Stack problem but the off-by-one stuff in the time calculation tripped me up.
Model the logs as a call tree or use a stack to track active functions, then compute exclusive time by subtracting time spent in child calls from each function's total elapsed time. Alternatively, use a timestamp sweep with a stack to accumulate exclusive time in a single pass.
Pro tip: Clarify assumptions upfront: whether logs are well-formed (proper nesting, no overlapping calls) and if timestamps are inclusive/exclusive. This shows attention to detail and avoids edge-case bugs.
Ask about log structure (e.g., function ID, start/end times), nesting rules, and whether timestamps are integers or floats. Confirm if logs are sorted and if calls can overlap or be malformed.
Decide between building a call tree or using a stack for a single-pass solution. A stack is often simpler and more efficient for streaming logs.
For each function, calculate total elapsed time (end - start) and subtract the exclusive times of all direct child calls. With a stack, maintain a running total and adjust when entering/leaving functions.
Consider recursive calls, multiple calls to the same function, zero-duration calls, and timestamps that are inclusive on both ends. Ensure the algorithm correctly aggregates exclusive time per function.
State time and space complexity (typically O(n) time, O(d) space where d is max depth). Walk through a small example to verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the problem and edge cases, then propose a sweep-line algorithm: separate start and end times, sort both, and use two pointers to count concurrent meetings. Alternatively, use a min-heap to track end times while iterating through meetings sorted by start time. Analyze time and space complexity, and discuss trade-offs.
Pro tip: Mention that the sweep-line approach is optimal for large inputs and can be extended to find the exact time of peak usage. Also, proactively discuss how to handle edge cases like empty input or back-to-back meetings.
Ask if intervals are inclusive/exclusive, if input is sorted, and what to return for no meetings. Confirm that overlapping meetings require separate rooms.
Briefly mention that a naive O(n^2) approach checking overlaps for each meeting is possible but inefficient, setting the stage for optimization.
Describe the sweep-line method: extract start and end times, sort them, and use two pointers to count active meetings, updating max rooms. Alternatively, explain the min-heap approach.
State that both approaches run in O(n log n) time due to sorting, with O(n) space. Compare with brute-force and mention when each might be preferable.
Walk through a sample input like [[0,30],[5,10],[15,20]] to show the algorithm yields 2 rooms. Discuss edge cases like empty input or all meetings overlapping.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use a BFS traversal while tracking each node's horizontal distance (column index) from the root, storing nodes in a map keyed by column. Then output the columns in sorted order from leftmost to rightmost, preserving top-to-bottom order within each column.
Pro tip: Clarify whether nodes in the same column and row should be ordered by their left-to-right position; if so, augment BFS with a row index and sort within each column by row, then by value or insertion order.
Ask about ordering within a column (top-to-bottom, and tie-breaking for same row), and confirm handling of empty tree or single node.
Use BFS with a queue storing (node, column, row) and a hash map from column to list of nodes; alternatively DFS with column tracking.
Process nodes level by level, updating column indices (left child: col-1, right child: col+1) and appending nodes to the corresponding column list.
Extract column keys, sort them ascending, and for each column, ensure nodes are ordered top-to-bottom (and left-to-right if same row) before adding to result.
State time and space complexity (O(n log n) due to sorting, or O(n) with ordered map), and walk through a small example to verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem constraints (e.g., array size, value range, duplicates) and then present multiple solutions with trade-offs. Begin with a simple sorting approach, then optimize using a min-heap of size k or Quickselect for average O(n) time. Discuss the choice based on expected input and system constraints.
Pro tip: Mention that Quickselect has O(n) average but O(n^2) worst-case, and you can avoid the worst-case by using a randomized pivot or the Median of Medians algorithm. Also, note that for streaming data or when k is small, a heap is more practical.
Ask about input size, value range, whether duplicates count as separate elements, and if the array can be modified. This shows you consider edge cases and practical limits.
Suggest sorting the array and returning the element at index n-k. This is simple and O(n log n) time, O(1) extra space if in-place, but may be inefficient for large n.
Explain using a min-heap of size k: iterate through the array, push elements, and if heap size exceeds k, pop the smallest. At the end, the heap root is the kth largest. This is O(n log k) time and O(k) space, good for large n and small k.
Describe the Quickselect algorithm: partition the array around a pivot, then recursively search the side that contains the kth largest. Average O(n) time, worst-case O(n^2), but can be mitigated with random pivots.
Discuss when to use each approach: sorting for simplicity, heap for streaming or small k, Quickselect for optimal average time when the array can be modified. Mention that Quickselect modifies the array, which may not be allowed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.