← Robinhood Interview Insights
Clarify the problem constraints and edge cases, then propose a greedy algorithm that assigns each string to the first row with sufficient remaining capacity. Analyze the time and space complexity, and discuss potential optimizations or alternative strategies.
Pro tip: Mention that the greedy approach is optimal for minimizing the number of rows used, but if the goal is to balance row usage, a different strategy like best-fit might be needed. Also, consider real-world frontend applications like text layout or flexbox wrapping.
Ask about constraints: string lengths, N, maximum width, whether order matters, and if rows must be filled sequentially. Confirm if the goal is to minimize rows or just assign feasibly.
Use an array to track remaining capacity per row. For efficiency, consider a balanced BST or heap to quickly find a row with enough capacity, but note that a simple linear scan may suffice for small N.
Iterate through strings, and for each, find the first row with remaining capacity >= string length. If found, assign and update capacity; else, handle failure (e.g., return error or start new row if allowed).
Discuss time complexity: O(M*N) for linear scan, or O(M log N) with a heap. Space complexity: O(N) for row capacities and O(M) for assignments.
Consider if strings can be split, if rows can be added, and if order must be preserved. Compare greedy vs. optimal packing, and mention real-world frontend use cases like text wrapping.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the problem: at each step, place the next string into the row with the most remaining space. Then, identify that a max-heap (priority queue) keyed by remaining space efficiently retrieves the row with maximum remaining space in O(log n) time per insertion, and update the heap after placing the string. Finally, discuss trade-offs and edge cases, such as ties and dynamic updates.
Pro tip: Mention that a max-heap is ideal because it gives O(log n) access to the row with the most space, but also note that if the number of rows is small, a simple linear scan might be sufficient—showing you consider practical constraints.
Confirm that at each step, we need to find the row with the maximum remaining space and place the string there, then update that row's remaining space.
The core operation is repeatedly finding the maximum remaining space among rows, which suggests using a priority queue (max-heap) for efficiency.
Use a max-heap where each element is a row with its remaining space. Extract the max, place the string, reduce the remaining space, and re-insert the updated row.
Each placement takes O(log n) time for heap operations, where n is the number of rows. This is efficient for large n compared to O(n) linear scan per step.
Consider ties (any row with max space works), rows that become full (remove from heap), and whether the number of rows is small enough that a simple array scan is simpler.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.