Longest Subarray With Sum K

The problem of finding the length of the longest subarray with a sum equal to k can be solved using two approaches. The naive approach involves iterating over all possible subarrays using nested loops to calculate their sums, and if the sum matches k, the length of the subarray is checked and updated. This approach has a time complexity of O(n^2), making it inefficient for large arrays.

A more efficient approach uses a hash map and prefix sums, achieving O(n) time complexity. In this method, the prefix sum is calculated iteratively, and for each prefix sum, the algorithm checks if prefixSum−k exists in the hash map. If it does, the subarray length is calculated and compared to find the maximum. This approach ensures an optimal solution while maintaining O(n) space complexity by storing the first occurrence of each prefix sum in the hash map.

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