I knew this one cold so maybe that hurt me.
Start by clarifying requirements (fixed capacity, O(1) get/put, LRU eviction) and then propose a hash map combined with a doubly linked list. Explain how the hash map provides O(1) access to nodes, while the linked list maintains recency order, enabling O(1) updates and evictions. Conclude with complexity analysis and edge cases.
Pro tip: Mention that you can use a sentinel head and tail to simplify edge cases in the linked list, and discuss thread-safety considerations if the cache might be accessed concurrently.
Confirm the cache capacity, operations (get/put), eviction policy (LRU), and performance requirements (O(1) average). Ask about concurrency needs and whether keys/values are generic.
Select a hash map for O(1) key lookup and a doubly linked list to track recency order. Explain that the hash map stores key -> node references, and the list nodes store key-value pairs.
Describe get: if key exists, move its node to the front (most recently used) and return value; else return -1. Describe put: if key exists, update value and move to front; else insert new node at front and add to map; if capacity exceeded, remove tail node (least recently used) and delete from map.
State that both get and put are O(1) average time due to hash map and constant-time list operations. Space is O(capacity). Discuss edge cases: capacity 0 or 1, updating existing key, evicting when full, and handling null values.
Mention possible variations: thread-safe implementation using locks or concurrent data structures, alternative eviction policies (LFU, FIFO), and trade-offs between memory overhead and performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem and edge cases, then present a sorting-based solution as a baseline. Next, discuss Quickselect and max-heap alternatives, comparing their time and space complexities and when each is preferable. Conclude with a recommendation based on constraints like k, n, and whether the input is static or streaming.
Pro tip: Mention that you can avoid computing square roots by comparing squared distances, which saves time and avoids floating-point precision issues. Also, note that Quickselect has O(n) average but O(n^2) worst-case, so randomization or a heap might be safer for adversarial inputs.
Confirm the definition of distance (Euclidean), whether k is always valid, and if the output order matters. Discuss handling of duplicate points and large inputs.
Compute squared distances for all points, sort them, and return the first k. This is simple but O(n log n) time and O(n) space.
Explain that Quickselect can find the k-th smallest distance in average O(n) time, then partition to get the k closest. Mention worst-case O(n^2) and how randomization mitigates it.
Describe maintaining a max-heap of size k: iterate through points, push if heap size < k, else replace the max if the current point is closer. This gives O(n log k) time and O(k) space, ideal for streaming data.
Summarize trade-offs: sorting is simple but slower for large n; Quickselect is faster on average but has worst-case risk; max-heap is best when k is small or data is streaming. Choose based on constraints.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.