Went straight to the brute force O(n^2) approach and just...
Clarify the problem constraints (e.g., array size, string lengths, character set) and discuss trade-offs between brute-force and optimized approaches. Propose using bitmasks to represent character sets for efficient intersection checks, and outline a solution that balances time and space complexity.
Pro tip: Mention that for large inputs, a brute-force O(n^2) solution may be too slow, so leveraging bitmasks can reduce the constant factor and enable early termination. Also, discuss how to handle duplicate strings and whether the output should include duplicate pairs.
Ask about input size, character set, and output format. Confirm whether pairs are ordered (i < j) and if duplicates should be included.
Compare brute-force O(n^2 * L) with bitmask optimization. Explain how bitmasks represent character sets and enable O(1) intersection checks.
Outline steps: compute bitmask for each string, iterate over all pairs, check if (mask[i] & mask[j]) == 0, and collect indices.
State time complexity O(n^2) and space O(n) for bitmasks. Discuss potential optimizations like grouping by mask or early termination.
Consider empty strings, strings with all characters, and large n. Discuss how to avoid integer overflow if using bitmasks for large character sets.
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