Dynamic Programming on graph
Last updated: July 23, 2025
Quick Overview
Apply the Sliding Window algorithm on a matrix to solve this optimization problem.
Lyft
July 23, 2025119
8
1,298 solved
Apply the Sliding Window algorithm on a matrix to solve this optimization problem.
This coding problem is frequently asked during Technical Screen at Lyft. The interviewer is testing your ability to translate a problem into clean, working code while discussing time and space complexity. Lyft expects candidates to write production-quality code, not just solve the puzzle.
What the Interviewer Expects
- Quickly identify the optimal approach and its theoretical basis
- Handle complex algorithm design with multiple interacting components
- Write concise, elegant code under time pressure
- Prove correctness of your approach and discuss alternative solutions
- Optimize beyond the obvious: discuss constant factor improvements
- Address follow-up variations and explain how the solution generalizes
Key Topics to Cover
How to Approach This
- Clarify input constraints and edge cases before writing code.
- Walk through your approach verbally and confirm with the interviewer before coding.
- Start with a brute force solution, then optimize. Mention time and space complexity.
- Test your solution with examples, including edge cases like empty input or duplicates.
- Consider common patterns: sliding window, two pointers, hash map, BFS/DFS, dynamic programming.
Possible Follow-up Questions
- Can you solve this in a single pass?
- What is the worst-case input for your solution?
- How would you parallelize this solution?
- Can you solve this iteratively instead of recursively (or vice versa)?
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