My first instinct was to just iterate and clone, which obviously breaks the moment you hit a random pointer pointing to a node you haven't created yet.
Start by clarifying the problem and edge cases, then propose an O(n) time and O(n) space solution using a hash map to map original nodes to their copies. Alternatively, present the O(1) space interleaving approach if the interviewer wants to optimize space. 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, while the interleaving approach achieves O(1) space by temporarily modifying the list. This shows you understand trade-offs and can optimize if needed.
Ask if the list can be empty, if random pointers can be null, and if we need to preserve the original list. Confirm that deep copy means new nodes with same values and pointer relationships.
Decide between hash map (O(n) space) and interleaving (O(1) space). Start with the hash map for simplicity, then mention the interleaving as an optimization.
First pass: create a copy of each node and store mapping from original to copy. Second pass: set next and random pointers of copies using the map.
First pass: insert copy nodes right after each original node. Second pass: set random pointers of copies. Third pass: separate the two lists by restoring original next pointers and linking copies.
State time complexity O(n) and space complexity O(n) for hash map, O(1) for interleaving. 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 constraints (e.g., input size, whether k is always valid, tie-breaking rules) and then propose a solution using a hash map to count frequencies followed by a min-heap of size k to extract the top k elements, achieving O(n log k) time. If the interviewer pushes for faster than O(n log n), mention that O(n log k) is better when k is small, and discuss potential O(n) approaches like bucket sort or quickselect when applicable.
Pro tip: Explicitly compare the heap-based O(n log k) approach with bucket sort O(n) and quickselect O(n) average, and explain when each is preferable based on constraints like k's size and whether the input is bounded. This shows you understand trade-offs beyond just the optimal big-O.
Ask about input size, range of values, whether k is guaranteed valid, and how to handle ties. This ensures you don't miss edge cases and shows attention to detail.
Iterate through the array and build a frequency map in O(n) time. This is the foundation for any efficient solution.
Maintain a min-heap of size k while iterating through the frequency map. For each element, push if heap size < k, else replace the smallest if the current frequency is larger. This yields O(n log k) time.
If the interviewer wants strictly faster than O(n log n), mention bucket sort (O(n) when frequencies are bounded by n) or quickselect (O(n) average) and explain their trade-offs.
State time and space complexity clearly, and walk through edge cases like k=1, k=n, empty array, or all elements unique.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.