Implement LRU and target-sum combinations
Problem Description
This problem consists of two independent parts:
Part 1: LRU Cache Implementation
Design and implement an in-memory cache that adheres to the Least Recently Used (LRU) eviction policy.
The cache must support the following operations with O(1) time complexity:
get(key): Retrieve the value associated with the given key.put(key, value): Insert or update a key-value pair. If the cache reaches its capacity, the least recently used item must be evicted.
When implementing from scratch:
- Describe the common approach using a hash map combined with a doubly linked list.
- Explain how sentinel (dummy) head and tail nodes simplify the handling of edge cases.
- Justify why Java's built-in
LinkedListclass is not suitable for this specific O(1) LRU implementation. - Compare your custom implementation with using Java's
LinkedHashMap, detailing the trade-offs involved. - Analyze the time and space complexity of your solution.
Part 2: Target Sum Combinations
Given a list of candidate transfer amounts (positive integers) and a target amount (a positive integer), return all unique combinations of candidates that sum up to the target. Each candidate amount may be used an unlimited number of times within a combination.
Your solution should:
- Explain the backtracking approach used to find these combinations.
- Describe any pruning rules applied to reduce the search space.
- Detail how duplicate combinations are avoided in the output.
- Analyze the time and space complexity of your approach.
- Justify why backtracking is an appropriate algorithm for this problem.
- Briefly compare this backtracking approach with a dynamic programming approach for solving similar problems.
AI interview prep
Core dilemma
Balancing the need for O(1) cache operations with efficient eviction logic, while also demonstrating mastery of combinatorial search algorithms and their optimization strategies for unique results.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Implement LRU and target-sum combinations
Problem Description
This problem consists of two independent parts:
Part 1: LRU Cache Implementation
Design and implement an in-memory cache that adheres to the Least Recently Used (LRU) eviction policy.
The cache must support the following operations with O(1) time complexity:
get(key): Retrieve the value associated with the given key.put(key, value): Insert or update a key-value pair. If the cache reaches its capacity, the least recently used item must be evicted.
When implementing from scratch:
- Describe the common approach using a hash map combined with a doubly linked list.
- Explain how sentinel (dummy) head and tail nodes simplify the handling of edge cases.
- Justify why Java's built-in
LinkedListclass is not suitable for this specific O(1) LRU implementation. - Compare your custom implementation with using Java's
LinkedHashMap, detailing the trade-offs involved. - Analyze the time and space complexity of your solution.
Part 2: Target Sum Combinations
Given a list of candidate transfer amounts (positive integers) and a target amount (a positive integer), return all unique combinations of candidates that sum up to the target. Each candidate amount may be used an unlimited number of times within a combination.
Your solution should:
- Explain the backtracking approach used to find these combinations.
- Describe any pruning rules applied to reduce the search space.
- Detail how duplicate combinations are avoided in the output.
- Analyze the time and space complexity of your approach.
- Justify why backtracking is an appropriate algorithm for this problem.
- Briefly compare this backtracking approach with a dynamic programming approach for solving similar problems.