My first instinct was just sort and scan, which is correct but I fumbled explaining the comparison logic for zero-padded segments out loud.
Start by clarifying the problem: support is not monotonic, so you cannot use binary search; you must check every version. Then outline a robust pipeline: parse each version into a tuple of integers (handling zero-padding and invalid inputs), deduplicate, sort by version order, and iterate from smallest to largest, calling isSupported until you find the first supported version. Finally, analyze time and space complexity, noting that sorting dominates at O(n log n) time and O(n) space.
Pro tip: Mention that you would cache isSupported results to avoid redundant API calls, and discuss how to handle invalid inputs gracefully (e.g., skip or raise) based on production requirements—this shows you think about real-world robustness and efficiency.
Confirm the version format, what constitutes an invalid input, whether duplicates should be treated as one, and that support is not monotonic (so no binary search).
Split each string by '.', convert each segment to an integer (handling zero-padding), and validate that there are exactly three numeric segments; decide how to handle invalid inputs (skip, raise, or log).
Remove duplicates using a set, then sort the unique versions in ascending order using tuple comparison (major, minor, patch).
Iterate through the sorted list and call isSupported on each version; return the first version that returns true, or indicate none found.
State that time complexity is O(n log n) due to sorting (plus O(n) API calls), and space complexity is O(n) for storing parsed versions; discuss potential optimizations like caching API results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use a hierarchical probing strategy: first find a supporting version in the next major group, then binary search within that group to find the earliest minor group, then within that minor group to find the earliest patch group, and finally within that patch group to find the exact earliest supporting version. At each level, exploit the guarantee that support reappears in the next group to bound the search space and achieve O(log N) calls. Address edge cases like no support at all by checking the last version and handling sparse support with careful boundary checks.
Pro tip: Emphasize that the rate limit forces you to think in terms of groups and logarithmic calls; explicitly state the call count as O(log N) and contrast it with the naive O(N) approach. Also, mention that you would validate the algorithm with a few concrete examples, including the no-support case, to demonstrate thoroughness.
Restate the problem: find the earliest version where isSupported returns true, given rate-limited calls and non-monotonic but group-wise reappearing support. Confirm the group sizes (patch, minor, major) and that versions are ordered.
Propose a hierarchical search: first identify a major group that contains a supporting version, then narrow down to the earliest minor group, then earliest patch group, and finally the exact version. Explain why this is sub-linear.
Describe each level: use exponential search or binary search to find the first major group with support, then binary search within that group for the first minor group with support, etc. Specify how to handle boundaries and the guarantee that support reappears.
Argue correctness by induction on the group hierarchy: if a supporting version exists, the algorithm will find the earliest one because it always narrows to the earliest group containing support. Analyze call count: O(log N) calls, with constants depending on group sizes.
Discuss no supporting version (check last version; if false, return none), very sparse support (still works due to reappearance guarantee), and trade-offs between different search methods (e.g., exponential vs binary search).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.