I went straight to two pointers and it clicked pretty well.
Start by clarifying the problem constraints (e.g., sorted arrays, duplicates handling) and then propose a two-pointer approach that leverages the sorted order to achieve O(m+n) time and O(1) extra space. Compare this with a hash set approach that takes O(m+n) time but O(min(m,n)) space, and discuss trade-offs. Walk through an example to illustrate duplicate handling.
Pro tip: Mention that the two-pointer approach is optimal for sorted arrays and can be extended to handle duplicates by advancing pointers past equal elements, showing you consider edge cases. Also, note that if one array is much smaller, binary search on the larger array could be more efficient, demonstrating awareness of trade-offs.
Ask about input sizes, whether arrays can be empty, if duplicates should be included once or multiple times, and if the output needs to be sorted. This shows attention to detail and avoids assumptions.
Explain that since arrays are sorted, use two pointers starting at the beginning of each array. Compare elements and move the pointer of the smaller element forward; if equal, add to result and move both pointers, handling duplicates by skipping subsequent equal elements if needed.
State that the two-pointer approach runs in O(m+n) time and O(1) extra space (excluding output). Contrast with hash set approach which also runs in O(m+n) time but uses O(min(m,n)) space, making two-pointer more space-efficient.
Explain that if duplicates should appear only once in the output, when a match is found, advance both pointers past all duplicates of that value. If duplicates should appear as many times as they occur in both arrays, then simply add the common element and advance both pointers by one.
Mention that if one array is significantly smaller, binary search for each element of the smaller array in the larger array could be O(n log m) which might be better. Also, note that hash set is simpler but uses extra space, and two-pointer is optimal for sorted arrays.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Acknowledge that the optimal approach depends on the relative sizes of the arrays and the problem constraints. Discuss how to adapt the algorithm to minimize time and space complexity, such as iterating over the smaller array and using a hash set for the larger one, or switching to a sort-based approach if one array is tiny. Emphasize the trade-offs and justify your choice based on the specific scenario.
Pro tip: Demonstrate awareness of real-world constraints by mentioning that if one array is extremely small, a brute-force nested loop might be acceptable, but if it's moderately smaller, a hash-based approach is better. Also, note that Adobe values practical optimization and clear reasoning over theoretical perfection.
Restate the problem and ask about the expected sizes of the arrays, the range of values, and any memory or time constraints. This shows you consider the context before optimizing.
Explain how the relative sizes affect the time and space complexity of your original approach. For example, if one array is much smaller, iterating over it and doing lookups in a hash set of the larger array reduces time to O(min(m,n)).
Suggest specific adaptations: if one array is tiny, a brute-force O(m*n) might be fine; if one is moderately smaller, use a hash set on the larger array and iterate the smaller; if both are large but one is sorted, use two pointers.
Compare the adapted approaches in terms of time, space, and code complexity. Mention that the best choice depends on the actual sizes and whether the arrays are sorted or have other properties.
Summarize your preferred approach for the given scenario, and note that you would confirm with the interviewer or test with sample data if possible.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.