The interviewer kept pushing me further every time I thought we were done.
Start by restating the problem, clarifying constraints, and proposing a brute-force solution with its complexity. Then iteratively optimize by identifying bottlenecks and applying appropriate data structures or algorithmic techniques, discussing trade-offs at each step. Finally, verify correctness and analyze final complexities.
Pro tip: Always communicate your thought process and explicitly state the time and space complexity after each optimization. This demonstrates structured thinking and helps the interviewer follow your reasoning.
Restate the problem in your own words, ask clarifying questions about input size, edge cases, and constraints. Confirm the expected output format.
Propose a straightforward, correct solution even if inefficient. Analyze its time and space complexity to establish a baseline.
Pinpoint the costly operations in the brute force approach. Suggest improvements using better data structures (e.g., hash maps, heaps) or algorithmic paradigms (e.g., two pointers, dynamic programming) to reduce time complexity.
After achieving a good time complexity, look for opportunities to reduce memory usage without sacrificing time efficiency. Consider in-place algorithms, iterative approaches, or more compact data structures.
Test the optimized solution with edge cases, walk through examples, and discuss the trade-offs between time and space. Be prepared to justify your choices.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Misread part of the problem early and thought I had it solved in under ten minutes.
Start by clarifying the problem and identifying the optimal substructure and overlapping subproblems to formulate a DP solution. Implement a straightforward DP, then analyze its time and space complexity to guide optimizations such as state reduction, rolling arrays, or advanced data structures.
Pro tip: Always discuss the trade-offs of each optimization (e.g., time vs. space) and validate with edge cases; Google values engineers who can reason about complexity and make informed decisions.
Restate the problem in your own words, ask clarifying questions about input constraints, expected output, and edge cases. Confirm the problem is suitable for DP by identifying optimal substructure and overlapping subproblems.
Clearly define the DP state (e.g., dp[i] represents the optimal solution for prefix i) and write the recurrence relation. Explain how the state transitions and base cases work.
Write a clean implementation of the DP solution, then analyze its time and space complexity. Identify bottlenecks and potential areas for optimization.
Propose and implement optimizations: reduce state dimensions, use rolling arrays, apply monotonic queues or prefix sums, or switch to a greedy approach if applicable. Discuss the trade-offs of each optimization.
Walk through the optimized solution with small examples and edge cases to ensure correctness. Compare with the naive DP to verify results and discuss any remaining limitations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.