This one tripped me up more than it should have.
Use a greedy approach with a max-heap to always place the most frequent remaining character that is different from the last placed character. If at any point the most frequent character is the only option and matches the last placed, return an empty string. This ensures feasibility and efficiency.
Pro tip: Mention the edge case where the maximum frequency exceeds (n+1)/2, which makes rearrangement impossible. Also, discuss the time complexity O(n log k) where k is the number of distinct characters, and note that using a heap is optimal for this problem.
Count the frequency of each character. If the maximum frequency is greater than (n+1)/2, return an empty string immediately.
Use a max-heap (priority queue) to store characters by their frequencies. Also, keep track of the last placed character and its remaining count.
While the heap is not empty, pop the character with the highest frequency. If it is the same as the last placed character, pop the next highest. If no other character is available, return an empty string.
Append the chosen character to the result, decrement its frequency, and if it still has remaining count, push it back into the heap after placing the next character (to avoid immediate reuse).
Once the heap is empty, return the constructed string. If at any point we cannot place a character, return an empty string.
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., k and total nodes) and discussing naive approaches like merging lists one by one. Then present an optimal solution using a min-heap or divide-and-conquer, explaining time and space complexity. Finally, walk through a small example to demonstrate correctness.
Pro tip: Amazon values scalability and efficiency, so emphasize the O(N log k) time complexity of the heap approach and discuss how it handles large inputs. Also, mention edge cases like empty lists and duplicate values to show thoroughness.
Ask about the number of lists (k), total number of nodes (N), and whether the lists are sorted in ascending order. Confirm if you can modify the input lists or need to create a new one.
Mention the simple approach of merging lists sequentially, which takes O(kN) time, and explain why it's inefficient for large k.
Present the min-heap approach: insert the head of each list into a min-heap, then repeatedly extract the minimum and add the next node from that list. This yields O(N log k) time and O(k) space.
State the time and space complexity clearly. Discuss edge cases: empty input, lists of different lengths, and duplicate values.
Trace the algorithm on a small example (e.g., k=3 lists) to demonstrate how the heap maintains the sorted order and how the result is built.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The random pointer is what makes this annoying.
Start by clarifying the problem and edge cases, then propose a solution using a hash map to map original nodes to their copies, allowing O(n) time and space. Alternatively, describe the O(1) space interleaving approach if the interviewer wants optimization. Walk through the steps with a small example to demonstrate correctness.
Pro tip: Mention that the hash map approach is straightforward but uses O(n) extra space, and then offer the interleaving method as an optimization to show depth. This demonstrates you can balance clarity and efficiency, which Amazon values.
Ask if the list can be empty, if random pointers can be null, and if modifying the original list is allowed. Confirm that deep copy means new nodes with same values and pointer structure.
Decide between hash map (O(n) space) and interleaving (O(1) space). Explain the trade-offs and pick one based on constraints or interviewer preference.
For hash map: traverse original, create copy nodes, store mapping; then set next and random pointers using the map. For interleaving: insert copy nodes after originals, set random pointers, then separate the lists.
Use a small list (e.g., 3 nodes with random pointers) to trace the steps, showing how pointers are updated and ensuring no cycles or lost references.
State time and space complexity. Discuss potential bugs (e.g., null handling) and how to test with edge cases like empty list, single node, random pointing to self or null.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.