid = (left+right) / 2;
return max(maxSubArrayPart(nums,left,mid),
max(maxSubArrayPart(nums,mid+1,right),maxSubArrayAll(nums,left,mid,right)));
}
//左右两边求和
int maxSubArrayAll(vector<int>& nums,int left,int mid,int right){
int leftSum = INT_MIN;
int sum=0;
for(int i=mid;i>=left;i--){
sum += nums[i];
if(sum>leftSum) leftSum=sum;
}
sum=0;
int rightSum= INT_MIN;
for(int i=mid+1;i<=right;i++){
sum += nums[i];
if(sum>rightSum) rightSum=sum;
}
return leftSum+rightSum;
}
};
性能:
Runtime: 8 ms, faster than 74.25% of C++ online submissions for Maximum Subarray.
Memory Usage: 9.4 MB, less than 33.33% of C++ online submissions for Maximum Subarray.