Start by clarifying constraints and expected input sizes to determine the required time complexity. Then, design an optimized algorithm using appropriate data structures, and analyze its time and space complexity to ensure it meets performance requirements.
Pro tip: Always state the brute-force complexity first, then explain how your optimized solution improves it—this shows you understand trade-offs and can communicate technical decisions clearly.
Ask about input size limits, time limits, and edge cases to understand the performance target. This determines whether an O(n log n) or O(n) solution is needed.
Briefly describe the naive solution and its time complexity to establish a baseline. Acknowledge why it fails for large inputs.
Propose an efficient approach using suitable data structures (e.g., hash maps, heaps, two pointers) and explain how it reduces complexity.
State the time and space complexity of your optimized solution and discuss any trade-offs (e.g., memory vs. speed).
Walk through a few test cases, including edge cases, to verify correctness and performance. Mention how you would handle potential pitfalls.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.