How do you solve Minimum Positive Sum Subarray on LeetCode?

Minimum Positive Sum Subarray (LeetCode #3364) can be solved using Brute Force. The optimal approach is Brute Force with O(n²) time complexity and O(1) space complexity. Key insight: The brute-force approach involves checking every possible subarray of lengths between l and r. This means calculating the sum for each subarray and keeping track of the smallest positive sum found.

What algorithm or data structure is used to solve Minimum Positive Sum Subarray ?

Minimum Positive Sum Subarray uses the following concepts: Array, Sliding Window, Prefix Sum. The recommended patterns to study are: .

#3364

Minimum Positive Sum Subarray

Easy

ArraySliding WindowPrefix Sum

LeetCode ↗

Approaches

💡

Intuition

Time O(n²)Space O(1)

The brute-force approach involves checking every possible subarray of lengths between l and r. This means calculating the sum for each subarray and keeping track of the smallest positive sum found.

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Algorithm

3 steps

1Step 1: Iterate through the array with a starting index.
2Step 2: For each starting index, iterate through possible ending indices to form subarrays of lengths from l to r.
3Step 3: Calculate the sum of each subarray and check if it's positive and smaller than the current minimum positive sum.

solution.py8 lines

1def min_positive_sum_subarray(nums, l, r):
2    min_sum = float('inf')
3    for i in range(len(nums)):
4        for j in range(i + l - 1, min(i + r, len(nums)) + 1):
5            subarray_sum = sum(nums[i:j])
6            if subarray_sum > 0:
7                min_sum = min(min_sum, subarray_sum)
8    return min_sum if min_sum != float('inf') else -1

ℹ

Complexity note: The time complexity is O(n²) because we have two nested loops iterating through the array, and for each subarray, we calculate the sum which can take O(n) time in the worst case.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.