I jumped straight to brute force, which works but felt embarrassing in hindsight.
Use a hash map to store the cumulative sum and its earliest index, then iterate through the array to find subarrays with zero sum by checking if the current cumulative sum has been seen before. For each match, print all subarrays from the stored index+1 to the current index. This approach runs in O(n) time on average and handles both positive and negative numbers.
Pro tip: Clarify whether to print all subarrays or just find them, and mention that using a hash map of lists can handle duplicate sums to print all subarrays efficiently. Also, discuss edge cases like empty array and zero elements.
Ask if the array can contain zeros, negatives, or duplicates, and whether to print all subarrays or just count them. Confirm output format and handling of empty array.
Describe how a zero-sum subarray corresponds to two equal cumulative sums. Use a hash map to store cumulative sum and its indices.
Initialize sum=0 and map with {0: [-1]}. Iterate through array, update sum, and for each previous index in map[sum], print subarray from index+1 to current index. Then add current index to map[sum].
State time complexity O(n) on average and space O(n) for the hash map. Mention that worst-case time can be O(n^2) if many subarrays are printed, but that's inherent to output size.
Run through a small example like [1, -1, 2, -2] to show how subarrays are found. Also test edge cases like [0] and empty array.
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