Given an integer array nums, find the subarray with the largest sum, and return its sum.
Example 1:
Input: nums = [-2,1,-3,4,-1,2,1,-5,4]
Output: 6
Explanation: The subarray [4,-1,2,1] has the largest sum 6.
Example 2:
Input: nums = [1]
Output: 1
Explanation: The subarray [1] has the largest sum 1.
Example 3:
Input: nums = [5,4,-1,7,8]
Output: 23
Explanation: The subarray [5,4,-1,7,8] has the largest sum 23.
Constraints:
1 <= nums.length <= 105
-104 <= nums[i] <= 104
Follow up: If you have figured out the O(n) solution, try coding another solution using the divide and conquer approach, which is more subtle.
Solutions
Solution 1: Dynamic Programming
We define \(f[i]\) to represent the maximum sum of a contiguous subarray ending at element \(\textit{nums}[i]\). Initially, \(f[0] = \textit{nums}[0]\). The final answer we seek is \(\max_{0 \leq i < n} f[i]\).
Consider \(f[i]\) for \(i \geq 1\). Its state transition equation is:
Since \(f[i]\) is only related to \(f[i - 1]\), we can use a single variable \(f\) to maintain the current value of \(f[i]\) and perform the state transition. The answer is \(\max_{0 \leq i < n} f\).
The time complexity is \(O(n)\), where \(n\) is the length of the array \(\textit{nums}\). The space complexity is \(O(1)\).