Maximum Sum Subarray in Python

Given an input array that may contain both positive and negative integers, find the sum of continuous subarray of numbers which has the largest sum.

Example:

Input: nums = [-2, -5, 6, -2, -3, 1, 5, -6]
Output: 7
Explanation: sum([6, -2, -3, 1, 5]) = 7

Note:

Your algorithm should run in O(n^2) time and use O(1) extra space.

Problem Definition

The problem requires finding the maximum sum of a continuous subarray within a given array of integers, which may include both positive and negative numbers.

Input:

An array of integers, nums.

Output:

An integer representing the maximum sum of a continuous subarray.

Constraints:

The algorithm should run in O(n^2) time complexity.
The algorithm should use O(1) extra space.

Example:

Input: nums = [-2, -5, 6, -2, -3, 1, 5, -6]
Output: 7
Explanation: sum([6, -2, -3, 1, 5]) = 7

Understanding the Problem

The core challenge is to identify the subarray with the maximum sum. This problem is significant in various applications such as financial analysis, where one might want to find the period with the maximum profit.

Potential pitfalls include misunderstanding the requirement for a continuous subarray and not considering negative numbers correctly.

Approach

To solve this problem, we can start with a brute force approach and then optimize it.

Naive Solution

The naive solution involves checking all possible subarrays and calculating their sums. This can be achieved using two nested loops:

maxSum = nums[0]
for i in range(len(nums)):
    for j in range(i, len(nums)):
        currentSum = sum(nums[i:j+1])
        maxSum = max(maxSum, currentSum)
return maxSum

While this approach is straightforward, it is not optimal as it has a time complexity of O(n^3) due to the sum calculation inside the inner loop.

Optimized Solution

We can optimize the naive solution by maintaining a running sum of the current subarray. This reduces the time complexity to O(n^2):

maxSum = nums[0]
for i in range(len(nums)):
    currentSum = 0
    for j in range(i, len(nums)):
        currentSum += nums[j]
        maxSum = max(maxSum, currentSum)
return maxSum

This approach ensures that we only traverse the array twice, making it more efficient.

Algorithm

Here is a step-by-step breakdown of the optimized algorithm:

Initialize maxSum with the first element of the array.
Use a nested loop to iterate over all possible subarrays.
Maintain a running sum of the current subarray.
Update maxSum if the current subarray sum is greater.
Return maxSum after all iterations.

Code Implementation

def max_subarray_sum(nums):
    # Initialize maxSum with the first element of the array
    maxSum = nums[0]
    
    # Iterate over all possible subarrays
    for i in range(len(nums)):
        currentSum = 0
        for j in range(i, len(nums)):
            # Add the current element to the running sum
            currentSum += nums[j]
            # Update maxSum if the current sum is greater
            maxSum = max(maxSum, currentSum)
    
    return maxSum

# Example usage
nums = [-2, -5, 6, -2, -3, 1, 5, -6]
print(max_subarray_sum(nums))  # Output: 7

Complexity Analysis

The time complexity of the optimized solution is O(n^2) because of the two nested loops. The space complexity is O(1) as we are using only a few extra variables.

Edge Cases

Consider the following edge cases:

All negative numbers: The algorithm should return the maximum single element.
Single element array: The algorithm should return that element.
Mixed positive and negative numbers: The algorithm should correctly identify the subarray with the maximum sum.

Testing

To test the solution comprehensively, consider the following test cases:

def test_max_subarray_sum():
    assert max_subarray_sum([-2, -5, 6, -2, -3, 1, 5, -6]) == 7
    assert max_subarray_sum([-1, -2, -3, -4]) == -1
    assert max_subarray_sum([1, 2, 3, 4]) == 10
    assert max_subarray_sum([1]) == 1
    assert max_subarray_sum([-1, 2, 3, -4, 5, -6]) == 6

test_max_subarray_sum()

Thinking and Problem-Solving Tips

When approaching such problems, consider the following tips:

Break down the problem into smaller parts.
Start with a brute force solution and then optimize.
Understand the constraints and edge cases.
Practice similar problems to improve problem-solving skills.

Conclusion

Understanding and solving the maximum sum subarray problem is crucial for developing strong problem-solving skills. Practice and exploration of different approaches can help in mastering such problems.

Additional Resources

For further reading and practice, consider the following resources:

Maximum Sum Subarray in O(n^2) Time Complexity using Python