BFS is the move here, pretty standard once you recognize it.
Clarify the problem constraints (grid size, obstacles, movement rules) and then model the grid as a graph where each cell is a node connected to its valid neighbors. Use BFS to find the shortest path in an unweighted grid, or Dijkstra/A* if edge weights vary, and reconstruct the path using parent pointers.
Pro tip: Always discuss trade-offs between BFS, Dijkstra, and A* based on grid size and whether weights are uniform; mentioning A* with a Manhattan heuristic shows depth and can impress interviewers at Meta.
Ask about grid dimensions, obstacles, movement directions (4-way vs 8-way), and whether edge weights are uniform. Confirm if the path must be returned or just its length.
For unweighted grids, BFS guarantees the shortest path; for weighted grids, use Dijkstra or A* with an admissible heuristic. Explain why the chosen algorithm fits the constraints.
Use a queue (BFS) or priority queue (Dijkstra) to explore neighbors, track visited cells, and store parent pointers to reconstruct the path once the target is reached.
State time and space complexity (O(V+E) for BFS, O(E log V) for Dijkstra) and discuss optimizations like bidirectional BFS or A* for large grids.
Walk through examples: start equals target, no path exists, obstacles blocking, and large grids. Verify correctness and discuss potential pitfalls.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.