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My first instinct was to brute force every possible prefix and suffix reversal and just take the min.
First clarify that the operation allows at most one reversal of either a prefix or a suffix, not both. Then systematically compare the original string with all possible prefix-reversed and suffix-reversed strings to find the lexicographically smallest. Optimize by noting that the best reversal will start at the first character that can be improved, and use string comparison to evaluate candidates efficiently.
Pro tip: Mention that you would test edge cases like strings already sorted, all identical characters, and very short strings to ensure correctness. Also, discuss time complexity: a naive approach is O(n^2) due to generating and comparing O(n) candidates each of length O(n), but you can optimize to O(n) by only considering reversals that affect the first differing character.
Confirm that at most one operation is allowed, and it can be either a prefix reversal or a suffix reversal, not both. Ask about input size and character set to determine if optimization is needed.
Recognize that only reversals that change the string's prefix matter for lexicographic order. Specifically, consider reversing a prefix that ends at some index i, or a suffix that starts at some index j.
Generate all possible strings by reversing every prefix (length 1 to n) and every suffix (length 1 to n), plus the original string. Compare them lexicographically to find the minimum.
Observe that the optimal reversal will make the first character as small as possible. For prefix reversals, the new first character is the character at the end of the reversed prefix. For suffix reversals, the first character remains unchanged, so only consider suffix reversals if no prefix reversal improves the first character.
State that the optimized approach runs in O(n) time by scanning for the smallest character that can be brought to the front, then comparing the resulting candidates. Test with edge cases like 'cba', 'aaa', 'ab', and 'zyx'.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Model the race as a simulation where you maintain a list of active drivers and their cumulative times. For each lap, add the lap time to each active driver's cumulative time, then identify and remove the driver with the maximum cumulative time (breaking ties alphabetically). Record the eliminated driver in order until one driver remains.
Pro tip: Clarify with the interviewer whether the last remaining driver should be included in the elimination order (often they are not, as they are the winner). Also, discuss time complexity: naive simulation is O(n^2) for n drivers, but using a heap can reduce it to O(n log n).
Confirm the number of laps, whether lap times are given per driver per lap, and whether the last remaining driver is included in the elimination order. Also, clarify tie-breaking: alphabetical order of driver names.
Use a list or dictionary to store each driver's cumulative time. For efficiency, consider a max-heap (or min-heap with negated times) to quickly find the driver with the highest cumulative time, but handle tie-breaking carefully.
For each lap, update cumulative times for all active drivers by adding their lap time. Then, find the driver with the maximum cumulative time (and alphabetically first in case of tie) and eliminate them, recording the elimination order.
When multiple drivers have the same maximum cumulative time, eliminate the one whose name comes first alphabetically. Ensure that after each elimination, the driver is removed from further consideration.
After all laps (or until one driver remains), return the list of eliminated drivers in the order they were removed. If the last driver is to be included, append them at the end.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.