I got the naive O(n^2 * L) thing out pretty fast, nested loops, check if word A is in word B, whatever.
Start by clearly stating the naive O(n^2 * L^2) approach where you compare every pair of words using substring checks. Then propose an optimized approach using a trie or sorting by length to reduce redundant comparisons, and analyze the time and space complexity of each. Finally, implement the optimized version with clean, efficient code and discuss trade-offs.
Pro tip: Mention that you would clarify constraints (e.g., word length, list size, case sensitivity) before diving in, and consider edge cases like duplicate words or empty strings. This shows you think like a production engineer, not just an algorithm solver.
Ask about input size, word length limits, case sensitivity, and whether duplicates or empty strings are possible. This guides your choice of algorithm and demonstrates thoroughness.
Describe the brute-force method: for each word, check if any other word is a substring. Analyze its time complexity (O(n^2 * L^2) with naive substring search) and space complexity (O(1) extra).
Suggest using a trie built from all words, then for each word, traverse the trie to find if any other word is a prefix (substring). Alternatively, sort words by length and use a hash set for O(1) substring checks. Analyze time and space complexity of the chosen approach.
Write clean code for the optimized approach, handling edge cases and ensuring correctness. Explain key parts of the code as you write.
Compare the naive and optimized approaches, mentioning when the naive might be preferable (e.g., very small input). Walk through a test case to verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.