This is basically a constrained selection problem.
Clarify the problem constraints (e.g., number of books, categories, points range) and edge cases. Then propose an efficient algorithm: for each category, keep the top 3 books by points, and use a max-heap or sorting to select up to 3 books from distinct categories, maximizing total points. Discuss time and space complexity and potential optimizations.
Pro tip: Mention that you only need the top 3 books per category because selecting more than 3 from one category is impossible, which reduces the problem size and simplifies the selection. Also, consider using a heap to efficiently merge the top candidates.
Ask about input size, whether points can be negative, if categories are strings or integers, and if exactly 3 books are required or up to 3. Confirm that each book must be from a different category.
Group books by category and keep only the top 3 points per category. Then, use a max-heap or sort all candidates and greedily pick the highest points while ensuring distinct categories, up to 3 books.
State time complexity: O(N log N) for sorting or O(N log K) with heap, where K is number of categories. Handle cases with fewer than 3 categories or books, negative points, and ties.
Write clean code with helper functions for grouping and selection. Test with examples: multiple books per category, exactly 3 categories, and edge cases like empty list.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the input types and expected output, then iterate through the dictionary and filter out closed locations. For each open location, include it in the result with its associated list of people, ensuring no mutation of the original data.
Pro tip: Mention that you would use a dictionary comprehension for a clean, Pythonic solution, but also discuss time and space complexity to show awareness of efficiency.
Confirm the data structures: dictionary with location keys and list values, and a set of closed locations. Ask about edge cases like empty inputs or locations not in the dictionary.
Decide to iterate over the dictionary items and include only those whose key is not in the closed set. Consider using a dictionary comprehension for conciseness.
Write code that creates a new dictionary with open locations and their people lists. Ensure the original dictionary is not modified.
State that time complexity is O(n) where n is the number of locations, and space complexity is O(m) where m is the number of open locations.
Walk through a simple example to verify correctness, including cases where all locations are closed or none are closed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use a hash set to track books currently checked out. Iterate through the log entries, updating the set based on the action and returning False if an invalid state is encountered.
Pro tip: Clarify edge cases upfront, such as empty input or duplicate actions, and mention that the solution runs in O(n) time and O(n) space, which is optimal for this problem.
Confirm the input format, expected output, and handle edge cases like empty list or invalid actions.
Use a hash set to efficiently track books currently checked out, allowing O(1) lookups and updates.
For each entry, if action is 'checkout', ensure the book is not already in the set, then add it; if 'return', ensure the book is in the set, then remove it.
If any violation is found during iteration, return False immediately; otherwise, return True after processing all entries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.