What is the optimal time complexity of solving LRU Cache?

The optimized solution achieves a time complexity of O(n) by using sorting, mathematical shortcuts, or hash table lookups.

What is the auxiliary space complexity for LRU Cache?

The space complexity is O(1) since we only use a minimal/constant amount of extra memory for tracking variables (or auxiliary space if dynamic structures are allocated).

What are the typical edge cases we must handle in LRU Cache?

Key edge cases include empty collections, negative or large bounds causing integer overflow, arrays with only one element, or inputs where target criteria are not met.

How can we optimize the brute force approach for LRU Cache?

Brute force methods often inspect all possible combinations. We optimize this by sorting the input list to enable binary search, or tracking processed items in a set to avoid redundant nested loop lookups.

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LRU Cache

Learn how to solve the 'LRU Cache' problem. This detailed resource details brute force and optimized approaches.

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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?

Analyze the input constraints. Try sorting first (O(n log n)) or using a hash map/set to track seen elements in O(n) time.

Edge Cases to Watch

Empty list or null input variables
Single item lists/arrays
Extremely large input bounds causing integer or stack overflow

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