I knew the doubly linked list plus hash map combo going in, but fumbled a bit explaining why sentinel dummy nodes matter.
Start by clarifying requirements (capacity, eviction policy, thread-safety) and then propose a hash map combined with a doubly linked list to achieve O(1) get and put. Walk through the design, implement the core operations, and discuss trade-offs and edge cases.
Pro tip: Mention that you would use a doubly linked list with sentinel nodes to simplify edge cases, and discuss how you would make it thread-safe if needed (e.g., using a lock or ConcurrentHashMap with synchronized blocks).
Ask about capacity, eviction policy (LRU), thread-safety, and expected operations. Confirm that get and put must be O(1).
Explain that a hash map provides O(1) access to nodes, and a doubly linked list maintains recency order. Together they enable O(1) get and put.
Describe how get moves the accessed node to the front (most recent), and put adds/updates the node at the front, evicting the least recent (tail) if capacity is exceeded.
Write clean code with sentinel head/tail nodes to avoid null checks. Handle edge cases: capacity 0, updating existing key, and eviction.
Talk about time/space complexity, thread-safety options, and possible variations (e.g., LFU, TTL).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Backtracking felt obvious to me but I got a little tangled up explaining the pruning.
Clarify the problem constraints (e.g., positive numbers, duplicates, target range) and then propose a backtracking solution that builds combinations incrementally, ensuring uniqueness by enforcing non-decreasing order. Discuss time/space complexity and potential optimizations like sorting and pruning.
Pro tip: Mention that sorting the candidates and using a start index to avoid duplicates is crucial, and that pruning when the remaining sum is less than the current candidate can significantly improve performance.
Ask about input size, whether numbers are positive, if duplicates exist, and if the output order matters. This shows attention to detail and helps tailor the solution.
Explain that you'll use recursion to explore combinations, starting from a given index, and allow reuse of the same element by not incrementing the index when recursing.
Sort the input to skip duplicates and enforce non-decreasing order. Prune branches when the current sum exceeds the target or when adding the smallest remaining candidate still exceeds the target.
Discuss time complexity (exponential in worst case) and space complexity (recursion depth). Mention that sorting adds O(n log n) but enables pruning and deduplication.
Walk through a small example (e.g., candidates [2,3,6,7], target 7) and consider edge cases like empty input, target 0, or no solution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.