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Recognize that the revenue from selling a unit of product i depends only on its current stock, and since each sale reduces the stock by one, the optimal strategy is to always sell the product with the highest current stock. This greedy approach can be implemented efficiently using a max-heap, repeatedly extracting the maximum, adding it to revenue, decrementing it, and reinserting if still positive. Alternatively, sort the array and compute the sum of the largest m elements after accounting for the decrements, which can be derived by sorting and using a formula.
Pro tip: Mention that the greedy choice is optimal because the marginal revenue of selling a unit is exactly the current stock, and selling a higher-stock product never reduces future options compared to selling a lower-stock product. Also, note that if m is large, a heap-based simulation is O((n+m) log n), but a sorting-based approach can be O(n log n + m) by processing sorted stocks in descending order and using arithmetic series for bulk sales.
Clarify that each sale of product i yields revenue equal to its current stock, then stock decreases by 1. We need to choose m sales (with repetition allowed) to maximize total revenue.
Argue that to maximize revenue, we should always sell the product with the highest current stock, because the marginal gain is highest and selling it reduces its stock, which may allow other products to be sold later.
Use an exchange argument: if an optimal solution sells a lower-stock product while a higher-stock product is available, swapping the sale to the higher-stock product increases or maintains revenue without reducing future potential.
Use a max-heap to simulate the process: extract max, add to revenue, decrement, and reinsert if >0. Repeat m times. Alternatively, sort the array descending and compute the sum of the largest m elements after accounting for decrements via a formula.
Heap approach: O((n+m) log n) time, O(n) space. Sorting approach: O(n log n + m) time if using arithmetic series. Handle cases where m exceeds total stock (sell all until zero) and when stocks are zero.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The parsing tripped me up more than I expected.
Clarify the requirements and edge cases, then propose an efficient solution using a hash map to count frequencies after normalizing case and extracting words via regex or manual parsing. Sort the entries by frequency descending and word ascending, then return the top 3. Discuss time and space complexity.
Pro tip: Mention that you would use a regex like \[A-Za-z\]+ to extract words, but also discuss a manual parsing approach to show you understand the underlying mechanics. This demonstrates both practical knowledge and depth.
Ask about input size, character set, handling of empty strings, and whether words are case-insensitive. Confirm that non-letter characters are separators and that only distinct words are considered.
Use a hash map to count frequencies. Extract words by iterating through the string and building words from consecutive letters, or use a regex. Normalize case by converting to lowercase.
Populate the frequency map, then convert to a list of entries. Sort by frequency descending, and for ties, sort alphabetically ascending.
Take the first up to 3 entries. State time complexity O(n + m log m) where n is string length and m is number of distinct words, and space complexity O(m).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.