BFS was the obvious call and I got there fast, but then they started asking about complexity and I got a little loose with my explanation.
Start by clarifying the problem constraints (grid size, movement rules, whether diagonal moves are allowed). Then propose BFS as the optimal algorithm for unweighted shortest path, explain its step-by-step execution, and analyze time and space complexity. Finally, discuss edge cases and potential optimizations.
Pro tip: Mention that BFS guarantees the shortest path in unweighted graphs, and that you can optimize space by using a queue of coordinates and marking visited cells in-place. Also, proactively discuss how you'd handle very large grids (e.g., using bidirectional BFS).
Ask about grid dimensions, movement directions (4-directional), what constitutes a valid cell (open vs wall), and whether the start or target can be blocked. Confirm return type (-1 if no path).
Explain that BFS is ideal for unweighted shortest path. Justify why DFS or Dijkstra would be less efficient or overkill.
Describe initializing a queue with the start cell, tracking visited cells, and exploring neighbors level by level until the target is found or the queue is empty. Mention using a distance array or storing distance in the queue.
State time complexity O(R*C) and space complexity O(R*C) for the queue and visited set, where R and C are grid dimensions.
Discuss cases like start equals target, start or target blocked, no path exists, and very large grids (mention bidirectional BFS or A* as alternatives).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.