LRU Cache
An LRU cache evicts the least recently used item when capacity is full. The classic O(1) design combines a hash map with a doubly linked list.
先从这个心智模型开始
An LRU cache evicts the least recently used item when capacity is full. The classic O(1) design combines a hash map with a doubly linked list.
它如何工作，不堆砌不必要的术语
The map gives O(1) key lookup.
The doubly linked list stores recency order so an accessed item can move to the front and the oldest item can be removed from the tail.
把算法读成一连串决策
Identify the invariant that must always remain true.
Follow the read/update path through the structure.
After a mutation, restore the invariant before returning.
Read a tiny input by hand and write down the state after every important operation. For LRU Cache, the goal is to make the invariant visible rather than to memorize code.
get/put O(1)
Do not quote this complexity mechanically. Ask which operation or input dimension it describes, then derive the cost from the loops, recursion depth, data-structure operations and any preprocessing assumptions.
适用场景
- Choose it when its dominant operations match your workload.
- Compare memory overhead as well as Big-O.
通常哪里会出错
- Memorizing complexity without understanding representation.
- Ignoring worst-case shape or resizing/rebalancing costs.
专业回答结构
The key insight is why two structures are needed: map for lookup, doubly linked list for O(1) recency updates and eviction.
What problem does LRU Cache solve?
An LRU cache evicts the least recently used item when capacity is full. The classic O(1) design combines a hash map with a doubly linked list.
关于复杂度应该提到什么？
Its typical cost here is get/put O(1), but a complete answer should connect that cost to the representation, input constraints and the dominant operations.
最常见的错误是什么？
Memorizing complexity without understanding representation.