LeetCode-in-All

53. Maximum Subarray

Easy

Given an integer array nums, find the contiguous subarray (containing at least one number) which has the largest sum and return its sum.

A subarray is a contiguous part of an array.

Example 1:

Input: nums = [-2,1,-3,4,-1,2,1,-5,4]

Output: 6

Explanation: [4,-1,2,1] has the largest sum = 6.

Example 2:

Input: nums = [1]

Output: 1

Example 3:

Input: nums = [5,4,-1,7,8]

Output: 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.

To solve the Maximum Subarray problem, we can use the Kadane’s algorithm, which efficiently finds the maximum subarray sum in linear time. Here are the steps to solve the task:

1. Initialize Variables:
   • Initialize two variables max_sum and current_sum to keep track of the maximum sum found so far and the sum of the current subarray, respectively.
   • Set max_sum and current_sum to the value of the first element in the array nums.
2. Iterate Through the Array:
   • Start iterating through the array nums from the second element (index 1).
   • For each element num in nums, do the following:
     • Update current_sum to be the maximum of either num or current_sum + num. This step ensures that we consider either starting a new subarray or extending the current subarray.
     • Update max_sum to be the maximum of either max_sum or current_sum. This step ensures that we keep track of the maximum subarray sum found so far.
3. Return the Result:
   • After iterating through the entire array, max_sum will contain the maximum subarray sum.
   • Return max_sum as the result.

Python Implementation:

class Solution:
    def maxSubArray(self, nums: List[int]) -> int:
        # Initialize variables
        max_sum = current_sum = nums[0]

        # Iterate through the array
        for num in nums[1:]:
            current_sum = max(num, current_sum + num)
            max_sum = max(max_sum, current_sum)

        return max_sum

# Example Usage:
solution = Solution()
nums1 = [-2, 1, -3, 4, -1, 2, 1, -5, 4]
print(solution.maxSubArray(nums1))  # Output: 6

nums2 = [1]
print(solution.maxSubArray(nums2))  # Output: 1

nums3 = [5, 4, -1, 7, 8]
print(solution.maxSubArray(nums3))  # Output: 23

This algorithm has a time complexity of O(n), where n is the length of the input array nums. Therefore, it efficiently finds the maximum subarray sum.

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