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LRU Cache

Detailed guide and Python implementation for the 'LRU Cache' problem.

Problem Statement

Easy

Design a data structure that follows the constraints of a Least Recently Used (LRU) cache.

Implement the LRUCache class:

- LRUCache(capacity: int) - Initialize the LRU cache with positive size capacity.

- get(key: int) -> int - Return the value of the key if the key exists, otherwise return -1.

- put(key: int, value: int) -> None - Update the value of the key if the key exists. Otherwise, add the key-value pair to the cache. If the number of keys exceeds the capacity, evict the least recently used key.

The get and put functions must each run in O(1) average time complexity.

Input is a list of operations and a list of arguments. Implement a function lruCache(operations: list, arguments: list) -> list that returns a list of results (None for constructor and put).

Constraints
  • 1 <= capacity <= 3000
  • 0 <= key <= 10000
  • 0 <= value <= 100000
  • At most 200000 calls will be made to get and put

Examples

Example 1
Input
["LRUCache","put","put","get","put","get","put","get","get","get"], [[2],[1,1],[2,2],[1],[3,3],[2],[4,4],[1],[3],[4]]
Output
[None,None,None,1,None,-1,None,-1,3,4]
Explanation

Cache capacity is 2. put(1,1), put(2,2), get(1) returns 1. put(3,3) evicts key 2. get(2) returns -1 (evicted). put(4,4) evicts key 1. get(1) returns -1, get(3) returns 3, get(4) returns 4.

Need a Hint?
Consider using Linked List-specific data structures like sets or heaps.
Edge Cases to Watch
  • Empty input structures
  • Single element inputs
  • Large numerical bounds

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