Pretty much a direct strStr implementation.
Start by clarifying the problem constraints (e.g., input sizes, character set, whether the pattern can be empty) and then propose a straightforward brute-force solution. After establishing correctness, discuss more efficient algorithms like KMP or Rabin-Karp, explaining their trade-offs. Finally, walk through a concrete example to demonstrate the algorithm's steps and edge cases.
Pro tip: Mention that in real-world systems like DoorDash, string matching is often used in search and routing, so considering performance and memory for large inputs is crucial. Also, explicitly state the time and space complexity of each approach and justify your final choice.
Ask about input sizes, character set, whether the pattern can be empty, and expected return value if no match is found. This ensures you handle edge cases and choose an appropriate algorithm.
Describe a simple O(n*m) approach that checks each possible starting position in the text. This demonstrates you can start with a correct baseline before optimizing.
Introduce KMP (O(n+m)) or Rabin-Karp (average O(n+m)) and explain how they improve efficiency by avoiding redundant comparisons. Mention when each is preferable.
Trace the chosen algorithm on a small example (e.g., text='abxabcabcaby', pattern='abcaby') to show step-by-step how the index is found and how edge cases are handled.
State the time and space complexity of each approach and justify your final recommendation based on the constraints. Mention any practical considerations like memory usage or implementation complexity.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The DoorDash theming made it feel less dry than a generic grid problem, but the core is still BFS.
First, clarify the problem constraints and edge cases (e.g., grid size, number of targets, obstacles). Then, present a BFS solution that models the state as (position, collected items bitmask) to find the minimum steps to collect all items. Finally, discuss optimizations such as precomputing distances between key points and using dynamic programming to reduce the state space.
Pro tip: Mention that for a small number of targets (≤10), the bitmask BFS is efficient, but for larger numbers, precomputing pairwise distances and using DP (TSP-like) is more scalable. This shows you consider trade-offs and scalability.
Ask about grid size, number of targets, obstacles, and whether all items must be collected. Confirm if movement is 4-directional and if revisiting cells is allowed.
Use BFS where each state is (row, col, mask) where mask represents collected items. Start from initial position with mask=0, and goal is any state with mask = (1<<k)-1.
Use a queue for BFS, a visited set to avoid revisiting states, and track steps. For each move, update mask if the new cell contains a target.
Time complexity: O(R*C*2^k), space: O(R*C*2^k). Discuss how this scales with k and grid size.
For larger k, precompute distances between start and targets (and between targets) using BFS from each key point, then use DP (Held-Karp) to find shortest path visiting all targets. This reduces to O(k^2 * 2^k) after precomputation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.