The overlap between blocks is what gets you.
For each row, represent the reserved seats as a bitmask of 10 bits and precompute which of the three 4-seat blocks (2-5, 4-7, 6-9) are free. Then, for each row, determine the maximum number of non-overlapping free blocks that can be assigned, considering that blocks 2-5 and 6-9 are disjoint while 4-7 overlaps both. Sum these maxima across all rows.
Pro tip: Clarify with the interviewer whether groups can be split across rows (they cannot) and whether a row can seat multiple groups simultaneously (yes, if non-overlapping). This shows attention to detail and prevents misinterpretation.
Confirm that each group must sit entirely within one row and that multiple groups can be seated in the same row if their blocks do not overlap. Also confirm that the three allowed blocks are exactly seats 2-5, 4-7, and 6-9.
For each row, create a bitmask of length 10 where 1 indicates a reserved seat and 0 indicates a free seat. Then check the three blocks: block A (2-5), block B (4-7), block C (6-9) to see which are completely free.
For each row, compute the maximum number of non-overlapping free blocks. Since A and C are disjoint, if both are free you can seat 2 groups; otherwise, if any one block is free you can seat 1 group; if none, 0. Note that B overlaps both A and C, so it cannot be combined with either.
Sum the maximum groups per row to get the total maximum number of 4-person groups that can be seated together.
The solution runs in O(n) time and O(1) extra space per row. Discuss edge cases such as rows with no free blocks, rows where only the middle block is free, and rows where all blocks are free.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The 'prefer longer phrase at same position' rule is the annoying part.
First, clarify requirements and edge cases (e.g., punctuation handling, case sensitivity, overlapping aliases). Then, design an algorithm that normalizes the headline and aliases, builds a trie or sorts aliases by length, and scans the headline for matches, ensuring whole-word matching and longest-phrase preference. Finally, implement and test with examples, discussing time/space complexity and potential optimizations.
Pro tip: Mention that you would preprocess the dictionary by grouping aliases by their first word and sorting by length descending, which allows efficient longest-match-first scanning and avoids redundant checks. Also, discuss how to handle punctuation by replacing it with spaces or using regex word boundaries, and consider Unicode normalization for robustness.
Ask about punctuation handling (e.g., hyphens, apostrophes), case sensitivity, overlapping aliases, and whether aliases can contain multiple words. Confirm that whole-word matching means aliases must not be substrings of larger words.
Convert headline and aliases to lowercase, and replace punctuation with spaces (or use regex word boundaries) to ensure consistent tokenization. Consider Unicode normalization if needed.
Build a data structure for efficient matching: either a trie of aliases or a map from first word to list of aliases sorted by length descending. This supports longest-match-first and whole-word matching.
Iterate through the headline word by word. At each position, attempt to match the longest alias starting there. If a match is found, record the ticker (if not already recorded) and advance past the matched phrase to avoid overlapping matches.
Return tickers in order of first appearance. Discuss time complexity (e.g., O(N * L) where N is headline length and L is max alias length) and space complexity, and suggest optimizations like early termination or caching.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.