Model the grid as a graph where each free cell is a node connected to its four orthogonal neighbors, then perform BFS or DFS from the start to see if the target is reachable. BFS is preferred for shortest path, but DFS works for reachability; both have O(R*C) time and space complexity.
Pro tip: Clarify edge cases upfront (start equals target, start or target on obstacle, empty grid) and mention that BFS avoids recursion depth issues on large grids, showing you think about production constraints.
Confirm grid dimensions, movement rules, and what constitutes a valid path. Ask about edge cases like start == target, start/target being obstacles, or empty grid.
Select BFS for shortest path or DFS for simple reachability. Explain that BFS uses a queue and DFS uses a stack/recursion, both visiting each cell at most once.
Use a visited set or modify the grid in-place to avoid revisiting cells. For each cell, check its four neighbors, ensuring they are within bounds and not obstacles.
State time and space complexity: O(R*C) for both. Discuss potential optimizations like early termination when target is found or using bidirectional BFS for large grids.
Walk through a simple grid example to verify correctness, including cases with no path and multiple paths. Mention how you would handle large grids or memory constraints.
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